2022

  • N. Wang, B. Siegmann, U. Rascher, J. G. P. W. Clevers, O. Muller, H. Bartholomeus, J. Bendig, D. Masiliunas, R. Pude, and L. Kooistra, “Comparison of a UAV- and an airborne-based system to acquire far-red sun-induced chlorophyll fluorescence measurements over structurally different crops,” Agricultural and Forest Meteorology, vol. 323, 2022. doi:https://doi.org/10.1016/j.agrformet.2022.109081
    [BibTeX] [PDF]
    @article{wang_meteorology,
    author = {Wang, Na and Siegmann, Bastian and Rascher, Uwe and Clevers, Jan G.P.W. and Muller, Onno and Bartholomeus, Harm and Bendig, Juliane and Masiliunas, Dainius and Pude, Ralf and Kooistra, Lammert},
    title = {Comparison of a UAV- and an airborne-based system to acquire far-red sun-induced chlorophyll fluorescence measurements over structurally different crops},
    journal = {Agricultural and Forest Meteorology},
    volume = {323},
    year = {2022},
    doi = {https://doi.org/10.1016/j.agrformet.2022.109081},
    url = {https://reader.elsevier.com/reader/sd/pii/S003442572200308X?token=5DD3E675A3E9678D4A5B13C53A7E31D990812E25707BFFA7040C4C1957BE534AD537B416262B0828BB286E556C7F3DA7&originRegion=eu-west-1&originCreation=20221128103651},
    }

  • M. Rossini, M. Celesti, G. Bramati, M. Migliavacca, S. Cogliati, U. Rascher, and R. Colombo, “Evaluation of the Spatial Representativeness of In Situ SIF Observations for the Validation of Medium-Resolution Satellite SIF Products,” Remote Sensing, vol. 14, iss. 20, 2022. doi:10.3390/rs14205107
    [BibTeX] [PDF]

    The upcoming Fluorescence Explorer (FLEX) mission will provide sun-induced fluorescence (SIF) products at unprecedented spatial resolution. Thus, accurate calibration and validation (cal/val) of these products are key to guarantee robust SIF estimates for the assessment and quantification of photosynthetic processes. In this study, we address one specific component of the uncertainty budget related to SIF retrieval: the spatial representativeness of in situ SIF observations compared to medium-resolution SIF products (e.g., 300 m pixel size). Here, we propose an approach to evaluate an optimal sampling strategy to characterise the spatial representativeness of in situ SIF observations based on high-spatial-resolution SIF data. This approach was applied for demonstration purposes to two agricultural areas that have been extensively characterized with a HyPlant airborne imaging spectrometer in recent years. First, we determined the spatial representativeness of an increasing number of sampling points with respect to a reference area (either monocultural crop fields or hypothetical FLEX pixels characterised by different land cover types). Then, we compared different sampling approaches to determine which strategy provided the most representative reference data for a given area. Results show that between 3 and 13.5 sampling points are needed to characterise the average SIF value of both monocultural fields and hypothetical FLEX pixels of the agricultural areas considered in this study. The number of sampling points tends to increase with the standard deviation of SIF of the reference area, as well as with the number of land cover classes in a FLEX pixel, even if the increase is not always statistically significant. This study contributes to guiding cal/val activities for the upcoming FLEX mission, providing useful insights for the selection of the validation site network and particularly for the definition of the best sampling scheme for each site.

    @Article{rs14205107,
    AUTHOR = {Rossini, Micol and Celesti, Marco and Bramati, Gabriele and Migliavacca, Mirco and Cogliati, Sergio and Rascher, Uwe and Colombo, Roberto},
    TITLE = {Evaluation of the Spatial Representativeness of In Situ SIF Observations for the Validation of Medium-Resolution Satellite SIF Products},
    JOURNAL = {Remote Sensing},
    VOLUME = {14},
    YEAR = {2022},
    NUMBER = {20},
    ARTICLE-NUMBER = {5107},
    URL = {https://www.mdpi.com/2072-4292/14/20/5107},
    ISSN = {2072-4292},
    ABSTRACT = {The upcoming Fluorescence Explorer (FLEX) mission will provide sun-induced fluorescence (SIF) products at unprecedented spatial resolution. Thus, accurate calibration and validation (cal/val) of these products are key to guarantee robust SIF estimates for the assessment and quantification of photosynthetic processes. In this study, we address one specific component of the uncertainty budget related to SIF retrieval: the spatial representativeness of in situ SIF observations compared to medium-resolution SIF products (e.g., 300 m pixel size). Here, we propose an approach to evaluate an optimal sampling strategy to characterise the spatial representativeness of in situ SIF observations based on high-spatial-resolution SIF data. This approach was applied for demonstration purposes to two agricultural areas that have been extensively characterized with a HyPlant airborne imaging spectrometer in recent years. First, we determined the spatial representativeness of an increasing number of sampling points with respect to a reference area (either monocultural crop fields or hypothetical FLEX pixels characterised by different land cover types). Then, we compared different sampling approaches to determine which strategy provided the most representative reference data for a given area. Results show that between 3 and 13.5 sampling points are needed to characterise the average SIF value of both monocultural fields and hypothetical FLEX pixels of the agricultural areas considered in this study. The number of sampling points tends to increase with the standard deviation of SIF of the reference area, as well as with the number of land cover classes in a FLEX pixel, even if the increase is not always statistically significant. This study contributes to guiding cal/val activities for the upcoming FLEX mission, providing useful insights for the selection of the validation site network and particularly for the definition of the best sampling scheme for each site.},
    DOI = {10.3390/rs14205107}
    }

  • D. Schulz and J. Börner, “Innovation context and technology traits explain heterogeneity across studies of agricultural technology adoption: A meta‐analysis,” Journal of Agricultural Economics, p. 1477–9552.12521, 2022. doi:10.1111/1477-9552.12521
    [BibTeX] [PDF]
    @article{schulz_innovation_2022,
    title = {Innovation context and technology traits explain heterogeneity across studies of agricultural technology adoption: {A} meta‐analysis},
    copyright = {All rights reserved},
    issn = {0021-857X, 1477-9552},
    shorttitle = {Innovation context and technology traits explain heterogeneity across studies of agricultural technology adoption},
    url = {https://onlinelibrary.wiley.com/doi/10.1111/1477-9552.12521},
    doi = {10.1111/1477-9552.12521},
    language = {en},
    urldate = {2022-11-23},
    journal = {Journal of Agricultural Economics},
    author = {Schulz, Dario and Börner, Jan},
    month = nov,
    year = {2022},
    pages = {1477--9552.12521},
    }

  • R. Hossain, F. R. Ispizua Yamati, A. Barreto, F. Savian, M. Varrelmann, A. Mahlein, and S. Paulus, “Elucidation of turnip yellows virus (TuYV) spectral reflectance pattern in Nicotiana benthamiana by non-imaging sensor technology,” Journal of Plant Diseases and Protection, 2022. doi:10.1007/s41348-022-00682-9
    [BibTeX]
    @article{Hossain_Plantdiseases,
    title = {Elucidation of turnip yellows virus (TuYV) spectral reflectance pattern in Nicotiana benthamiana by non-imaging sensor technology},
    journal = {Journal of Plant Diseases and Protection},
    year = {2022},
    doi = {10.1007/s41348-022-00682-9},
    author = {Hossain, Roxana and Ispizua Yamati, Facundo Ramón and Barreto, Abel and Savian, Francesco and Varrelmann, Mark and Mahlein, Anne-Kathrin and Paulus, Stefan},
    }

  • M. Günder, N. Piatkowski, and C. Bauckhage, “Full Kullback-Leibler-Divergence Loss for Hyperparameter-free Label Distribution Learning,” preprint, 2022. doi:10.48550/ARXIV.2209.02055
    [BibTeX] [PDF]
    @article{https://doi.org/10.48550/arxiv.2209.02055,
    title = {Full Kullback-Leibler-Divergence Loss for Hyperparameter-free Label Distribution Learning},
    journal = {preprint},
    publisher = {arXiv},
    year = {2022},
    author = {Günder, Maurice and Piatkowski, Nico and Bauckhage, Christian},
    doi = {10.48550/ARXIV.2209.02055},
    url = {https://arxiv.org/abs/2209.02055}
    }

  • A. Brugger, I. F. Yamati, A. Barreto, S. Paulus, P. Schramowski, K. Kersting, U. Steiner, S. Neugart, and A. -K. Mahlein, “Hyperspectral imaging in the UV-range allows for differentiation of sugar beet diseases based on changes of secondary plant metabolites,” Phytopathology, 2022. doi:10.1094/PHYTO-03-22-0086-R
    [BibTeX]
    @article{Brugger_Pythopathology,
    title = {Hyperspectral imaging in the UV-range allows for differentiation of sugar beet diseases based on changes of secondary plant metabolites},
    journal = {Phytopathology},
    year = {2022},
    doi = {10.1094/PHYTO-03-22-0086-R},
    author = {A. Brugger and F. Ispizua Yamati and A. Barreto and S. Paulus and P. Schramowski and K. Kersting and U. Steiner and S. Neugart and A.-K. Mahlein}
    }

  • J. Kierdorf, L. V. Junker-Frohn, M. Delaney, D. M. Olave, A. Burkart, H. Jaenicke, O. Muller, U. Rascher, and R. Roscher, “GrowliFlower: An image time-series dataset for GROWth analysis of cauLIFLOWER,” Journal of Field Robotics, 2022. doi:http://doi.org/10.1002/rob.22122
    [BibTeX]
    @article{Kierdorf_JournalofFieldRobotics,
    title = {GrowliFlower: An image time-series dataset for GROWth analysis of cauLIFLOWER},
    journal = {Journal of Field Robotics},
    year = {2022},
    issn = {1556-4959},
    doi = {http://doi.org/10.1002/rob.22122},
    author = {J. Kierdorf and L.V. Junker-Frohn and M. Delaney and M. Donoso Olave and A. Burkart, and H. Jaenicke and O. Muller and U. Rascher and R. Roscher}
    }

  • N. Senapati, M. A. Semenov, N. G. Halford, M. J. Hawkesford, S. Asseng, M. Cooper, F. Ewert, M. K. van Ittersum, P. Martre, J. E. Olesen, M. Reynolds, R. P. Rötter, and H. Webber, “Global wheat production could benefit from closing the genetic yield gap,” Nature Food, vol. 3, 2022. doi:10.1038/s43016-022-00540-9
    [BibTeX]
    @Article{Senapati_naturefood,
    author = {N. Senapati and M. A. Semenov and N. G. Halford and M. J. Hawkesford and S. Asseng and M. Cooper and F. Ewert and M. K. van Ittersum and P. Martre and J. E. Olesen and M. Reynolds and R. P. Rötter and H. Webber},
    title = {Global wheat production could benefit from closing the genetic yield gap},
    journal = {Nature Food},
    year = {2022},
    volume = {3},
    doi = {10.1038/s43016-022-00540-9}
    }

  • G. Hölzl, R. B. Rezaeva, J. Kumlehn, and P. Dörmann, “Ablation of glucosinolate accumulation in the oil crop Camelina sativa by targeted mutagenesis of genes encoding the transporters GTR1 and GTR2 and regulators of biosynthesis MYB28 and MYB29,” Plant Biotechnology Journal, 2022. doi:10.1111/pbi.13936
    [BibTeX] [PDF]
    @Article{Doermann_plantbiotechnology,
    author = {G. Hölzl and B. Ruzimurodovna Rezaeva and J. Kumlehn and P. Dörmann},
    title = {Ablation of glucosinolate accumulation in the oil crop Camelina sativa by targeted mutagenesis of genes encoding the transporters GTR1 and GTR2 and regulators of biosynthesis MYB28 and MYB29},
    journal = {Plant Biotechnology Journal},
    year = {2022},
    doi = {10.1111/pbi.13936},
    url = {https://onlinelibrary.wiley.com/doi/epdf/10.1111/pbi.13936}
    }

  • J. S. Bates, F. Jonard, H. Vereecken, and C. Montzka, “UAS LiDAR Local Maximum Filtering for Individual Maize Detection,” in IGARSS 2022 – 2022 IEEE International Geoscience and Remote Sensing Symposium , 2022, pp. 520-522. doi:10.1109/IGARSS46834.2022.9883527
    [BibTeX]
    @INPROCEEDINGS{9883527,
    author={Bates, Jordan Steven and Jonard, François and Vereecken, Harry and Montzka, Carsten},
    booktitle={IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium},
    title={UAS LiDAR Local Maximum Filtering for Individual Maize Detection},
    year={2022},
    pages={520-522},
    doi={10.1109/IGARSS46834.2022.9883527}}

  • J. S. Bates, F. Jonard, R. Bajracharya, H. Vereecken, and C. Montzka, “UAS Lidar Derived Metrics for Winter Wheat Biomass Estimations using Multiple Linear Regression,” in IGARSS 2022 – 2022 IEEE International Geoscience and Remote Sensing Symposium , 2022, pp. 7234-7237. doi:10.1109/IGARSS46834.2022.9883339
    [BibTeX]
    @INPROCEEDINGS{9883339,
    author={Bates, Jordan Steven and Jonard, François and Bajracharya, Rajina and Vereecken, Harry and Montzka, Carsten},
    booktitle={IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium},
    title={UAS Lidar Derived Metrics for Winter Wheat Biomass Estimations using Multiple Linear Regression},
    year={2022},
    pages={7234-7237},
    doi={10.1109/IGARSS46834.2022.9883339}}

  • E. Chakhvashvili, J. Bendig, B. Siegmann, O. Muller, J. Verrelst, and U. Roscher, “LAI and Leaf Chlorophyll Content Retrieval Under Changing Spatial Scale Using a UAV-Mounted Multispectral Camera,” in IGARSS 2022 – 2022 IEEE International Geoscience and Remote Sensing Symposium , 2022, pp. 7891-7894. doi:10.1109/IGARSS46834.2022.9883446
    [BibTeX]
    @INPROCEEDINGS{9883446,
    author={Chakhvashvili, Erekle and Bendig, Juliane and Siegmann, Bastian and Muller, Onno and Verrelst, Jochem and Roscher, Uwe},
    booktitle={IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium},
    title={LAI and Leaf Chlorophyll Content Retrieval Under Changing Spatial Scale Using a UAV-Mounted Multispectral Camera},
    year={2022},
    volume={},
    number={},
    pages={7891-7894},
    doi={10.1109/IGARSS46834.2022.9883446}}

  • C. Smitt, M. Halstead, A. Ahmadi, and C. McCool, “Explicitly incorporating spatial information to recurrent networks for agriculture,” Robotics and Automation Letters, 2022.
    [BibTeX]
    @Article{Smitt22_journal,
    author = {C. Smitt and M. Halstead and A. Ahmadi and C. McCool},
    title = {Explicitly incorporating spatial information to recurrent networks for agriculture},
    journal = {Robotics and Automation Letters},
    year = {2022},
    }

  • A. Ahmadi, M. Halstead, and C. McCool, “Towards Autonomous Visual Navigation in Arable Fields,” in IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) , 2022.
    [BibTeX]
    @InProceedings{Ahmadi22_IROS2,
    author = {A. Ahmadi and M. Halstead and C. McCool},
    title = {Towards Autonomous Visual Navigation in Arable Fields},
    booktitle = {IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
    year = {2022},
    }

  • A. Ahmadi, M. Halstead, and C. McCool, “BonnBot-I: a precise weed management and crop monitoring platform,” in IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) , 2022.
    [BibTeX]
    @InProceedings{Ahmadi22_IROS1,
    author = {A. Ahmadi and M. Halstead and C. McCool},
    title = {BonnBot-I: a precise weed management and crop monitoring platform},
    booktitle = {IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
    year = {2022},
    }

  • N. P. Laha, R. F. H. Giehl, E. Riemer, D. Qui, N. J. Pullagurla, R. Schneider, Y. W. Dhir, R. Yadav, Y. E. Mihiret, P. Gaugler, G. V., H. Mao, Z. N., N. von Wirén, A. Saiardi, S. Bhattacharjee, J. H. Jessen, L. D., and G. Schaaf, “INOSITOL (1,3,4) TRIPHOSPHATE 5/6 KINASE1-Dependent Inositol Polyphosphates Regulate Auxin Responses in Arabidopsis,” Plant Physiology, 2022. doi:10.1093/plphys/kiac425
    [BibTeX] [PDF]
    @article{Schaaf2022PlantPhysiology,
    author = {Laha, N. P. AND Giehl, R. F. H. AND Riemer, E. AND Qui, D. AND Pullagurla, N. J. AND Schneider, R. AND Dhir, Y. W. AND Yadav, R. AND Mihiret, Y. E. AND Gaugler, P. AND Gaugler V. AND Mao, H. AND Zheng N. AND von Wirén, N. AND Saiardi, A AND Bhattacharjee, S. AND Jessen, J. H. AND Laha D. AND Schaaf, G..},
    title = {{INOSITOL (1,3,4) TRIPHOSPHATE 5/6 KINASE1-Dependent Inositol Polyphosphates Regulate Auxin Responses in Arabidopsis}},
    journal = {Plant Physiology},
    year = {2022},
    month = {09},
    doi = {10.1093/plphys/kiac425},
    issn = {0032-0889},
    url = {https://academic.oup.com/plphys/advance-article-pdf/doi/10.1093/plphys/kiac425/45911630/kiac425.pdf},
    }

  • H. Vereecken, W. Amelung, S. L. Bauke, H. Bogena, N. Brüggemann, C. Montzka, J. Vanderborght, M. Bechtold, G. Blöschl, A. Carminati, M. Javaux, A. G. Konings, J. Kusche, I. Neuweiler, D. Or, S. Steele-Dunne, A. Verhoef, M. Young, and Y. Zhang, “Soil hydrology in the Earthsystem,” Nature Reviews Earth & Environment, vol. 3, pp. 573-587, 2022. doi:10.1038/s43017-022-00324-6
    [BibTeX]
    @article{Vereecken2022NatRevEarthEnviron,
    author = {Vereecken, Harry AND Amelung, Wulf AND Bauke, Sara L. AND Bogena, Heye AND Brüggemann, Nicolas AND Montzka, Carsten AND Vanderborght, Jan AND Bechtold, Michel AND Blöschl, Günter AND Carminati, Andrea AND Javaux, Mathieu AND Konings, Alexandra G. AND Kusche, Jürgen AND Neuweiler, Insa AND Or, Dani AND Steele-Dunne, Susan AND Verhoef, Anne AND Young, Michael AND Zhang, Yonggen},
    title = {{Soil hydrology in the Earthsystem}},
    journal = {Nature Reviews Earth & Environment},
    volume = {3},
    year = {2022},
    doi = {10.1038/s43017-022-00324-6},
    pages = {573-587},
    }

  • C. W. Kuppe, A. Schnepf, E. Lieres, M. Watt, and J. A. Postma, “Rhizosphere models: their concepts and application to plant-soil ecosystems,” Plant and Soil, vol. 474, pp. 17-55, 2022. doi:10.1007/s11104-021-05201-7
    [BibTeX]
    @article{Kuppe2022PlantAndSoil,
    author = {Kuppe, C.W. AND Schnepf, A. AND Lieres, E. AND Watt, M. AND Postma, J.A.},
    title = {{Rhizosphere models: their concepts and application to plant-soil ecosystems}},
    journal = {Plant and Soil},
    volume = {474},
    year = {2022},
    doi = {10.1007/s11104-021-05201-7},
    pages = {17-55},
    }

  • J. Rückin, J. Liren, and M. Popović, “Adaptive Informative Path Planning Using Deep Reinforcement Learning for UAV-based Active Sensing,” in Proc. of the IEEE/RSJ Intl. Conf. on Robotics and Automation (ICRA) , 2022.
    [BibTeX]
    @InProceedings{Rückin2022ICRA,
    author = {Rückin, J. AND Liren, J. AND Popović, M.},
    title = {{Adaptive Informative Path Planning Using Deep Reinforcement Learning for UAV-based Active Sensing}},
    booktitle = {Proc. of the IEEE/RSJ Intl. Conf. on Robotics and Automation (ICRA)},
    year = {2022},
    }

  • L. Jin, J. Rückin, S. H. Kiss, T. Vidal-Calleja, and M. Popović, “Adaptive-Resolution Field Mapping Using Gaussian Process Fusion With Integral Kernels,” IEEE Robotics and Automation Letters, vol. 7, pp. 7471-7478, 2022. doi:10.1109/LRA.2022.3183797
    [BibTeX]
    @article{Jin2022IEEE,
    author = {Jin, L. AND Rückin, J. AND Kiss, S. H. AND Vidal-Calleja, T. AND Popović, M.},
    title = {{Adaptive-Resolution Field Mapping Using Gaussian Process Fusion With Integral Kernels}},
    journal = {IEEE Robotics and Automation Letters},
    volume = {7},
    issue = {3},
    year = {2022},
    doi = {10.1109/LRA.2022.3183797},
    pages = {7471-7478},
    }

  • A. Massfeller, M. Meraner, S. Huettel, and R. Uehleke, “Farmers’ acceptance of results-based agri-environmental schemes: A German perspective,” Land Use Policy, vol. 120, 2022. doi:10.1016/j.landusepol.2022.106281
    [BibTeX] [PDF]
    @article{Massfeller2022LandUsePolicy,
    author = {Massfeller, A. AND Meraner, M. AND Huettel, S. AND Uehleke, R.},
    title = {{Farmers' acceptance of results-based agri-environmental schemes: A German perspective}},
    journal = {Land Use Policy},
    volume = {120},
    year = {2022},
    doi = {10.1016/j.landusepol.2022.106281},
    issn = {1-12},
    url = {https://www.sciencedirect.com/science/article/pii/S0264837722003088/pdfft?md5=bd306e3f4f6533272484eee9c634c4e8&pid=1-s2.0-S0264837722003088-main.pdf},
    }

  • J. Bates, F. Jonard, R. Bajracharya, H. Vereecken, and C. Montzka, “Machine Learning with UAS LiDAR for Winter Wheat Biomass Estimations,” AGILE GISience Series, vol. 3, pp. 1-4, 2022. doi:10.5194/agile-giss-3-23-2022
    [BibTeX] [PDF]
    @article{Bates2022AGILEJ,
    author = {Bates, J. AND Jonard, F. AND Bajracharya, R. AND Vereecken, H. AND Montzka, C.},
    title = {{Machine Learning with UAS LiDAR for Winter Wheat Biomass Estimations}},
    journal = {AGILE GISience Series},
    volume = {3},
    issue = {23},
    year = {2022},
    doi = {10.5194/agile-giss-3-23-2022},
    pages = {1-4},
    url = {https://agile-giss.copernicus.org/articles/3/23/2022/agile-giss-3-23-2022.pdf},
    }

  • J. Bindics, K. Mamoona, S. Uhse, B. Kogelmann, L. Baggely, D. Reumann, K. D. Ingole, A. Stirnberg, A. Rybecky, M. Darino, F. Navarrete, G. Dochlemann, and A. Djamei, “Many ways to TOPLESS-manipulation of plant auxin signalling by a cluster of fungal effectors,” New Phytologist Foundation, 2022. doi:10.1111/nph.18315
    [BibTeX]
    @article{Djamei2022NewPhytologistFoundation,
    author = {Bindics, J. AND Mamoona, K. AND Uhse, S. AND Kogelmann, B. AND Baggely, L. AND Reumann, D. AND Ingole, K. D. AND Stirnberg, A. AND Rybecky, A. AND Darino, M. AND Navarrete, F. AND Dochlemann, G. AND Djamei, A. },
    title = {{Many ways to TOPLESS-manipulation of plant auxin signalling by a cluster of fungal effectors}},
    journal = {New Phytologist Foundation},
    year = {2022},
    doi = {10.1111/nph.18315},
    }

  • C. Latka, A. Parodi, O. van Hal, T. Heckelei, A. Leip, H. Witzke, and H. H. E. van Zanten, “Competing for food waste – Policies’ market feedbacks imply sustainability tradeoffs,” Resources, Conservation and Recycling, vol. 186, 2022. doi:10.1016/j.resconrec.2022.106545
    [BibTeX]
    @article{Heckelei2022ResourcesConservationAndRecycling,
    author = {Latka, C. AND Parodi, A. AND van Hal, O. AND Heckelei, T. AND Leip, A. AND Witzke, HP. AND van Zanten, H.H.E.},
    title = {{Competing for food waste – Policies’ market feedbacks imply sustainability tradeoffs}},
    journal = {Resources, Conservation and Recycling},
    volume = {186},
    year = {2022},
    doi = {10.1016/j.resconrec.2022.106545},
    }

  • E. Riemer, N. Jyothi Pullagurla, R. Yadav, P. Rana, H. J. Jessen, M. Kamleitner, G. Schaaf, and D. Laha, “Regulation of plant biotic interactions and abiotic stress responses by inositol polyphosphates,” Frontiers in Plant Science, vol. 13, pp. 1-18, 2022. doi:10.3389/fpls.2022.944515
    [BibTeX] [PDF]
    @article{Schaaf2022FrontPlantSci,
    author = {Riemer, E. AND Jyothi Pullagurla, N. AND Yadav, R. AND Rana, P. AND Jessen, H. J. AND Kamleitner, M. AND Schaaf, G. AND Laha, D.},
    title = {{Regulation of plant biotic interactions and abiotic stress responses by inositol polyphosphates}},
    journal = {Frontiers in Plant Science},
    volume = {13},
    year = {2022},
    doi = {10.3389/fpls.2022.944515},
    pages = {1-18},
    url = {https://www.frontiersin.org/articles/10.3389/fpls.2022.944515/pdf},
    }

  • I. M. Hernandez-Ochoa, T. Gaiser, K. Kersebaum, H. Webber, S. J. Seidel, K. Grahmann, and F. Ewert, “Model-based design of crop diversification through new field arrangements in spatially heterogeneous landscapes. A review.,” Agronomy for Sustainable Development, vol. 42, pp. 1-25, 2022. doi:10.1007/s13593-022-00805-4
    [BibTeX] [PDF]
    @article{Hernandez-Ochoa2022ASD,
    author = {Hernandez-Ochoa, I. M. AND Gaiser, T. AND Kersebaum, KC. AND Webber, H. AND Seidel, S. J. AND Grahmann, K. AND Ewert, F. },
    title = {{Model-based design of crop diversification through new field arrangements in spatially heterogeneous landscapes. A review.}},
    journal = {Agronomy for Sustainable Development},
    volume = {42},
    issue = {4},
    year = {2022},
    doi = {10.1007/s13593-022-00805-4},
    pages = {1-25},
    url = {https://link.springer.com/content/pdf/10.1007/s13593-022-00805-4.pdf},
    }

  • O. Esmaeelipoor Jahromi, M. Knott, R. K. Janakiram, R. Rahim, and E. Kroener, “Pore-scale simulation of mucilage drainage,” Vadose Zone Journal, vol. e20218, pp. 1-13, 2022. doi:10.1002/vzj2.20218
    [BibTeX] [PDF]
    @article{EsmaeelipoorJahromi2022VadoseZoneJ,
    author = {Esmaeelipoor Jahromi, O. AND Knott, M. AND Janakiram, R. K. AND Rahim, R. AND Kroener, E.},
    title = {{Pore-scale simulation of mucilage drainage}},
    journal = {Vadose Zone Journal},
    volume = {e20218},
    year = {2022},
    doi = {10.1002/vzj2.20218},
    pages = {1-13},
    url = {https://acsess.onlinelibrary.wiley.com/doi/epdf/10.1002/vzj2.20218},
    }

  • J. Knechtel, L. Klingbeil, J. -H. Haunert, and Y. Dehbi, “Optimal Position and Path Planning for Stop-and-Go Laserscanning for the Acquisition of 3D Building Models.” 2022, p. 129–136. doi:10.5194/isprs-annals-V-4-2022-129-2022
    [BibTeX] [PDF]
    @InProceedings{Knechtel2022ISPRS,
    AUTHOR = {Knechtel, J. and Klingbeil, L. and Haunert, J.-H. and Dehbi, Y.},
    TITLE = {Optimal Position and Path Planning for Stop-and-Go Laserscanning for the Acquisition of 3D Building Models},
    JOURNAL = {ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences},
    VOLUME = {V-4-2022},
    YEAR = {2022},
    PAGES = {129--136},
    URL = {https://www.isprs-ann-photogramm-remote-sens-spatial-inf-sci.net/V-4-2022/129/2022/},
    DOI = {10.5194/isprs-annals-V-4-2022-129-2022}
    }

  • L. Klingbeil, A. Dreier, F. Esser, L. Zabawa, D. Pavlic, and H. Kuhlmann, “Mobile Mapping auf dem Acker – hochaufgelöste 3D-Vermessung für nachhaltige Planzenproduktion,” Allgemeine Vermessungs-Nachrichten (AVN), vol. 03/2022, pp. 96-103, 2022.
    [BibTeX] [PDF]
    @article{Klingbeil2022AVN,
    author = {Klingbeil, L. AND Dreier, A. AND Esser, F. AND Zabawa, L. AND Pavlic, D. AND Kuhlmann, H.},
    title = {Mobile Mapping auf dem Acker - hochaufgelöste 3D-Vermessung für nachhaltige Planzenproduktion},
    journal = {Allgemeine Vermessungs-Nachrichten (AVN)},
    volume = {03/2022},
    issue = {129},
    year = {2022},
    pages = {96-103},
    url = {file:///C:/Users/Herz/AppData/Local/Microsoft/Windows/INetCache/Content.Outlook/5A9224LH/S_96-103_Klingbeil_u_a_avn_3_2022.pdf},
    }

  • M. Guender, F. R. Ispizua Yamati, J. Kierdorf, R. Roscher, A. -K. Mahlein, and C. Bauckhage, “Agricultural plant cataloging and establishment of a data framework from UAV-based crop images by computer vision,” GigaScience, vol. 11, p. 1–14, 2022. doi:10.1093/gigascience/giac054
    [BibTeX]
    @article{guender2022gigascience,
    author = {Guender, M. AND Ispizua Yamati, F.R. AND Kierdorf, J. AND Roscher, R. AND Mahlein, A.-K. AND Bauckhage, C.},
    title = {{Agricultural plant cataloging and establishment of a data framework from UAV-based crop images by computer vision}},
    journal = {GigaScience},
    volume = {11},
    pages = {1--14},
    year = {2022},
    month = {06},
    publisher = {Oxford University Press},
    doi = {10.1093/gigascience/giac054},
    }

  • S. De Canniere, H. Vereecken, P. Defourny, and F. Jonard, “Remote Sensing of Instantaneous Drought Stress at Canopy Level Using Sun-Induced Chlorophyll Fluorescence and Canopy Reflectance,” Remote Sensing, vol. 14, 2022. doi:10.3390/rs14112642
    [BibTeX] [PDF]
    @Article{decanniere2022remotese,
    AUTHOR = {De Canniere, S. AND Vereecken, H. AND Defourny, P. AND Jonard, F.},
    TITLE = {Remote Sensing of Instantaneous Drought Stress at Canopy Level Using Sun-Induced Chlorophyll Fluorescence and Canopy Reflectance},
    JOURNAL = {Remote Sensing},
    VOLUME = {14},
    ISSUE = {2072-4292},
    YEAR = {2022},
    URL = {https://www.mdpi.com/2072-4292/14/11/2642},
    DOI = {10.3390/rs14112642}
    }

  • P. Gaugler, R. Schneider, G. Liu, D. Qiu, J. Weber, J. Schmid, N. Jork, M. Häner, K. Ritter, N. Fernández-Rebollo, R. F. H. Giehl, M. N. Trung, R. Yadav, D. Fiedler, V. Gaugler, H. J. Jessen, G. Schaaf, and D. Laha, “Arabidopsis PFA-DSP-Type Phosphohydrolases Target Specific Inositol Pyrophosphate Messengers,” Biochemistry, 2022. doi:10.1021/acs.biochem.2c00145
    [BibTeX] [PDF]
    @article{gaugler2022biochem,
    author = {Gaugler, P. AND Schneider, R. AND Liu, G. AND Qiu, D. AND Weber, J. AND Schmid, J. AND Jork, N. AND Häner, M. AND Ritter, K. AND Fernández-Rebollo, N. AND Giehl, R. F. H. AND Trung, M. N. AND Yadav, R. AND Fiedler, D. AND Gaugler, V. AND Jessen, H. J. AND Schaaf, G. AND Laha, D.},
    title = {Arabidopsis PFA-DSP-Type Phosphohydrolases Target Specific Inositol Pyrophosphate Messengers},
    journal = {Biochemistry},
    year = {2022},
    doi = {10.1021/acs.biochem.2c00145},
    URL = { https://doi.org/10.1021/acs.biochem.2c00145},
    }

  • M. Miranda, L. Zabawa, A. Kicherer, L. Strothmann, U. Rascher, and R. Roscher, “Detection of Anomalous Grapevine Berries Using Variational Autoencoders,” Frontiers in Plant Science, vol. 13, 2022. doi:10.3389/fpls.2022.729097
    [BibTeX] [PDF]
    @article{miranda2022frontplantsci,
    author={Miranda, M. AND Zabawa, L. AND Kicherer, A. AND Strothmann, L. AND Rascher, U. AND Roscher, R.},
    title={Detection of Anomalous Grapevine Berries Using Variational Autoencoders},
    journal={Frontiers in Plant Science},
    volume={13},
    year={2022},
    URL={https://www.frontiersin.org/article/10.3389/fpls.2022.729097},
    doi={10.3389/fpls.2022.729097},
    }

  • F. Bauer, L. Lärm, S. Morandage, G. Lobet, J. Vanderborght, H. Vereecken, and A. Schnepf, “Development and Validation of a Deep Learning Based Automated Minirhizotron Image Analysis Pipeline,” Plant Phenomics, vol. 2022, 2022. doi:https://doi.org/10.34133/2022/9758532
    [BibTeX]
    @article{bauer2022plantpheno,
    author = {Bauer, F. AND Lärm, L. AND Morandage, S. AND Lobet, G. AND Vanderborght, J. AND Vereecken, H. AND Schnepf, A.},
    title = {{Development and Validation of a Deep Learning Based Automated Minirhizotron Image Analysis Pipeline}},
    journal = {Plant Phenomics},
    volume = {2022},
    year = {2022},
    doi = {https://doi.org/10.34133/2022/9758532},
    }

  • M. Tazifor, E. Zimmermann, J. A. Huisman, M. Dick, A. Mester, and S. Van Waasen, “Model-Based Correction of Temperature-Dependent Measurement Errors in Frequency Domain Electromagnetic Induction (FDEMI) Systems,” Sensors, vol. 22, 2022. doi:10.3390/s22103882
    [BibTeX]
    @article{tazifor2022sensors,
    author = {Tazifor, M. AND Zimmermann, E. AND Huisman, J.A. AND Dick, M. AND Mester, A. AND Van Waasen, S.},
    title = {{Model-Based Correction of Temperature-Dependent Measurement Errors in Frequency Domain Electromagnetic Induction (FDEMI) Systems}},
    journal = {Sensors},
    volume = {22},
    year = {2022},
    doi = {10.3390/s22103882},
    }

  • I. Saado, K. Chia, R. Betz, A. Alcantara, A. Pettko-Szandtner, F. Navarrete, J. C. D’Auria, M. V. Kolomiets, M. Melzer, I. Feussner, and A. Djamei, “Effector-mediated relocalization of a maize lipoxygenase protein triggers susceptibility to Ustilago maydis,” The Plant Cell, pp. 1-21, 2022. doi:https://doi.org/10.1093/plcell/koac105
    [BibTeX] [PDF]
    @article{saado2022plantcell,
    author = {Saado, I. AND Chia, K. AND Betz, R. AND Alcantara, A. AND Pettko-Szandtner, A. AND Navarrete, F. AND D'Auria, J. C. AND Kolomiets, M. V. AND Melzer, M. AND Feussner, I. AND Djamei, A.},
    title = {{Effector-mediated relocalization of a maize lipoxygenase
    protein triggers susceptibility to Ustilago maydis}},
    journal = {The Plant Cell},
    year = {2022},
    issn = {1040-4651},
    pages = {1-21},
    doi = {https://doi.org/10.1093/plcell/koac105},
    url = {https://academic.oup.com/plcell/advance-article-pdf/doi/10.1093/plcell/koac105/43541391/koac105.pdf},
    }

  • L. Zabawa, A. Kicherer, L. Klingbeil, R. Töpfer, R. Roscher, and H. Kuhlmann, “Image-based analysis of yield parameters in viticulture,” Biosystems Engineering, vol. 218, pp. 94-109, 2022. doi:https://doi.org/10.1016/j.biosystemseng.2022.04.009
    [BibTeX] [PDF]

    Yield estimation is of great interest in viticulture, since an early estimation could influence management decisions of winegrowers. The current practice involves destructive sampling of small sets in the field and a subsequent detailed analysis in the laboratory. The results are extrapolated to the field and only approximate the actual conditions. Therefore, research in recent years focused on sensor-based systems mounted on field vehicles since they offer a fast, accurate and robust data acquisition. However many works stop after detecting fruits, rarely the actual yield estimation is tackled. We present a novel yield estimation pipeline that uses images captured by a multi-camera system. The system is mounted on a field phenotyping platform called Phenoliner, which has been built from a modified grapevine harvester. We use a neural network whose output is used to count berries in single images. In contrast to other existing methods we take the step from the single vine image processing to the plant level. The information of multiple images is used to acquire a count on plant level and the approach is extended to the processing based on the whole row. The acquired berry counts are used as input for the yield estimation, and we explore the limitations and potentials of our pipeline. We identify the variability of the leaf occlusion as the main limiting factor, but nonetheless we achieve a mean absolute yield prediction error of 26\% for plants in the vertical shoot positioned system. We evaluate each described stage comprehensively in this study.

    @article{ZABAWA202294,
    title = {Image-based analysis of yield parameters in viticulture},
    journal = {Biosystems Engineering},
    volume = {218},
    pages = {94-109},
    year = {2022},
    issn = {1537-5110},
    doi = {https://doi.org/10.1016/j.biosystemseng.2022.04.009},
    url = {https://www.sciencedirect.com/science/article/pii/S1537511022000861},
    author = {Zabawa, L. AND Kicherer, A. AND Klingbeil, L. AND Töpfer, R. AND Roscher, R. AND Kuhlmann, H.},
    keywords = {Deep Learning, Semantic Segmentation, Geoinformation, Viticulture, Yield Estimation},
    abstract = {Yield estimation is of great interest in viticulture, since an early estimation could influence management decisions of winegrowers. The current practice involves destructive sampling of small sets in the field and a subsequent detailed analysis in the laboratory. The results are extrapolated to the field and only approximate the actual conditions. Therefore, research in recent years focused on sensor-based systems mounted on field vehicles since they offer a fast, accurate and robust data acquisition. However many works stop after detecting fruits, rarely the actual yield estimation is tackled. We present a novel yield estimation pipeline that uses images captured by a multi-camera system. The system is mounted on a field phenotyping platform called Phenoliner, which has been built from a modified grapevine harvester. We use a neural network whose output is used to count berries in single images. In contrast to other existing methods we take the step from the single vine image processing to the plant level. The information of multiple images is used to acquire a count on plant level and the approach is extended to the processing based on the whole row. The acquired berry counts are used as input for the yield estimation, and we explore the limitations and potentials of our pipeline. We identify the variability of the leaf occlusion as the main limiting factor, but nonetheless we achieve a mean absolute yield prediction error of 26\% for plants in the vertical shoot positioned system. We evaluate each described stage comprehensively in this study.}
    }

  • A. S. Wendel, S. L. Bauke, W. Amelung, and C. Knief, “Root-rhizosphere-soil interactions in biopores,” Plant and Soil, p. 1–25, 2022. doi:10.1007/s11104-022-05406-4
    [BibTeX] [PDF]
    @article{wendel2022root,
    author = {Wendel, A.S. AND Bauke, S.L. AND Amelung, W. AND Knief, C.},
    title = {{Root-rhizosphere-soil interactions in biopores}},
    journal = {Plant and Soil},
    year = {2022},
    doi = {10.1007/s11104-022-05406-4},
    pages = {1--25},
    url = {https://link.springer.com/content/pdf/10.1007/s11104-022-05406-4.pdf},
    }

  • D. Demie, T. Döring, M. Finckh, W. van der Werf, J. Enjalbert, and S. Seidel, “Mixture X Genotype Effects in Cereal/Legume Intercropping,” Frontiers in Plant Science, vol. 13, 2022. doi:10.3389/fpls.2022.846720
    [BibTeX]
    @article{demie2022frontiers,
    author = {Demie, D. AND Döring, T. AND Finckh, M. AND van der Werf, W. AND Enjalbert, J. AND Seidel, S.},
    title = {{Mixture X Genotype Effects in Cereal/Legume Intercropping}},
    journal = {Frontiers in Plant Science},
    volume = {13},
    issue = {1664-462X},
    year = {2022},
    doi = {10.3389/fpls.2022.846720},
    }

  • E. Chakhvashvili, B. Siegmann, O. Muller, J. Verrelst, J. Bendig, T. Kraska, and U. Rascher, “Retrieval of Crop Variables from Proximal Multispectral UAV Image Data Using PROSAIL in Maize Canopy,” Remote Sensing, vol. 14, 2022. doi:10.3390/rs14051247
    [BibTeX]
    @article{Chakhvashvili2022remote,
    author = {Chakhvashvili, E. AND Siegmann, B. AND Muller, O. AND Verrelst, J. AND Bendig, J. AND Kraska, T. AND Rascher, U.},
    title = {{Retrieval of Crop Variables from Proximal Multispectral UAV Image Data Using PROSAIL in Maize Canopy}},
    journal = {Remote Sensing},
    volume = {14},
    issue = {2072-4292},
    year = {2022},
    doi = {10.3390/rs14051247},
    }

  • X. Zeng, T. Zaenker, and M. Bennewitz, “Deep Reinforcement Learning for Next-Best-View Planning in Agricultural Applications,” in Proc.~of the IEEE International Conference on Robotics & Automation (ICRA) , 2022.
    [BibTeX]
    @InProceedings{Zeng22icra,
    author = {X. Zeng and T. Zaenker and M. Bennewitz},
    title = {Deep Reinforcement Learning for Next-Best-View Planning in Agricultural Applications},
    booktitle = {Proc.~of the IEEE International Conference on Robotics \& Automation (ICRA)},
    year = 2022
    }

  • W. Shi, Y. Zhou, X. Zeng, S. Li, and M. Bennewitz, “Enhanced Spatial Attention Graph for Motion Planning in Crowded, Partially Observable Environments,” in Proc.~of the IEEE International Conference on Robotics & Automation (ICRA) , 2022.
    [BibTeX]
    @InProceedings{Shi22icra,
    author = {W. Shi and Y. Zhou and X. Zeng and S. Li and M. Bennewitz},
    title = {Enhanced Spatial Attention Graph for Motion Planning in Crowded, Partially Observable Environments},
    booktitle = {Proc.~of the IEEE International Conference on Robotics \& Automation (ICRA)},
    year = 2022
    }

  • D. Khare, T. Selzner, D. Leitner, J. Vanderborght, H. Vereecken, and A. Schnepf, “Root System Scale Models Significantly Overestimate Root Water Uptake at Drying Soil Conditions,” Frontiers in Plant Science, vol. 13, 2022. doi:10.3389/fpls.2022.798741
    [BibTeX] [PDF]

    Soil hydraulic conductivity (ksoil) drops significantly in dry soils, resulting in steep soil water potential gradients (ψs) near plant roots during water uptake. Coarse soil grid resolutions in root system scale (RSS) models of root water uptake (RWU) generally do not spatially resolve this gradient in drying soils which can lead to a large overestimation of RWU. To quantify this, we consider a benchmark scenario of RWU from drying soil for which a numerical reference solution is available. We analyze this problem using a finite volume scheme and investigate the impact of grid size on the RSS model results. At dry conditions, the cumulative RWU was overestimated by up to 300\% for the coarsest soil grid of 4.0 cm and by 30\% for the finest soil grid of 0.2 cm, while the computational demand increased from 19 s to 21 h. As an accurate and computationally efficient alternative to the RSS model, we implemented a continuum multi-scale model where we keep a coarse grid resolution for the bulk soil, but in addition, we solve a 1-dimensional radially symmetric soil model at rhizosphere scale around individual root segments. The models at the two scales are coupled in a mass-conservative way. The multi-scale model compares best to the reference solution (−20\%) at much lower computational costs of 4 min. Our results demonstrate the need to shift to improved RWU models when simulating dry soil conditions and highlight that results for dry conditions obtained with RSS models of RWU should be interpreted with caution.

    @Article{10.3389/fpls.2022.798741,
    author = {Khare, Deepanshu and Selzner, Tobias and Leitner, Daniel and Vanderborght, Jan and Vereecken, Harry and Schnepf, Andrea},
    title = {Root System Scale Models Significantly Overestimate Root Water Uptake at Drying Soil Conditions},
    journal = {Frontiers in Plant Science},
    volume = {13},
    year = {2022},
    url = {https://www.frontiersin.org/article/10.3389/fpls.2022.798741},
    doi = {10.3389/fpls.2022.798741},
    issn = {1664-462X},
    abstract = {Soil hydraulic conductivity (ksoil) drops significantly in dry soils, resulting in steep soil water potential gradients (ψs) near plant roots during water uptake. Coarse soil grid resolutions in root system scale (RSS) models of root water uptake (RWU) generally do not spatially resolve this gradient in drying soils which can lead to a large overestimation of RWU. To quantify this, we consider a benchmark scenario of RWU from drying soil for which a numerical reference solution is available. We analyze this problem using a finite volume scheme and investigate the impact of grid size on the RSS model results. At dry conditions, the cumulative RWU was overestimated by up to 300\% for the coarsest soil grid of 4.0 cm and by 30\% for the finest soil grid of 0.2 cm, while the computational demand increased from 19 s to 21 h. As an accurate and computationally efficient alternative to the RSS model, we implemented a continuum multi-scale model where we keep a coarse grid resolution for the bulk soil, but in addition, we solve a 1-dimensional radially symmetric soil model at rhizosphere scale around individual root segments. The models at the two scales are coupled in a mass-conservative way. The multi-scale model compares best to the reference solution (−20\%) at much lower computational costs of 4 min. Our results demonstrate the need to shift to improved RWU models when simulating dry soil conditions and highlight that results for dry conditions obtained with RSS models of RWU should be interpreted with caution.},
    }

  • S. Li, X. Chen, Y. Liu, D. Dai, C. Stachniss, and J. Gall, “Multi-Scale Interaction for Real-Time LiDAR Data Segmentation on an Embedded Platform,” IEEE Robotics and Automation Letters, vol. 7, iss. 2, pp. 738-745, 2022. doi:10.1109/LRA.2021.3132059
    [BibTeX]
    @Article{9633188,
    author = {Li, Shijie and Chen, Xieyuanli and Liu, Yun and Dai, Dengxin and Stachniss, Cyrill and Gall, Juergen},
    journal = {IEEE Robotics and Automation Letters},
    title = {Multi-Scale Interaction for Real-Time LiDAR Data Segmentation on an Embedded Platform},
    year = {2022},
    volume = {7},
    number = {2},
    pages = {738-745},
    doi = {10.1109/LRA.2021.3132059},
    }

  • I. Vizzo, T. Guadagnino, J. Behley, and C. Stachniss, “VDBFusion: Flexible and Efficient TSDF Integration of Range Sensor Data,” Sensors, vol. 22, iss. 3, 2022. doi:10.3390/s22031296
    [BibTeX] [PDF]
    @Article{vizzo2022sensors,
    author = {Vizzo, Ignacio and Guadagnino, Tiziano and Behley, Jens and Stachniss, Cyrill},
    title = {VDBFusion: Flexible and Efficient TSDF Integration of Range Sensor Data},
    journal = {Sensors},
    volume = {22},
    year = {2022},
    number = {3},
    article-number= {1296},
    url = {https://www.mdpi.com/1424-8220/22/3/1296},
    issn = {1424-8220},
    doi = {10.3390/s22031296},
    }

  • L. Wiesmann, R. Marcuzzi, C. Stachniss, and J. Behley, “Retriever: Point Cloud Retrieval in Compressed 3D Maps,” in Proc.~of the IEEE Intl.~Conf.~on Robotics & Automation (ICRA) , 2022.
    [BibTeX]
    @InProceedings{wiesmann2022icra,
    author = {L. Wiesmann and R. Marcuzzi and C. Stachniss and J. Behley},
    title = {{Retriever: Point Cloud Retrieval in Compressed 3D Maps}},
    booktitle = {Proc.~of the IEEE Intl.~Conf.~on Robotics \& Automation (ICRA)},
    year = 2022,
    }

  • E. Marks, F. Magistri, and C. Stachniss, “Precise 3D Reconstruction of Plants from UAV Imagery Combining Bundle Adjustment and Template Matching,” in Proc.~of the IEEE Intl.~Conf.~on Robotics & Automation (ICRA) , 2022.
    [BibTeX]
    @InProceedings{marks2022icra,
    author = {E. Marks and F. Magistri and C. Stachniss},
    title = {{Precise 3D Reconstruction of Plants from UAV Imagery Combining Bundle Adjustment and Template Matching}},
    booktitle = {Proc.~of the IEEE Intl.~Conf.~on Robotics \& Automation (ICRA)},
    year = 2022,
    }

  • L. Mau, S. Junker, H. Bochmann, Y. E. Mihiret, J. M. Kelm, S. D. Schrey, U. Roessner, G. Schaaf, M. Watt, J. Kant, and B. Arsova, “Root Growth and Architecture of Wheat and Brachypodium Vary in Response to Algal Fertilizer in Soil and Solution,” Agronomy, vol. 12, iss. 2, 2022. doi:10.3390/agronomy12020285
    [BibTeX] [PDF]

    Alternative, recycled sources for mined phosphorus (P) fertilizers are needed to sustain future crop growth. Quantification of phenotypic adaptations and performance of plants with a recycled nutrient source is required to identify breeding targets and agronomy practices for new fertilization strategies. In this study, we tested the phenotypic responses of wheat (Triticum aestivum) and its genetic model, Brachypodium (Brachypodium distachyon), to dried algal biomass (with algae or high or low mineral P) under three growing conditions (fabricated ecosystems (EcoFABs), hydroponics, and sand). For both species, algal-grown plants had similar shoot biomass to mineral-grown plants, taking up more P than the low mineral P plants. Root phenotypes however were strongly influenced by nutrient form, especially in soilless conditions. Algae promoted the development of shorter and thicker roots, notably first and second order lateral roots. Root hairs were 21\% shorter in Brachypodium, but 24\% longer in wheat with algae compared to mineral high P. Our results are encouraging to new recycled fertilization strategies, showing algae is a nutrient source to wheat and Brachypodium. Variation in root phenotypes showed algal biomass is sensed by roots and is taken up at a higher amount per root length than mineral P. These phenotypes can be selected and further adapted in phenotype-based breeding for future renewal agriculture systems.

    @Article{agronomy12020285,
    author = {Mau, Lisa and Junker, Simone and Bochmann, Helena and Mihiret, Yeshambel E. and Kelm, Jana M. and Schrey, Silvia D. and Roessner, Ute and Schaaf, Gabriel and Watt, Michelle and Kant, Josefine and Arsova, Borjana},
    title = {Root Growth and Architecture of Wheat and Brachypodium Vary in Response to Algal Fertilizer in Soil and Solution},
    journal = {Agronomy},
    volume = {12},
    year = {2022},
    number = {2},
    article-number= {285},
    url = {https://www.mdpi.com/2073-4395/12/2/285},
    issn = {2073-4395},
    abstract = {Alternative, recycled sources for mined phosphorus (P) fertilizers are needed to sustain future crop growth. Quantification of phenotypic adaptations and performance of plants with a recycled nutrient source is required to identify breeding targets and agronomy practices for new fertilization strategies. In this study, we tested the phenotypic responses of wheat (Triticum aestivum) and its genetic model, Brachypodium (Brachypodium distachyon), to dried algal biomass (with algae or high or low mineral P) under three growing conditions (fabricated ecosystems (EcoFABs), hydroponics, and sand). For both species, algal-grown plants had similar shoot biomass to mineral-grown plants, taking up more P than the low mineral P plants. Root phenotypes however were strongly influenced by nutrient form, especially in soilless conditions. Algae promoted the development of shorter and thicker roots, notably first and second order lateral roots. Root hairs were 21\% shorter in Brachypodium, but 24\% longer in wheat with algae compared to mineral high P. Our results are encouraging to new recycled fertilization strategies, showing algae is a nutrient source to wheat and Brachypodium. Variation in root phenotypes showed algal biomass is sensed by roots and is taken up at a higher amount per root length than mineral P. These phenotypes can be selected and further adapted in phenotype-based breeding for future renewal agriculture systems.},
    doi = {10.3390/agronomy12020285},
    }

  • A. Deja-Muylle, D. Opdenacker, B. Parizot, H. Motte, G. Lobet, V. Storme, P. Clauw, M. Njo, and T. Beeckman, “Genetic Variability of Arabidopsis thaliana Mature Root System Architecture and Genome-Wide Association Study,” Frontiers in Plant Science, vol. 12, 2022.
    [BibTeX]
    @Article{deja2022genetic,
    title = {Genetic Variability of Arabidopsis thaliana Mature Root System Architecture and Genome-Wide Association Study},
    author = {Deja-Muylle, Agnieszka and Opdenacker, Davy and Parizot, Boris and Motte, Hans and Lobet, Guillaume and Storme, Veronique and Clauw, Pieter and Njo, Maria and Beeckman, Tom},
    journal = {Frontiers in Plant Science},
    volume = {12},
    year = {2022},
    }

  • F. Ispizua Yamati, M. Günder, C. Bauckhage, and A. Mahlein, “Sensing the occurrence and dynamics of Cercospora leaf sport disease using UAV-supported image data and deep learning,” Sugar Industry, vol. 147, 2022. doi:10.36961/si28345
    [BibTeX]
    @Article{ispizua2022sugar,
    title = {Sensing the occurrence and dynamics of Cercospora leaf sport disease using UAV-supported image data and deep learning},
    author = {Ispizua Yamati, Facundo and Günder, Maurice and Bauckhage, Christian and Mahlein, Anne-Kathrin},
    journal = {Sugar Industry},
    volume = {147},
    issn = {2},
    pages {79-86},
    year = {2022},
    doi = {10.36961/si28345},
    }

  • B. Mersch, X. Chen, J. Behley, and C. Stachniss, “Self-supervised point cloud prediction using 3d spatio-temporal convolutional networks,” in Conference on Robot Learning , 2022, p. 1444–1454.
    [BibTeX]
    @InProceedings{mersch2022self,
    title = {Self-supervised point cloud prediction using 3d spatio-temporal convolutional networks},
    author = {Mersch, Benedikt and Chen, Xieyuanli and Behley, Jens and Stachniss, Cyrill},
    booktitle = {Conference on Robot Learning},
    pages = {1444--1454},
    year = {2022},
    organization = {PMLR},
    }

  • J. Weyler, F. Magistri, P. Seitz, J. Behley, and C. Stachniss, “In-Field Phenotyping Based on Crop Leaf and Plant Instance Segmentation,” in Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision , 2022, p. 2725–2734.
    [BibTeX]
    @InProceedings{weyler2022field,
    title = {In-Field Phenotyping Based on Crop Leaf and Plant Instance Segmentation},
    author = {Weyler, Jan and Magistri, Federico and Seitz, Peter and Behley, Jens and Stachniss, Cyrill},
    booktitle = {Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision},
    pages = {2725--2734},
    year = {2022},
    }

  • A. K. Srivastava, T. Gaiser, A. S. Akinwumiju, W. Zeng, A. Ceglar, K. S. Ezui, F. Ewert, A. Adelodun, A. Adebayo, J. Sobamowo, and others, “Simulating Regional Cassava Yield Gap in Nigeria,” , 2022.
    [BibTeX]
    @Article{srivastava2022simulating,
    title = {Simulating Regional Cassava Yield Gap in Nigeria},
    author = {Srivastava, Amit Kumar and Gaiser, Thomas and Akinwumiju, Akinola Shola and Zeng, Wenzhi and Ceglar, Andrej and Ezui, Kodjovi Senam and Ewert, Frank and Adelodun, Adedeji and Adebayo, Abass and Sobamowo, Jumoke and others},
    year = {2022},
    }

  • M. Morisse, D. M. Wells, E. J. Millet, M. Lillemo, S. Fahrner, F. Cellini, P. Lootens, O. Muller, J. M. Herrera, A. R. Bentley, and others, “A European perspective on opportunities and demands for field-based crop phenotyping,” Field Crops Research, vol. 276, p. 108371, 2022.
    [BibTeX]
    @Article{morisse2022european,
    title = {A European perspective on opportunities and demands for field-based crop phenotyping},
    author = {Morisse, Merlijn and Wells, Darren M and Millet, Emilie J and Lillemo, Morten and Fahrner, Sven and Cellini, Francesco and Lootens, Peter and Muller, Onno and Herrera, Juan M and Bentley, Alison R and others},
    journal = {Field Crops Research},
    volume = {276},
    pages = {108371},
    year = {2022},
    publisher = {Elsevier},
    }

  • Z. Ballouch, R. Hajji, and M. Ettarid, “Toward a Deep Learning Approach for Automatic Semantic Segmentation of 3D Lidar Point Clouds in Urban Areas,” in Geospatial Intelligence, Springer, 2022, p. 67–77.
    [BibTeX]
    @InCollection{ballouch2022toward,
    title = {Toward a Deep Learning Approach for Automatic Semantic Segmentation of 3D Lidar Point Clouds in Urban Areas},
    author = {Ballouch, Zouhair and Hajji, Rafika and Ettarid, Mohamed},
    booktitle = {Geospatial Intelligence},
    pages = {67--77},
    year = {2022},
    publisher = {Springer},
    }

  • S. Thomas, J. Behmann, U. Rascher, and A. Mahlein, “Evaluation of the benefits of combined reflection and transmission hyperspectral imaging data through disease detection and quantification in plant–pathogen interactions,” Journal of Plant Diseases and Protection, p. 1–16, 2022.
    [BibTeX]
    @Article{thomas2022evaluation,
    title = {Evaluation of the benefits of combined reflection and transmission hyperspectral imaging data through disease detection and quantification in plant--pathogen interactions},
    author = {Thomas, Stefan and Behmann, Jan and Rascher, Uwe and Mahlein, Anne-Katrin},
    journal = {Journal of Plant Diseases and Protection},
    pages = {1--16},
    year = {2022},
    publisher = {Springer},
    }

  • S. Stark, L. Biber-Freudenberger, T. Dietz, N. Escobar, J. J. Förster, J. Henderson, N. Laibach, and J. Börner, “Sustainability implications of transformation pathways for the bioeconomy,” Sustainable Production and Consumption, vol. 29, p. 215–227, 2022.
    [BibTeX]
    @Article{stark2022sustainability,
    title = {Sustainability implications of transformation pathways for the bioeconomy},
    author = {Stark, Sascha and Biber-Freudenberger, Lisa and Dietz, Thomas and Escobar, Neus and F{\"o}rster, Jan Janosch and Henderson, James and Laibach, Natalie and B{\"o}rner, Jan},
    journal = {Sustainable Production and Consumption},
    volume = {29},
    pages = {215--227},
    year = {2022},
    publisher = {Elsevier},
    }

  • M. Günder, F. R. Ispizua Yamati, J. Kierdorf, R. Roscher, A. Mahlein, and C. Bauckhage, “Agricultural Plant Cataloging and Establishment of a Data Framework from UAV-based Crop Images by Computer Vision,” GigaScience, vol. 11, 2022. doi:10.1093/gigascience/giac054
    [BibTeX] [PDF]
    @Article{gunder2022agricultural,
    title = {Agricultural Plant Cataloging and Establishment of a Data Framework from UAV-based Crop Images by Computer Vision},
    author = {G{\"u}nder, Maurice and Ispizua Yamati, Facundo R and Kierdorf, Jana and Roscher, Ribana and Mahlein, Anne-Katrin and Bauckhage, Christian},
    journal = {GigaScience},
    volume = {11},
    year = {2022},
    issn = {2047-217X},
    doi = {10.1093/gigascience/giac054},
    url = {https://doi.org/10.1093/gigascience/giac054},
    }

  • M. Weigand, E. Zimmermann, V. Michels, J. A. Huisman, and A. Kemna, “Design and operation of a long-term monitoring system for spectral electrical impedance tomography (sEIT),” Geoscientific Instrumentation, Methods and Data Systems Discussions, p. 1–35, 2022.
    [BibTeX]
    @Article{weigand2022design,
    title = {Design and operation of a long-term monitoring system for spectral electrical impedance tomography (sEIT)},
    author = {Weigand, Maximilian and Zimmermann, Egon and Michels, Valentin and Huisman, Johan Alexander and Kemna, Andreas},
    journal = {Geoscientific Instrumentation, Methods and Data Systems Discussions},
    pages = {1--35},
    year = {2022},
    publisher = {Copernicus GmbH},
    }

  • E. Cisneros, J. Börner, S. Pagiola, and S. Wunder, “Impacts of conservation incentives in protected areas: The case of Bolsa Floresta, Brazil,” Journal of Environmental Economics and Management, vol. 111, p. 102572, 2022.
    [BibTeX]
    @Article{cisneros2022impacts,
    title = {Impacts of conservation incentives in protected areas: The case of Bolsa Floresta, Brazil},
    author = {Cisneros, El{\'\i}as and B{\"o}rner, Jan and Pagiola, Stefano and Wunder, Sven},
    journal = {Journal of Environmental Economics and Management},
    volume = {111},
    pages = {102572},
    year = {2022},
    publisher = {Elsevier},
    }

  • R. A. Rosu and S. Behnke, “NeuralMVS: Bridging Multi-View Stereo and Novel View Synthesis,” in Proceedings of the International Joint Conference on Neural Networks (IJCNN) 2022 , 2022.
    [BibTeX]
    @inproceedings{rosuneural2022,
    author = {Rosu, Radu Alexandru and Behnke, Sven},
    year = {2022},
    month = {07},
    pages = {},
    title = {NeuralMVS: Bridging Multi-View Stereo and Novel View Synthesis},
    booktitle = {Proceedings of the International Joint Conference on Neural Networks (IJCNN) 2022},
    }

  • J. Kierdorf, I. Weber, A. Kicherer, L. Zabawa, L. Drees, and R. Roscher, “Behind the leaves–Estimation of occluded grapevine berries with conditional generative adversarial networks,” Frontiers in Artificial Intelligence, 2022.
    [BibTeX]
    @Article{kierdorf2022behind,
    title = {Behind the leaves--Estimation of occluded grapevine berries with conditional generative adversarial networks},
    author = {Kierdorf, Jana and Weber, Immanuel and Kicherer, Anna and Zabawa, Laura and Drees, Lukas and Roscher, Ribana},
    journal = {Frontiers in Artificial Intelligence},
    year = {2022},
    }

  • A. Schnepf, A. Carminati, M. Ahmed, M. Ani, P. Benard, J. Bentz, M. Bonkowski, M. Brax, D. Diehl, P. Duddek, and others, “Linking rhizosphere processes across scales: Opinion,” Plant and Soil, 2022.
    [BibTeX]
    @Article{schnepf2021linking,
    title = {Linking rhizosphere processes across scales: Opinion},
    author = {Schnepf, A and Carminati, A and Ahmed, MA and Ani, M and Benard, P and Bentz, J and Bonkowski, M and Brax, M and Diehl, D and Duddek, P and others},
    journal = {Plant and Soil},
    year = {2022},
    }

  • T. LaRue, H. Lindner, A. Srinivas, M. Exposito-Alonso, G. Lobet, and J. R. Dinneny, “Uncovering natural variation in root system architecture and growth dynamics using a robotics-assisted phenomics platform,” eLife, vol. 11, p. e76968, 2022. doi:10.7554/eLife.76968
    [BibTeX] [PDF]

    The plant kingdom contains a stunning array of complex morphologies easily observed above-ground, but more challenging to visualize below-ground. Understanding the magnitude of diversity in root distribution within the soil, termed root system architecture (RSA), is fundamental to determining how this trait contributes to species adaptation in local environments. Roots are the interface between the soil environment and the shoot system and therefore play a key role in anchorage, resource uptake, and stress resilience. Previously, we presented the GLO-Roots (Growth and Luminescence Observatory for Roots) system to study the RSA of soil-grown \textit{Arabidopsis thaliana} plants from germination to maturity (Rellán-Álvarez et al. 2015). In this study, we present the automation of GLO-Roots using robotics and the development of image analysis pipelines in order to examine the temporal dynamic regulation of RSA and the broader natural variation of RSA in Arabidopsis, over time. These datasets describe the developmental dynamics of two independent panels of accessions and reveal highly complex and polygenic RSA traits that show significant correlation with climate variables of the accessions’ respective origins.

    @Article{larue2021uncovering,
    title = {Uncovering natural variation in root system architecture and growth dynamics using a robotics-assisted phenomics platform},
    author = {LaRue, Therese and Lindner, Heike and Srinivas, Ankit and Exposito-Alonso, Moises and Lobet, Guillaume and Dinneny, Jos{\'e} R},
    volume = 11,
    year = 2022,
    pages = {e76968},
    doi = {10.7554/eLife.76968},
    url = {https://doi.org/10.7554/eLife.76968},
    abstract = {The plant kingdom contains a stunning array of complex morphologies easily observed above-ground, but more challenging to visualize below-ground. Understanding the magnitude of diversity in root distribution within the soil, termed root system architecture (RSA), is fundamental to determining how this trait contributes to species adaptation in local environments. Roots are the interface between the soil environment and the shoot system and therefore play a key role in anchorage, resource uptake, and stress resilience. Previously, we presented the GLO-Roots (Growth and Luminescence Observatory for Roots) system to study the RSA of soil-grown \textit{Arabidopsis thaliana} plants from germination to maturity (Rellán-Álvarez et al. 2015). In this study, we present the automation of GLO-Roots using robotics and the development of image analysis pipelines in order to examine the temporal dynamic regulation of RSA and the broader natural variation of RSA in Arabidopsis, over time. These datasets describe the developmental dynamics of two independent panels of accessions and reveal highly complex and polygenic RSA traits that show significant correlation with climate variables of the accessions' respective origins.},
    journal = {eLife},
    issn = {2050-084X},
    publisher = {eLife Sciences Publications, Ltd},
    }

  • J. Weyler, J. Quakernack, P. Lottes, J. Behley, and C. Stachniss, “Joint Plant and Leaf Instance Segmentation on Field-Scale UAV Imagery,” IEEE Robotics and Automation Letters, vol. 7, iss. 2, pp. 3787-3794, 2022. doi:10.1109/LRA.2022.3147462
    [BibTeX]
    @Article{weyler2022ral,
    author = {J. Weyler and J. Quakernack and P. Lottes and J. Behley and C. Stachniss},
    title = {{Joint Plant and Leaf Instance Segmentation on Field-Scale UAV Imagery}},
    journal = {IEEE Robotics and Automation Letters},
    year = 2022,
    doi = {10.1109/LRA.2022.3147462},
    issn = {},
    volume = {7},
    number = {2},
    pages = {3787-3794},
    }

  • L. Nunes, R. Marcuzzi, X. Chen, J. Behley, and C. Stachniss, “SegContrast: 3D Point Cloud Feature Representation Learning through Self-supervised Segment Discrimination,” IEEE Robotics and Automation Letters, 2022. doi:10.1109/LRA.2022.3142440
    [BibTeX]
    @Article{nunes2022ral,
    author = {L. Nunes and R. Marcuzzi and X. Chen and J. Behley and C. Stachniss},
    title = {{SegContrast: 3D Point Cloud Feature Representation Learning through Self-supervised Segment Discrimination}},
    journal = {IEEE Robotics and Automation Letters},
    year = 2022,
    doi = {10.1109/LRA.2022.3142440},
    issn = {2377-3766},
    volume = {},
    number = {},
    pages = {},
    }

  • R. Marcuzzi, L. Nunes, L. Wiesmann, I. Vizzo, J. Behley, and C. Stachniss, “Contrastive Instance Association for 4D Panoptic Segmentation using Sequences of 3D LiDAR Scans,” IEEE Robotics and Automation Letters, vol. 7, iss. 2, pp. 1550-1557, 2022. doi:10.1109/LRA.2022.3140439
    [BibTeX]
    @Article{marcuzzi2022ral,
    author = {R. Marcuzzi and L. Nunes and L. Wiesmann and I. Vizzo and J. Behley and C. Stachniss},
    title = {{Contrastive Instance Association for 4D Panoptic Segmentation using Sequences of 3D LiDAR Scans}},
    journal = {IEEE Robotics and Automation Letters},
    year = 2022,
    doi = {10.1109/LRA.2022.3140439},
    issn = {2377-3766},
    volume = 7,
    number = 2,
    pages = {1550-1557},
    }

2021

  • S. Hao, D. Ryu, A. Western, E. Perry, H. Bogena, and H. J. H. Franssen, “Performance of a wheat yield prediction model and factors influencing the performance: A review and meta-analysis,” Agricultural Systems, vol. 194, p. 103278, 2021. doi:https://doi.org/10.1016/j.agsy.2021.103278
    [BibTeX] [PDF]

    CONTEXT Process-based crop models provide ways to predict crop growth, evaluate environmental impacts on crops, test various crop management options, and guide crop breeding. They can be used to explore options for mitigating climate change impacts when combined with climate projections and explore mitigation of environmental impacts of production. The Agricultural Production Systems SIMulator (APSIM) is a widely adopted crop model that offers modules for simulation of various crops, soil processes, climate, and grazing within a modelling system that enables robust addition of new components. OBJECTIVE This study uses APSIM Classic-Wheat as an example to examine yield prediction accuracy of biophysically based crop yield modelling and to analyse the factors influencing the model performance. METHODS We analysed yield prediction results of APSIM Classic-Wheat from 76 published studies across thirteen countries on four continents. In addition, a meta-database of modelled and observed yields from 30 studies was established and used to identify factors that influence yield prediction uncertainty. RESULTS AND CONCLUSIONS Our analysis indicates that, with site-specific calibration, APSIM predicts yield with a root mean squared error (RMSE) smaller than 1 t/ha and a normalised RMSE (NRMSE) of about 28%, across a wide range of environmental conditions for independent evaluation periods. The results show increasing errors in yield with limited modelling information and adverse environmental conditions. Using soil hydraulic parameters derived from site-specific measurements and/or tuning cultivar parameters improves yield prediction accuracy: RMSE decreases from 1.25 t/ha to 0.64 t/ha and NRMSE from 32% to 14%. Lower model accuracy was found where APSIM overestimates yield under high water deficit condition and when it underestimates yield under nitrogen limitation. APSIM severely over-predicts yield when some abiotic stresses such as heatwaves and frost affect the crop growth. SIGNIFICANCE This paper uses APSIM-Wheat as an example to provide perspectives on crop model yield prediction performance under different conditions covering a wide spectrum of management practices, and environments. The findings deepen the understanding of model uncertainty associated with different calibration processes or under various stressed conditions. The results also indicate the need to improve the model’s predictive skill by filling functional gaps in the wheat simulations and by assimilating external observations (e.g., biomass information estimated by remote sensing) to adjust the model simulation for stressed crops.

    @article{HAO2021103278,
    title = {Performance of a wheat yield prediction model and factors influencing the performance: A review and meta-analysis},
    journal = {Agricultural Systems},
    volume = {194},
    pages = {103278},
    year = {2021},
    issn = {0308-521X},
    doi = {https://doi.org/10.1016/j.agsy.2021.103278},
    url = {https://www.sciencedirect.com/science/article/pii/S0308521X21002316},
    author = {Shirui Hao and Dongryeol Ryu and Andrew Western and Eileen Perry and Heye Bogena and Harrie Jan Hendricks Franssen},
    keywords = {Cropping system, APSIM classic, Wheat, Yield prediction performance, meta-analysis, Literature review},
    abstract = {CONTEXT
    Process-based crop models provide ways to predict crop growth, evaluate environmental impacts on crops, test various crop management options, and guide crop breeding. They can be used to explore options for mitigating climate change impacts when combined with climate projections and explore mitigation of environmental impacts of production. The Agricultural Production Systems SIMulator (APSIM) is a widely adopted crop model that offers modules for simulation of various crops, soil processes, climate, and grazing within a modelling system that enables robust addition of new components.
    OBJECTIVE
    This study uses APSIM Classic-Wheat as an example to examine yield prediction accuracy of biophysically based crop yield modelling and to analyse the factors influencing the model performance.
    METHODS
    We analysed yield prediction results of APSIM Classic-Wheat from 76 published studies across thirteen countries on four continents. In addition, a meta-database of modelled and observed yields from 30 studies was established and used to identify factors that influence yield prediction uncertainty.
    RESULTS AND CONCLUSIONS
    Our analysis indicates that, with site-specific calibration, APSIM predicts yield with a root mean squared error (RMSE) smaller than 1 t/ha and a normalised RMSE (NRMSE) of about 28%, across a wide range of environmental conditions for independent evaluation periods. The results show increasing errors in yield with limited modelling information and adverse environmental conditions. Using soil hydraulic parameters derived from site-specific measurements and/or tuning cultivar parameters improves yield prediction accuracy: RMSE decreases from 1.25 t/ha to 0.64 t/ha and NRMSE from 32% to 14%. Lower model accuracy was found where APSIM overestimates yield under high water deficit condition and when it underestimates yield under nitrogen limitation. APSIM severely over-predicts yield when some abiotic stresses such as heatwaves and frost affect the crop growth.
    SIGNIFICANCE
    This paper uses APSIM-Wheat as an example to provide perspectives on crop model yield prediction performance under different conditions covering a wide spectrum of management practices, and environments. The findings deepen the understanding of model uncertainty associated with different calibration processes or under various stressed conditions. The results also indicate the need to improve the model's predictive skill by filling functional gaps in the wheat simulations and by assimilating external observations (e.g., biomass information estimated by remote sensing) to adjust the model simulation for stressed crops.}
    }

  • K. Baylis, T. Heckelei, and H. Storm, “Chapter 83 – Machine learning in agricultural economics,” in Handbook of Agricultural Economics, C. B. Barrett and D. R. Just, Eds., Elsevier, 2021, vol. 5, pp. 4551-4612. doi:https://doi.org/10.1016/bs.hesagr.2021.10.007
    [BibTeX] [PDF]

    With the substantial growth in novel data sources and computational power, machine learning holds great potential for economic analysis. However, like any new approach, the strengths and weaknesses of these tools need to be considered when deciding where and how they can be successfully applied. In this chapter, we introduce key ML methods, from penalized regressions, to tree-based methods to neural networks, relating these approaches to common econometric practice. We then explore the potential afforded by ML to fill gaps in our current methodological toolbox. We discuss use cases like the need for flexible functional forms, the use of unstructured data, and large numbers of explanatory variables in both prediction and causal analysis. We also highlight the challenges of complex simulation models including calibration, validation and computational demands and identify places where machine learning can help. We highlight these issues drawing from existing examples in agricultural and applied economics. To unpack the black box of ML, we present numerous approaches used in computer science and statistics for model interpretability. Finally, we highlight some ethical issues around the use of ML. We argue that economists can play a vital role in adapting ML methods for the use in economics by combining them with our domain knowledge of economic mechanisms, and our approach to causal identification.

    @InCollection{baylis20214551,
    title = {Chapter 83 - Machine learning in agricultural economics},
    editor = {Christopher B. Barrett and David R. Just},
    series = {Handbook of Agricultural Economics},
    publisher = {Elsevier},
    volume = {5},
    pages = {4551-4612},
    year = {2021},
    booktitle = {Handbook of Agricultural Economics},
    issn = {1574-0072},
    doi = {https://doi.org/10.1016/bs.hesagr.2021.10.007},
    url = {https://www.sciencedirect.com/science/article/pii/S1574007221000074},
    author = {Kathy Baylis and Thomas Heckelei and Hugo Storm},
    keywords = {Machine learning, Agricultural economics, Deep learning, Artificial intelligence, Neural networks, Random forest, Simulation modeling, Causal estimation},
    abstract = {With the substantial growth in novel data sources and computational power, machine learning holds great potential for economic analysis. However, like any new approach, the strengths and weaknesses of these tools need to be considered when deciding where and how they can be successfully applied. In this chapter, we introduce key ML methods, from penalized regressions, to tree-based methods to neural networks, relating these approaches to common econometric practice. We then explore the potential afforded by ML to fill gaps in our current methodological toolbox. We discuss use cases like the need for flexible functional forms, the use of unstructured data, and large numbers of explanatory variables in both prediction and causal analysis. We also highlight the challenges of complex simulation models including calibration, validation and computational demands and identify places where machine learning can help. We highlight these issues drawing from existing examples in agricultural and applied economics. To unpack the black box of ML, we present numerous approaches used in computer science and statistics for model interpretability. Finally, we highlight some ethical issues around the use of ML. We argue that economists can play a vital role in adapting ML methods for the use in economics by combining them with our domain knowledge of economic mechanisms, and our approach to causal identification.},
    }

  • E. Stadtländer, T. Horváth, and S. Wrobel, “Learning weakly convex sets in metric spaces,” in Joint European Conference on Machine Learning and Knowledge Discovery in Databases , 2021, p. 200–216.
    [BibTeX]
    @InProceedings{stadtlander2021learning,
    title = {Learning weakly convex sets in metric spaces},
    author = {Stadtl{\"a}nder, Eike and Horv{\'a}th, Tam{\'a}s and Wrobel, Stefan},
    booktitle = {Joint European Conference on Machine Learning and Knowledge Discovery in Databases},
    pages = {200--216},
    year = {2021},
    organization = {Springer},
    }

  • Z. Akata, A. Geiger, T. Sattler, O. Zatsarynna, J. Sawatzky, and J. Gall, “Discovering Latent Classes for Semi-supervised Semantic Segmentation,” in Proc. of German Conference Pattern Recognition, DAGM GCPR 2020 , 2021, p. 202–217.
    [BibTeX]
    @InProceedings{akata2021discovering,
    title = {Discovering Latent Classes for Semi-supervised Semantic Segmentation},
    author = {Akata, Z and Geiger, A and Sattler, T and Zatsarynna, O and Sawatzky, J and Gall, J},
    booktitle = {Proc. of German Conference Pattern Recognition, DAGM GCPR 2020},
    volume = {12544},
    pages = {202--217},
    year = {2021},
    }

  • S. Stark, J. Rhyner, J. Börner, A. Kopaleyshvili, and S. Middelhauve, “Bioökonomie in Nordrhein-Westfalen,” , 2021.
    [BibTeX]
    @Article{stark2021biookonomie,
    title = {Bio{\"o}konomie in Nordrhein-Westfalen},
    author = {Stark, Sascha and Rhyner, Jakob and B{\"o}rner, Jan and Kopaleyshvili, Alexandra and Middelhauve, Stella},
    year = {2021},
    publisher = {Zentrum f{\"u}r Entwicklungsforschung (ZEF), Rheinische Friedrich-Wilhelms~…},
    }

  • I. Kögel-Knabner and W. Amelung, “Soil organic matter in major pedogenic soil groups,” Geoderma, vol. 384, p. 114785, 2021.
    [BibTeX]
    @Article{kogel2021soil,
    title = {Soil organic matter in major pedogenic soil groups},
    author = {K{\"o}gel-Knabner, Ingrid and Amelung, Wulf},
    journal = {Geoderma},
    volume = {384},
    pages = {114785},
    year = {2021},
    publisher = {Elsevier},
    }

  • A. Mahlein, A. B. A. Alcántara, F. I. R. Yamati, and S. Paulus, “Unlocking the Potential of Hyperspectral Imaging of Plants for Precision Agriculture and Plant Phenotyping,” in Optics and Photonics for Sensing the Environment , 2021, p. EW4G–2.
    [BibTeX]
    @InProceedings{mahlein2021unlocking,
    title = {Unlocking the Potential of Hyperspectral Imaging of Plants for Precision Agriculture and Plant Phenotyping},
    author = {Mahlein, Anne-Katrin and Alc{\'a}ntara, Abel A Barreto and Yamati, Facundo R Ispizua and Paulus, Stefan},
    booktitle = {Optics and Photonics for Sensing the Environment},
    pages = {EW4G--2},
    year = {2021},
    organization = {Optical Society of America},
    }

  • U. Rascher, K. Acebron, J. Bendig, J. Krämer, V. Krieger, J. Quiros-Vargas, B. Siegmann, and O. Muller, “Measuring and Understanding the Dynamics of Solar-Induced Fluorescence (SIF) and its Relation to Photochemical and Non-Photochemical Energy Dissipation-Scaling Leaf Level Regulation to Canopy and Ecosystem Remote Sensing,” in 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS , 2021, p. 203–206.
    [BibTeX]
    @InProceedings{rascher2021measuring,
    title = {Measuring and Understanding the Dynamics of Solar-Induced Fluorescence (SIF) and its Relation to Photochemical and Non-Photochemical Energy Dissipation-Scaling Leaf Level Regulation to Canopy and Ecosystem Remote Sensing},
    author = {Rascher, U and Acebron, K and Bendig, J and Kr{\"a}mer, J and Krieger, V and Quiros-Vargas, J and Siegmann, B and Muller, O},
    booktitle = {2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS},
    pages = {203--206},
    year = {2021},
    organization = {IEEE},
    }

  • S. Li, Y. Liu, and J. Gall, “Rethinking 3-D LiDAR Point Cloud Segmentation,” IEEE Transactions on Neural Networks and Learning Systems, 2021.
    [BibTeX]
    @Article{li2021rethinking,
    title = {Rethinking 3-D LiDAR Point Cloud Segmentation},
    author = {Li, Shijie and Liu, Yun and Gall, Juergen},
    journal = {IEEE Transactions on Neural Networks and Learning Systems},
    year = {2021},
    publisher = {IEEE},
    }

  • M. Günder, N. Piatkowski, L. Von Rueden, R. Sifa, and C. Bauckhage, “Towards Intelligent Food Waste Prevention: An Approach Using Scalable and Flexible Harvest Schedule Optimization With Evolutionary Algorithms,” IEEE Access, vol. 9, p. 169044–169055, 2021.
    [BibTeX]
    @Article{gunder2021towards,
    title = {Towards Intelligent Food Waste Prevention: An Approach Using Scalable and Flexible Harvest Schedule Optimization With Evolutionary Algorithms},
    author = {G{\"u}nder, Maurice and Piatkowski, Nico and Von Rueden, Laura and Sifa, Rafet and Bauckhage, Christian},
    journal = {IEEE Access},
    volume = {9},
    pages = {169044--169055},
    year = {2021},
    publisher = {IEEE},
    }

  • C. Gebauer and M. Bennewitz, “Hierarchical Reinforcement Learning for Sensor-Based Navigation,” arXiv preprint arXiv:2108.13268, 2021.
    [BibTeX]
    @Article{gebauer2021hierarchical,
    title = {Hierarchical Reinforcement Learning for Sensor-Based Navigation},
    author = {Gebauer, Christopher and Bennewitz, Maren},
    journal = {arXiv preprint arXiv:2108.13268},
    year = {2021},
    }

  • R. Neuville, J. S. Bates, and F. Jonard, “Estimating forest structure from UAV-mounted LiDAR point cloud using machine learning,” Remote sensing, vol. 13, iss. 3, p. 352, 2021.
    [BibTeX]
    @Article{neuville2021estimating,
    title = {Estimating forest structure from UAV-mounted LiDAR point cloud using machine learning},
    author = {Neuville, Romain and Bates, Jordan Steven and Jonard, Fran{\c{c}}ois},
    journal = {Remote sensing},
    volume = {13},
    number = {3},
    pages = {352},
    year = {2021},
    publisher = {Multidisciplinary Digital Publishing Institute},
    }

  • C. Gebauer and M. Bennewitz, “The Pitfall of More Powerful Autoencoders in Lidar-Based Navigation,” arXiv preprint arXiv:2102.02127, 2021.
    [BibTeX]
    @Article{gebauer2021pitfall,
    title = {The Pitfall of More Powerful Autoencoders in Lidar-Based Navigation},
    author = {Gebauer, Christopher and Bennewitz, Maren},
    journal = {arXiv preprint arXiv:2102.02127},
    year = {2021},
    }

  • R. A. Rosu, P. Schütt, J. Quenzel, and S. Behnke, “LatticeNet: fast spatio-temporal point cloud segmentation using permutohedral lattices,” Autonomous Robots, p. 1–16, 2021.
    [BibTeX]
    @Article{rosu2021latticenet,
    title = {LatticeNet: fast spatio-temporal point cloud segmentation using permutohedral lattices},
    author = {Rosu, Radu Alexandru and Sch{\"u}tt, Peer and Quenzel, Jan and Behnke, Sven},
    journal = {Autonomous Robots},
    pages = {1--16},
    year = {2021},
    publisher = {Springer},
    }

  • P. Kraft, E. E. Rezaei, L. Breuer, F. Ewert, A. Große-Stoltenberg, T. Kleinebecker, D. Seserman, and C. Nendel, “Modelling Agroforestry’s Contributions to People—A Review of Available Models,” Agronomy, vol. 11, iss. 11, p. 2106, 2021.
    [BibTeX]
    @Article{kraft2021modelling,
    title = {Modelling Agroforestry’s Contributions to People—A Review of Available Models},
    author = {Kraft, Philipp and Rezaei, Ehsan Eyshi and Breuer, Lutz and Ewert, Frank and Gro{\ss}e-Stoltenberg, Andr{\'e} and Kleinebecker, Till and Seserman, Diana-Maria and Nendel, Claas},
    journal = {Agronomy},
    volume = {11},
    number = {11},
    pages = {2106},
    year = {2021},
    publisher = {Multidisciplinary Digital Publishing Institute},
    }

  • G. Lopez, T. Gaiser, F. Ewert, and A. Srivastava, “Effects of Recent Climate Change on Maize Yield in Southwest Ecuador,” Atmosphere, vol. 12, iss. 3, p. 299, 2021.
    [BibTeX]
    @Article{lopez2021effects,
    title = {Effects of Recent Climate Change on Maize Yield in Southwest Ecuador},
    author = {Lopez, Gina and Gaiser, Thomas and Ewert, Frank and Srivastava, Amit},
    journal = {Atmosphere},
    volume = {12},
    number = {3},
    pages = {299},
    year = {2021},
    publisher = {Multidisciplinary Digital Publishing Institute},
    }

  • T. Stella, H. Webber, J. E. Olesen, A. C. Ruane, S. Fronzek, S. Bregaglio, S. Mamidanna, M. Bindi, B. Collins, B. Faye, and others, “Methodology to assess the changing risk of yield failure due to heat and drought stress under climate change,” Environmental Research Letters, vol. 16, iss. 10, p. 104033, 2021.
    [BibTeX]
    @Article{stella2021methodology,
    title = {Methodology to assess the changing risk of yield failure due to heat and drought stress under climate change},
    author = {Stella, Tommaso and Webber, Heidi and Olesen, J{\o}rgen E and Ruane, Alex C and Fronzek, Stefan and Bregaglio, Simone and Mamidanna, Sravya and Bindi, Marco and Collins, Brian and Faye, Babacar and others},
    journal = {Environmental Research Letters},
    volume = {16},
    number = {10},
    pages = {104033},
    year = {2021},
    publisher = {IOP Publishing},
    }

  • S. Li, Y. Zhou, J. Yi, and J. Gall, “Spatial-Temporal Consistency Network for Low-Latency Trajectory Forecasting,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2021, p. 1940–1949.
    [BibTeX]
    @InProceedings{li2021spatial,
    title = {Spatial-Temporal Consistency Network for Low-Latency Trajectory Forecasting},
    author = {Li, Shijie and Zhou, Yanying and Yi, Jinhui and Gall, Juergen},
    booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision},
    pages = {1940--1949},
    year = {2021},
    }

  • M. Halstead, A. Ahmadi, C. Smitt, O. Schmittmann, and C. McCool, “Crop Agnostic Monitoring Driven by Deep Learning,” Frontiers in plant science, vol. 12, 2021.
    [BibTeX]
    @Article{halstead2021crop,
    title = {Crop Agnostic Monitoring Driven by Deep Learning},
    author = {Halstead, Michael and Ahmadi, Alireza and Smitt, Claus and Schmittmann, Oliver and McCool, Chris},
    journal = {Frontiers in plant science},
    volume = {12},
    year = {2021},
    }

  • N. Wilke, B. Siegmann, J. A. Postma, O. Muller, V. Krieger, R. Pude, and U. Rascher, “Assessment of plant density for barley and wheat using UAV multispectral imagery for high-throughput field phenotyping,” Computers and Electronics in Agriculture, vol. 189, p. 106380, 2021.
    [BibTeX]
    @Article{wilke2021assessment,
    title = {Assessment of plant density for barley and wheat using UAV multispectral imagery for high-throughput field phenotyping},
    author = {Wilke, Norman and Siegmann, Bastian and Postma, Johannes A and Muller, Onno and Krieger, Vera and Pude, Ralf and Rascher, Uwe},
    journal = {Computers and Electronics in Agriculture},
    volume = {189},
    pages = {106380},
    year = {2021},
    publisher = {Elsevier},
    }

  • F. He, B. Thiele, D. Kraus, S. Bouteyine, M. Watt, T. Kraska, U. Schurr, and A. J. Kuhn, “Effects of Short-Term Root Cooling before Harvest on Yield and Food Quality of Chinese Broccoli (Brassica oleracea var. Alboglabra Bailey),” Agronomy, vol. 11, iss. 3, p. 577, 2021.
    [BibTeX]
    @Article{he2021effects,
    title = {Effects of Short-Term Root Cooling before Harvest on Yield and Food Quality of Chinese Broccoli (Brassica oleracea var. Alboglabra Bailey)},
    author = {He, Fang and Thiele, Bj{\"o}rn and Kraus, David and Bouteyine, Souhaila and Watt, Michelle and Kraska, Thorsten and Schurr, Ulrich and Kuhn, Arnd J{\"u}rgen},
    journal = {Agronomy},
    volume = {11},
    number = {3},
    pages = {577},
    year = {2021},
    publisher = {Multidisciplinary Digital Publishing Institute},
    }

  • S. De Cannière, M. Herbst, H. Vereecken, P. Defourny, and F. Jonard, “Constraining water limitation of photosynthesis in a crop growth model with sun-induced chlorophyll fluorescence,” Remote Sensing of Environment, vol. 267, p. 112722, 2021.
    [BibTeX]
    @Article{de2021constraining,
    title = {Constraining water limitation of photosynthesis in a crop growth model with sun-induced chlorophyll fluorescence},
    author = {De Canni{\`e}re, S and Herbst, M and Vereecken, H and Defourny, P and Jonard, Fran{\c{c}}ois},
    journal = {Remote Sensing of Environment},
    volume = {267},
    pages = {112722},
    year = {2021},
    publisher = {Elsevier},
    }

  • Y. Huang, J. Weis, H. Vereecken, and H. Hendricks Franssen, “Long-term trends in agricultural droughts over Netherlands and Germany: how extreme was the year 2018?,” Hydrology and Earth System Sciences Discussions, p. 1–27, 2021.
    [BibTeX]
    @Article{huang2021long,
    title = {Long-term trends in agricultural droughts over Netherlands and Germany: how extreme was the year 2018?},
    author = {Huang, Yafei and Weis, Jonas and Vereecken, Harry and Hendricks Franssen, Harrie-Jan},
    journal = {Hydrology and Earth System Sciences Discussions},
    pages = {1--27},
    year = {2021},
    publisher = {Copernicus GmbH},
    }

  • M. Landl, A. Haupenthal, D. Leitner, E. Kroener, D. Vetterlein, R. Bol, H. Vereecken, J. Vanderborght, and A. Schnepf, “Simulating rhizodeposition patterns around growing and exuding root systems,” bioRxiv, 2021.
    [BibTeX]
    @Article{landl2021simulating,
    title = {Simulating rhizodeposition patterns around growing and exuding root systems},
    author = {Landl, Magdalena and Haupenthal, Adrian and Leitner, Daniel and Kroener, Eva and Vetterlein, Doris and Bol, Roland and Vereecken, Harry and Vanderborght, Jan and Schnepf, Andrea},
    journal = {bioRxiv},
    year = {2021},
    publisher = {Cold Spring Harbor Laboratory},
    }

  • S. Hao, D. Ryu, A. Western, E. Perry, H. Bogena, and H. J. H. Franssen, “Performance of a wheat yield prediction model and factors influencing the performance: A review and meta-analysis,” Agricultural Systems, vol. 194, p. 103278, 2021.
    [BibTeX]
    @Article{hao2021performance,
    title = {Performance of a wheat yield prediction model and factors influencing the performance: A review and meta-analysis},
    author = {Hao, Shirui and Ryu, Dongryeol and Western, Andrew and Perry, Eileen and Bogena, Heye and Franssen, Harrie Jan Hendricks},
    journal = {Agricultural Systems},
    volume = {194},
    pages = {103278},
    year = {2021},
    publisher = {Elsevier},
    }

  • E. Blagodatskaya, M. Tarkka, C. Knief, R. Koller, S. Peth, V. Schmidt, S. Spielvogel, D. Uteau, M. Weber, and B. S. Razavi, “Bridging Microbial Functional Traits With Localized Process Rates at Soil Interfaces,” Frontiers in microbiology, vol. 12, 2021.
    [BibTeX]
    @Article{blagodatskaya2021bridging,
    title = {Bridging Microbial Functional Traits With Localized Process Rates at Soil Interfaces},
    author = {Blagodatskaya, Evgenia and Tarkka, Mika and Knief, Claudia and Koller, Robert and Peth, Stephan and Schmidt, Volker and Spielvogel, Sandra and Uteau, Daniel and Weber, Matthias and Razavi, Bahar S},
    journal = {Frontiers in microbiology},
    volume = {12},
    year = {2021},
    publisher = {Frontiers Media SA},
    }

  • C. Pahmeyer, T. Kuhn, and W. Britz, “Single plots or shares of land-How modeling of crop choices in bio-economic farm models influences simulation results,” 2021.
    [BibTeX]
    @TechReport{pahmeyer2021single,
    title = {Single plots or shares of land-How modeling of crop choices in bio-economic farm models influences simulation results},
    author = {Pahmeyer, Christoph and Kuhn, Till and Britz, Wolfgang},
    year = {2021},
    }

  • Z. Zhou, Z. Zhang, A. S. Mason, L. Chen, C. Liu, M. Qin, W. Li, B. Tian, Z. Wu, Z. Lei, and others, “Quantitative traits loci mapping and molecular marker development for total glutenin and glutenin fraction contents in wheat,” BMC plant biology, vol. 21, iss. 1, p. 1–13, 2021.
    [BibTeX]
    @Article{zhou2021quantitative,
    title = {Quantitative traits loci mapping and molecular marker development for total glutenin and glutenin fraction contents in wheat},
    author = {Zhou, Zhengfu and Zhang, Ziwei and Mason, Annaliese S and Chen, Lingzhi and Liu, Congcong and Qin, Maomao and Li, Wenxu and Tian, Baoming and Wu, Zhengqing and Lei, Zhensheng and others},
    journal = {BMC plant biology},
    volume = {21},
    number = {1},
    pages = {1--13},
    year = {2021},
    publisher = {BioMed Central},
    }

  • E. I. Katche, A. Schierholt, S. V. Schiessl, Z. Lv, J. Batley, H. C. Becker, and A. S. Mason, “Genetic factors inherited from both diploid parents interact to affect genome stability and fertility in resynthesized allotetraploid B. napus,” , 2021.
    [BibTeX]
    @Article{katche2021genetic,
    title = {Genetic factors inherited from both diploid parents interact to affect genome stability and fertility in resynthesized allotetraploid B. napus},
    author = {Katche, Elizabeth Ihien and Schierholt, Antje and Schiessl, Sarah V and Lv, Zhenling and Batley, Jacqueline and Becker, Heiko C and Mason, Annaliese S},
    year = {2021},
    }

  • W. Yang, P. Gutbrod, K. Gutbrod, H. Peisker, X. Song, A. Falz, A. J. Meyer, and P. Dörmann, “2-Hydroxy-phytanoyl-CoA lyase (AtHPCL) is involved in phytol metabolism in Arabidopsis,” The Plant Journal, 2021.
    [BibTeX]
    @Article{yang20212,
    title = {2-Hydroxy-phytanoyl-CoA lyase (AtHPCL) is involved in phytol metabolism in Arabidopsis},
    author = {Yang, Wentao and Gutbrod, Philipp and Gutbrod, Katharina and Peisker, Helga and Song, Xiaoning and Falz, Anna-Lena and Meyer, Andreas J and D{\"o}rmann, Peter},
    journal = {The Plant Journal},
    year = {2021},
    publisher = {Wiley Online Library},
    }

  • M. Schneider, M. Barbosa, A. Ballvora, and J. Leon, “Organic farming-Deep genotyping reveals specific selection footprints in barley populations,” , 2021.
    [BibTeX]
    @Article{schneider2021organic,
    title = {Organic farming-Deep genotyping reveals specific selection footprints in barley populations},
    author = {Schneider, Michael and Barbosa, Marissa and Ballvora, Agim and Leon, Jens},
    year = {2021},
    }

  • J. Schielein, G. P. Frey, J. Miranda, R. A. de Souza, J. Börner, and J. Henderson, “The role of accessibility for land use and land cover change in the Brazilian Amazon,” Applied Geography, vol. 132, p. 102419, 2021.
    [BibTeX]
    @Article{schielein2021role,
    title = {The role of accessibility for land use and land cover change in the Brazilian Amazon},
    author = {Schielein, Johannes and Frey, Gabriel Ponzoni and Miranda, Javier and de Souza, Rodrigo Ant{\^o}nio and Börner, Jan and Henderson, James},
    journal = {Applied Geography},
    volume = {132},
    pages = {102419},
    year = {2021},
    publisher = {Elsevier},
    }

  • R. Giudice and J. Börner, “Benefits and costs of incentive-based forest conservation in the Peruvian Amazon,” Forest Policy and Economics, vol. 131, p. 102559, 2021.
    [BibTeX]
    @Article{giudice2021benefits,
    title = {Benefits and costs of incentive-based forest conservation in the Peruvian Amazon},
    author = {Giudice, Renzo and B{\"o}rner, Jan},
    journal = {Forest Policy and Economics},
    volume = {131},
    pages = {102559},
    year = {2021},
    publisher = {Elsevier},
    }

  • L. Peruzzo, X. Liu, C. Chou, E. B. Blancaflor, H. Zhao, X. Ma, B. Mary, V. Iván, M. Weigand, and Y. Wu, “Three-channel electrical impedance spectroscopy for field-scale root phenotyping,” The Plant Phenome Journal, vol. 4, iss. 1, p. e20021, 2021.
    [BibTeX]
    @Article{peruzzo2021three,
    title = {Three-channel electrical impedance spectroscopy for field-scale root phenotyping},
    author = {Peruzzo, Luca and Liu, Xiuwei and Chou, Chunwei and Blancaflor, Elison B and Zhao, Haijun and Ma, Xue-Feng and Mary, Benjamin and Iv{\'a}n, Veronika and Weigand, Maximilian and Wu, Yuxin},
    journal = {The Plant Phenome Journal},
    volume = {4},
    number = {1},
    pages = {e20021},
    year = {2021},
    publisher = {Wiley Online Library},
    }

  • R. Žydelis, L. Weihermüller, and M. Herbst, “Future climate change will accelerate maize phenological development and increase yield in the Nemoral climate,” Science of The Total Environment, vol. 784, p. 147175, 2021.
    [BibTeX]
    @Article{vzydelis2021future,
    title = {Future climate change will accelerate maize phenological development and increase yield in the Nemoral climate},
    author = {{\v{Z}}ydelis, R and Weiherm{\"u}ller, L and Herbst, Michael},
    journal = {Science of The Total Environment},
    volume = {784},
    pages = {147175},
    year = {2021},
    publisher = {Elsevier},
    }

  • M. Habib-ur-Rahman, A. Raza, H. E. Ahrends, H. Hüging, and T. Gaiser, “Impact of in-field soil heterogeneity on biomass and yield of winter triticale in an intensively cropped hummocky landscape under temperate climate conditions,” Precision agriculture, p. 1–27, 2021.
    [BibTeX]
    @Article{habib2021impact,
    title = {Impact of in-field soil heterogeneity on biomass and yield of winter triticale in an intensively cropped hummocky landscape under temperate climate conditions},
    author = {Habib-ur-Rahman, Muhammad and Raza, Ahsan and Ahrends, Hella Ellen and H{\"u}ging, Hubert and Gaiser, Thomas},
    journal = {Precision agriculture},
    pages = {1--27},
    year = {2021},
    publisher = {Springer},
    }

  • F. Navarrete, M. Gallei, A. E. Kornienko, I. Saado, M. Khan, K. Chia, M. A. Darino, J. Bindics, and A. Djamei, “TOPLESS promotes plant immunity by repressing auxin signaling and is targeted by the fungal effector Naked1,” Plant Communications, p. 100269, 2021.
    [BibTeX]
    @Article{navarrete2021topless,
    title = {TOPLESS promotes plant immunity by repressing auxin signaling and is targeted by the fungal effector Naked1},
    author = {Navarrete, Fernando and Gallei, Michelle and Kornienko, Aleksandra E and Saado, Indira and Khan, Mamoona and Chia, Khong-Sam and Darino, Martin A and Bindics, Janos and Djamei, Armin},
    journal = {Plant Communications},
    pages = {100269},
    year = {2021},
    publisher = {Elsevier},
    }

  • J. Krämer, B. Siegmann, T. Kraska, O. Muller, and U. Rascher, “The potential of spatial aggregation to extract remotely sensed sun-induced fluorescence (SIF) of small-sized experimental plots for applications in crop phenotyping,” International Journal of Applied Earth Observation and Geoinformation, vol. 104, p. 102565, 2021. doi:https://doi.org/10.1016/j.jag.2021.102565
    [BibTeX] [PDF]
    @Article{kramer2021102565,
    title = {The potential of spatial aggregation to extract remotely sensed sun-induced fluorescence (SIF) of small-sized experimental plots for applications in crop phenotyping},
    journal = {International Journal of Applied Earth Observation and Geoinformation},
    volume = {104},
    pages = {102565},
    year = {2021},
    issn = {0303-2434},
    doi = {https://doi.org/10.1016/j.jag.2021.102565},
    url = {https://www.sciencedirect.com/science/article/pii/S0303243421002725},
    author = {Julie Krämer and Bastian Siegmann and Thorsten Kraska and Onno Muller and Uwe Rascher},
    keywords = {Sun-induced chlorophyll fluorescence, SIF, Airborne remote sensing, Spatial aggregation, Outlier detection, Hampel identifier, Field phenotyping},
    }

  • H. Gulabani, K. Goswami, Y. Walia, A. Roy, J. J. Noor, K. D. Ingole, M. Kasera, D. Laha, R. F. H. Giehl, G. Schaaf, and S. Bhattacharjee, “TArabidopsis inositol polyphosphate kinases IPK1 and ITPK1 modulate crosstalk between SA-dependent immunity and phosphate-starvation responses,” Plant Cell Reports, 2021. doi:https://doi.org/10.1007/s00299-021-02812-3
    [BibTeX] [PDF]
    @Article{gulabani2021,
    title = {TArabidopsis inositol polyphosphate kinases IPK1 and ITPK1 modulate crosstalk between SA-dependent immunity and phosphate-starvation responses},
    journal = {Plant Cell Reports},
    year = {2021},
    doi = {https://doi.org/10.1007/s00299-021-02812-3},
    url = {https://link.springer.com/article/10.1007/s00299-021-02812-3},
    author = {Gulabani, Hitika AND Goswami, Krishnendu AND Walia, Yashika And Roy, Abhisha AND Noor, Jewel Jameeta AND Ingole, Kishor D. AND Kasera, Mritunjay AND Laha, Debabrata AND Giehl, Ricardo F. H. AND Schaaf, Gabriel AND Bhattacharjee, Saikat},
    }

  • B. Schmitz, H. Kuhlmann, and C. Holst, “Towards the empirical determination of correlations in terrestrial laser scanner range observations and the comparison of the correlation structure of different scanners,” ISPRS Journal of Photogrammetry and Remote Sensing, vol. 182, pp. 228-241, 2021. doi:https://doi.org/10.1016/j.isprsjprs.2021.10.012
    [BibTeX] [PDF]
    @Article{schmitz2021228,
    title = {Towards the empirical determination of correlations in terrestrial laser scanner range observations and the comparison of the correlation structure of different scanners},
    journal = {ISPRS Journal of Photogrammetry and Remote Sensing},
    volume = {182},
    pages = {228-241},
    year = {2021},
    issn = {0924-2716},
    doi = {https://doi.org/10.1016/j.isprsjprs.2021.10.012},
    url = {https://www.sciencedirect.com/science/article/pii/S092427162100280X},
    author = {B. Schmitz and H. Kuhlmann and C. Holst},
    keywords = {Variance-covariance matrix, Anisotropy, Point cloud, Autocovariance, Stochastic model, Terrestrial laser scanning},
    }

  • Y. Zeng, D. Hao, G. Badgley, A. Damm, U. Rascher, Y. Ryu, J. Johnson, V. Krieger, S. Wu, H. Qiu, Y. Liu, J. A. Berry, and M. Chen, “Estimating near-infrared reflectance of vegetation from hyperspectral data,” Remote Sensing of Environment, vol. 267, p. 112723, 2021. doi:https://doi.org/10.1016/j.rse.2021.112723
    [BibTeX] [PDF]
    @Article{zeng2021112723,
    title = {Estimating near-infrared reflectance of vegetation from hyperspectral data},
    journal = {Remote Sensing of Environment},
    volume = {267},
    pages = {112723},
    year = {2021},
    issn = {0034-4257},
    doi = {https://doi.org/10.1016/j.rse.2021.112723},
    url = {https://www.sciencedirect.com/science/article/pii/S0034425721004430},
    author = {Yelu Zeng and Dalei Hao and Grayson Badgley and Alexander Damm and Uwe Rascher and Youngryel Ryu and Jennifer Johnson and Vera Krieger and Shengbiao Wu and Han Qiu and Yaling Liu and Joseph A. Berry and Min Chen},
    keywords = {Solar-induced chlorophyll fluorescence (SIF), Hyperspectral remote sensing, Soil contamination, Near-infrared reflectance of vegetation (NIRv), Singular value decomposition (SVD), Red edge},
    }

  • T. Hertel, I. Elouafi, M. Tanticharoen, and F. Ewert, “Diversification for enhanced food systems resilience,” Nature Food, vol. 2, pp. 832-834, 2021. doi:https://doi.org/10.1038/s43016-021-00403-9
    [BibTeX] [PDF]
    @Article{hertel2021,
    title = {Diversification for enhanced food systems resilience},
    journal = {Nature Food},
    volume = {2},
    pages = {832-834},
    year = {2021},
    issn = {2662-1355},
    doi = {https://doi.org/10.1038/s43016-021-00403-9},
    url = {https://www.nature.com/articles/s43016-021-00403-9.pdf},
    author = {Hertel, Thomas AND Elouafi, Ismahane AND Tanticharoen, Morakot AND Ewert, Frank},
    keywords = {At the field, farm, household and market levels, multiple options exist for diversification of activities, building resilience of food systems to stresses and shocks},
    }

  • D. L. Giammarino, I. Aloise, C. Stachniss, and G. Grisetti, “Visual Place Recognition using LiDAR Intensity Information,” in IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS) , 2021.
    [BibTeX] [PDF]
    @InProceedings{digiammarino2021iros,
    author = {Giammarino, D. L. AND Aloise, I. AND Stachniss, C. AND Grisetti, G.},
    title = {{Visual Place Recognition using LiDAR Intensity Information}},
    booktitle = {IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS)},
    year = {2021},
    url = {https://www.ipb.uni-bonn.de/wp-content/papercite-data/pdf/digiammarino2021iros.pdf},
    }

  • P. Rottmann, T. Posewsky, A. Milioto, C. Stachniss, and J. Behley, “Improving Monocular Depth Estimation by Semantic Pre-training,” in IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS) , 2021.
    [BibTeX] [PDF]
    @InProceedings{rottmann2021iros,
    author = {P. Rottmann AND T. Posewsky AND A. Milioto AND C. Stachniss AND J. Behley},
    title = {{Improving Monocular Depth Estimation by Semantic Pre-training}},
    booktitle = {IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS)},
    year = {2021},
    url = {https://www.ipb.uni-bonn.de/wp-content/papercite-data/pdf/rottmann2021iros.pdf},
    }

  • B. Mersch, T. Höllen, K. Zhao, S. C., and R. Roscher, “Maneuver-based Trajectory Prediction for Self-driving Cars Using Spatio-temporal Convolutional Networks,” in IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS) , 2021.
    [BibTeX] [PDF]
    @InProceedings{mersch2021iros,
    author = {Mersch, B. AND Höllen, T. AND Zhao, K. AND Stachniss C. AND Roscher, R.},
    title = {{Maneuver-based Trajectory Prediction for Self-driving Cars Using Spatio-temporal Convolutional Networks}},
    booktitle = {IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS)},
    year = {2021},
    url = {https://www.ipb.uni-bonn.de/wp-content/papercite-data/pdf/mersch2021iros.pdf},
    }

  • F. Stache, J. Westheider, F. Magistri, M. Popović, and C. Stachniss, “Adaptive Path Planning for UAV-based Multi-Resolution Semantic Segmentation,” in European Conference on Mobile Robots (ECMR) , 2021.
    [BibTeX] [PDF]
    @InProceedings{stache2021ecmr,
    author = {Stache, F. AND Westheider, J. AND Magistri, F. AND Popović, M. AND Stachniss, C.},
    title = {{Adaptive Path Planning for UAV-based Multi-Resolution Semantic Segmentation}},
    booktitle = {European Conference on Mobile Robots (ECMR)},
    year = {2021},
    url = {https://www.ipb.uni-bonn.de/wp-content/papercite-data/pdf/stache2021ecmr.pdf},
    }

  • M. Arora, L. Wiesmann, X. Chen, and C. Stachniss, “Static Map Construction for 3D LiDAR Point Clouds exploiting Ground Segmentation,” in European Conference on Mobile Robots (ECMR) , 2021.
    [BibTeX] [PDF]
    @InProceedings{arora2021ecmr,
    author = {Arora, M. AND Wiesmann, L. AND Chen, X. AND Stachniss, C. },
    title = {{Static Map Construction for 3D LiDAR Point Clouds exploiting Ground Segmentation}},
    booktitle = {European Conference on Mobile Robots (ECMR)},
    year = {2021},
    url = {https://www.ipb.uni-bonn.de/wp-content/papercite-data/pdf/arora2021ecmr.pdf},
    }

  • R. A. Rosu and S. Behnke, “EasyPBR: A Lightweight Physically-Based Renderer,” in Proceedings of 16th International Conference on Computer Graphics Theory and Applications , 2021.
    [BibTeX] [PDF]
    @InProceedings{rosu2021grapp,
    author = {Rosu, Radu Alexandru AND Behnke, Sven},
    title = {{EasyPBR: A Lightweight Physically-Based Renderer}},
    booktitle = {Proceedings of 16th International Conference on Computer Graphics Theory and Applications},
    year = {2021},
    url = {https://www.ais.uni-bonn.de/papers/GRAPP_2021_Rosu_EasyPBR.pdf},
    }

  • M. Herbst, P. Pohlig, A. Graf, L. Weihermüller, M. Schmidt, J. Vanderborght, and H. Vereecken, “Quantification of water stress induced within-field variability of carbon dioxide fluxes in a sugar beet stand,” Agricultural and Forest Meteorology, vol. 297, p. 108242, 2021. doi:https://doi.org/10.1016/j.agrformet.2020.108242
    [BibTeX] [PDF]

    Net ecosystem exchange of carbon dioxide (NEE) and soil respiration at field scale can exhibit considerable spatial variability linked to the heterogeneity of soil properties and state variables. In this study, we measured NEE with the eddy covariance (EC) method in a sugar beet field characterized by high spatial variability in soil physical properties. We further measured NEE and soil respiration by chambers as well as soil water content and temperature at 18 locations within the field. Spatially averaged chamber-measured NEE showed good agreement to the EC-based data. During a dry period high spatial variation of within-field NEE was detected with the chamber method. The coefficient of variation was on average 0.57 during the dry period, with a maximum of 0.72. Based on the depth-specific soil water content measurements the AgroC ecosystem model was inverted for soil hydraulic properties at each of the 18 locations, where soil water content was measured. Analyzing the model results revealed that root water uptake stress was the main driver of spatial and temporal variability in crop development and NEE, whereby the soil coarse material fraction (gravel content) and thickness of the layer above a gravel dominated soil layer were identified as the main influencing soil properties. The chamber-measured NEE and the flux footprint analysis showed that particularly during periods of severe root water uptake stress EC-based measurements would be prone to biases. A combination of the footprint model with the AgroC ecosystem model estimated a bias of 14\% for the dry period and a vegetation period bias of 6\% in relation to the average CO2 flux.

    @Article{herbst2021108242,
    title = {Quantification of water stress induced within-field variability of carbon dioxide fluxes in a sugar beet stand},
    journal = {Agricultural and Forest Meteorology},
    volume = {297},
    pages = {108242},
    year = {2021},
    issn = {0168-1923},
    doi = {https://doi.org/10.1016/j.agrformet.2020.108242},
    url = {https://www.sciencedirect.com/science/article/pii/S0168192320303440},
    author = {M. Herbst and P. Pohlig and A. Graf and L. Weihermüller and M. Schmidt and J. Vanderborght and H. Vereecken},
    keywords = {Spatial variation, Net ecosystem exchange, Respiration, Eddy covariance, Crop model, Water stress},
    abstract = {Net ecosystem exchange of carbon dioxide (NEE) and soil respiration at field scale can exhibit considerable spatial variability linked to the heterogeneity of soil properties and state variables. In this study, we measured NEE with the eddy covariance (EC) method in a sugar beet field characterized by high spatial variability in soil physical properties. We further measured NEE and soil respiration by chambers as well as soil water content and temperature at 18 locations within the field. Spatially averaged chamber-measured NEE showed good agreement to the EC-based data. During a dry period high spatial variation of within-field NEE was detected with the chamber method. The coefficient of variation was on average 0.57 during the dry period, with a maximum of 0.72. Based on the depth-specific soil water content measurements the AgroC ecosystem model was inverted for soil hydraulic properties at each of the 18 locations, where soil water content was measured. Analyzing the model results revealed that root water uptake stress was the main driver of spatial and temporal variability in crop development and NEE, whereby the soil coarse material fraction (gravel content) and thickness of the layer above a gravel dominated soil layer were identified as the main influencing soil properties. The chamber-measured NEE and the flux footprint analysis showed that particularly during periods of severe root water uptake stress EC-based measurements would be prone to biases. A combination of the footprint model with the AgroC ecosystem model estimated a bias of 14\% for the dry period and a vegetation period bias of 6\% in relation to the average CO2 flux.},
    }

  • A. Dreier, F. Zimmermann, L. Klingbeil, C. Holst, and H. Kuhlmann, “Strategien zur Selektion von Satelliten in kinematischen GNSS-Anwendungen auf Basis von 3D-Umgebungsmodellen,” Allgemeine Vermessungs-Nachrichten (AVN), 2021.
    [BibTeX] [PDF]
    @Article{dreier2021avn,
    author = {Dreier, A. AND Zimmermann, F. AND Klingbeil, L. AND Holst, C. AND Kuhlmann, H.},
    title = {{Strategien zur Selektion von Satelliten in kinematischen GNSS-Anwendungen auf Basis von 3D-Umgebungsmodellen}},
    journal = {Allgemeine Vermessungs-Nachrichten (AVN)},
    year = {2021},
    url = {https://gispoint.de/artikelarchiv/avn/2021/avn-ausgabe-012021/6841-strategien-zur-selektion-von-satelliten-in-kinematischen-gnss-anwendungen-auf-basis-von-3d-umgebungsmodellen.html},
    }

  • K. Baylis, T. Heckelei, and T. W. Hertel, “Agricultural Trade and Environmental Sustainability,” Annual Review of Resource Economics, vol. 13, iss. 1, pp. 379-401, 2021. doi:10.1146/annurev-resource-101420-090453
    [BibTeX] [PDF]
    @Article{doi:10.1146/annurev-resource-101420-090453,
    author = {Baylis, Kathy and Heckelei, Thomas and Hertel, Thomas W.},
    title = {Agricultural Trade and Environmental Sustainability},
    journal = {Annual Review of Resource Economics},
    volume = {13},
    number = {1},
    pages = {379-401},
    year = {2021},
    doi = {10.1146/annurev-resource-101420-090453},
    url = { https://doi.org/10.1146/annurev-resource-101420-090453
    },
    eprint = { https://doi.org/10.1146/annurev-resource-101420-090453},
    }

  • J. Krause, M. Günder, D. Schulz, and R. Gruna, “New active learning algorithms for near-infrared spectroscopy in agricultural applications,” at – Automatisierungstechnik, vol. 69, iss. 4, p. 297–306, 2021. doi:doi:10.1515/auto-2020-0143
    [BibTeX] [PDF]
    @Article{krausegünderschulzgruna+2021+297+306,
    author = {Julius Krause and Maurice Günder and Daniel Schulz and Robin Gruna},
    doi = {doi:10.1515/auto-2020-0143},
    url = {https://doi.org/10.1515/auto-2020-0143},
    title = {New active learning algorithms for near-infrared spectroscopy in agricultural applications},
    journal = {at - Automatisierungstechnik},
    number = {4},
    volume = {69},
    year = {2021},
    pages = {297--306},
    }

  • V. Sushko, E. Schönfeld, D. Zhang, J. Gall, B. Schiele, and A. Khoreva, “You Only Need Adversarial Supervision for Semantic Image Synthesis,” in International Conference on Learning Representations (ICLR) , 2021.
    [BibTeX] [PDF]
    @InProceedings{sushko2021iclr,
    author = {Sushko,V. AND Schönfeld, E. AND Zhang, D. AND Gall, J. AND Schiele, B. AND Khoreva, A.},
    title = {{You Only Need Adversarial Supervision for Semantic Image Synthesis}},
    booktitle = {International Conference on Learning Representations (ICLR)},
    year = {2021},
    url = {https://openreview.net/pdf?id=yvQKLaqNE6M},
    }

  • J. Vanderborght, V. Couvreur, F. Meunier, A. Schnepf, H. Vereecken, M. Bouda, and M. Javaux, “From hydraulic root architecture models to macroscopic representations of root hydraulics in soil water flow and land surface models,” Hydrology and Earth System Sciences, vol. 25, iss. 9, p. 4835–4860, 2021. doi:10.5194/hess-25-4835-2021
    [BibTeX] [PDF]
    @Article{hess-25-4835-2021,
    author = {Vanderborght, J. and Couvreur, V. and Meunier, F. and Schnepf, A. and Vereecken, H. and Bouda, M. and Javaux, M.},
    title = {From hydraulic root architecture models to macroscopic representations of root hydraulics in soil water flow and land surface models},
    journal = {Hydrology and Earth System Sciences},
    volume = {25},
    year = {2021},
    number = {9},
    pages = {4835--4860},
    url = {https://hess.copernicus.org/articles/25/4835/2021/},
    doi = {10.5194/hess-25-4835-2021},
    }

  • C. Brogi, J. A. Huisman, L. Weihermüller, M. Herbst, and H. Vereecken, “Added value of geophysics-based soil mapping in agro-ecosystem simulations,” SOIL, vol. 7, iss. 1, p. 125–143, 2021. doi:10.5194/soil-7-125-2021
    [BibTeX] [PDF]
    @Article{soil-7-125-2021,
    author = {Brogi, C. and Huisman, J. A. and Weiherm\"uller, L. and Herbst, M. and Vereecken, H.},
    title = {Added value of geophysics-based soil mapping in agro-ecosystem simulations},
    journal = {SOIL},
    volume = {7},
    year = {2021},
    number = {1},
    pages = {125--143},
    url = {https://soil.copernicus.org/articles/7/125/2021/},
    doi = {10.5194/soil-7-125-2021},
    }

  • S. Li, J. Yi, Y. Abu Farha, and J. Gall, “Pose Refinement Graph Convolutional Network for Skeleton-based Action Recognition,” IEEE Robotics and Automation Letters, 2021.
    [BibTeX] [PDF]
    @Article{li2021ieee,
    author = {Li, S. AND Yi, J. AND Abu Farha, Y. AND Gall, J.},
    title = {{Pose Refinement Graph Convolutional Network for Skeleton-based Action Recognition}},
    journal = {IEEE Robotics and Automation Letters},
    year = {2021},
    url = {https://arxiv.org/pdf/2010.07367.pdf},
    }

  • X. Chen, S. Li, B. Mersch, L. Wiesmann, J. Gall, J. Behley, and C. Stachniss, “Moving Object Segmentation in 3D LiDAR Data: A Learning-based Approach Exploiting Sequential Data,” IEEE Robotics and Automation Letters, 2021.
    [BibTeX] [PDF]
    @Article{chen2021ieee,
    author = {Chen, X. AND Li, S. AND Mersch, B. AND Wiesmann, L. AND Gall, J. AND Behley, J. AND Stachniss, C.},
    title = {{Moving Object Segmentation in 3D LiDAR Data: A Learning-based Approach Exploiting Sequential Data}},
    journal = {IEEE Robotics and Automation Letters},
    year = {2021},
    url = {https://arxiv.org/pdf/2105.08971.pdf},
    }

  • T. Stomberg, I. Weber, M. Schmitt, and R. Roscher, “JUNGLE-NET: USING EXPLAINABLE MACHINE LEARNING TO GAIN NEW INSIGHTS INTO THE APPEARANCE OF WILDERNESS IN SATELLITE IMAGERY,” ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol. V-3-2021, p. 317–324, 2021. doi:10.5194/isprs-annals-V-3-2021-317-2021
    [BibTeX] [PDF]
    @Article{isprs-annals-v-3-2021-317-2021,
    author = {Stomberg, T. and Weber, I. and Schmitt, M. and Roscher, R.},
    title = {JUNGLE-NET: USING EXPLAINABLE MACHINE LEARNING TO GAIN NEW INSIGHTS INTO THE APPEARANCE OF WILDERNESS IN SATELLITE IMAGERY},
    journal = {ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences},
    volume = {V-3-2021},
    year = {2021},
    pages = {317--324},
    url = {https://www.isprs-ann-photogramm-remote-sens-spatial-inf-sci.net/V-3-2021/317/2021/},
    doi = {10.5194/isprs-annals-V-3-2021-317-2021},
    }

  • X. Chen, T. Läbe, A. Milioto, T. Röhling, J. Behley, and C. Stachniss, “OverlapNet: A Siamese Network for Computing LiDAR Scan Similarity with Applications to Loop Closing and Localization,” Autonomous Robots, 2021. doi:10.1007/s10514-021-09999-0
    [BibTeX] [PDF]
    @Article{chen2021autro,
    author = {Chen, X. AND Läbe, T. AND Milioto, A. AND Röhling, T. AND Behley, J. AND Stachniss, C.},
    title = {{OverlapNet: A Siamese Network for Computing LiDAR Scan Similarity with Applications to Loop Closing and Localization}},
    journal = {Autonomous Robots},
    year = {2021},
    doi = {10.1007/s10514-021-09999-0},
    url = {https://link.springer.com/article/10.1007%2Fs10514-021-09999-0#citeas},
    }

  • S. L. Bauke, A. Schnepf, C. von Sperber, N. Orlowski, H. Lewandowski, T. Selzner, F. Tamburini, and W. Amelung, “Tracing uptake and translocation of phosphorus in wheat using oxygen isotopes and mathematical modelling,” New Phytologist, vol. 230, iss. 5, pp. 1883-1895, 2021. doi:https://doi.org/10.1111/nph.17307
    [BibTeX] [PDF]

    Summary Understanding P uptake in soil–plant systems requires suitable P tracers. The stable oxygen isotope ratio in phosphate (expressed as δ18OP) is an alternative to radioactive labelling, but the degree to which plants preserve the δ18OP value of the P source is unclear. We hypothesised that the source signal will be preserved in roots rather than shoots. In soil and hydroponic experiments with spring wheat (Triticum aestivum), we replaced irrigation water by 18O-labelled water for up to 10 d. We extracted plant inorganic phosphates with trichloroacetic acid (TCA), assessed temporal dynamics of δ18OTCA-P values after changing to 18O-labelled water and combined the results with a mathematical model. Within 1 wk, full equilibration of δ18OTCA-P values with the isotope value of the water in the growth medium occurred in shoots but not in roots. Model results further indicated that root δ18OTCA-P values were affected by back transport of phosphate from shoots to roots, with a greater contribution of source P at higher temperatures when back transport was reduced. Root δ18OTCA-P partially preserved the source signal, providing an indicator of P uptake sources. This now needs to be tested extensively for different species, soil and climate conditions to enable application in future ecosystem studies.

    @Article{https://doi.org/10.1111/nph.17307,
    author = {Bauke, Sara L. and Schnepf, Andrea and von Sperber, Christian and Orlowski, Natalie and Lewandowski, Hans and Selzner, Tobias and Tamburini, Federica and Amelung, Wulf},
    title = {Tracing uptake and translocation of phosphorus in wheat using oxygen isotopes and mathematical modelling},
    journal = {New Phytologist},
    volume = {230},
    number = {5},
    pages = {1883-1895},
    keywords = {hydroponics, isotope model, oxygen isotope exchange, phosphate, plant P uptake, roots},
    doi = {https://doi.org/10.1111/nph.17307},
    url = {https://nph.onlinelibrary.wiley.com/doi/abs/10.1111/nph.17307},
    eprint = {https://nph.onlinelibrary.wiley.com/doi/pdf/10.1111/nph.17307},
    abstract = {Summary Understanding P uptake in soil–plant systems requires suitable P tracers. The stable oxygen isotope ratio in phosphate (expressed as δ18OP) is an alternative to radioactive labelling, but the degree to which plants preserve the δ18OP value of the P source is unclear. We hypothesised that the source signal will be preserved in roots rather than shoots. In soil and hydroponic experiments with spring wheat (Triticum aestivum), we replaced irrigation water by 18O-labelled water for up to 10 d. We extracted plant inorganic phosphates with trichloroacetic acid (TCA), assessed temporal dynamics of δ18OTCA-P values after changing to 18O-labelled water and combined the results with a mathematical model. Within 1 wk, full equilibration of δ18OTCA-P values with the isotope value of the water in the growth medium occurred in shoots but not in roots. Model results further indicated that root δ18OTCA-P values were affected by back transport of phosphate from shoots to roots, with a greater contribution of source P at higher temperatures when back transport was reduced. Root δ18OTCA-P partially preserved the source signal, providing an indicator of P uptake sources. This now needs to be tested extensively for different species, soil and climate conditions to enable application in future ecosystem studies.},
    year = {2021},
    }

  • E. Katche, R. Gaebelein, Z. Idris, P. Vasquez-Teuber, Y. Lo, D. Nugent, J. Batley, and A. S. Mason, “Stable, fertile lines produced by hybridization between allotetraploids Brassica juncea (AABB) and Brassica carinata (BBCC) have merged the A and C genomes,” New Phytologist, vol. 230, iss. 3, pp. 1242-1257, 2021. doi:https://doi.org/10.1111/nph.17225
    [BibTeX] [PDF]

    Summary Many flowering plant taxa contain allopolyploids that share one or more genomes in common. In the Brassica genus, crop species Brassica juncea and Brassica carinata share the B genome, with 2n = AABB and 2n = BBCC genome complements, respectively. Hybridization results in 2n = BBAC hybrids, but the fate of these hybrids over generations of self-pollination has never been reported. We produced and characterized B. juncea × B. carinata (2n = BBAC) interspecific hybrids over six generations of self-pollination under selection for high fertility using a combination of genotyping, fertility phenotyping, and cytogenetics techniques. Meiotic pairing behaviour improved from 68\% bivalents in the F1 to 98\% in the S5/S6 generations, and initially low hybrid fertility also increased to parent species levels. The S5/S6 hybrids contained an intact B genome (16 chromosomes) plus a new, stable A/C genome (18–20 chromosomes) resulting from recombination and restructuring of A and C-genome chromosomes. Our results provide the first experimental evidence that two genomes can come together to form a new, restructured genome in hybridization events between two allotetraploid species that share a common genome. This mechanism should be considered in interpreting phylogenies in taxa with multiple allopolyploid species.

    @Article{https://doi.org/10.1111/nph.17225,
    author = {Katche, Elvis and Gaebelein, Roman and Idris, Zurianti and Vasquez-Teuber, Paula and Lo, Yu-tzu and Nugent, David and Batley, Jacqueline and Mason, Annaliese S.},
    title = {Stable, fertile lines produced by hybridization between allotetraploids Brassica juncea (AABB) and Brassica carinata (BBCC) have merged the A and C genomes},
    journal = {New Phytologist},
    volume = {230},
    number = {3},
    pages = {1242-1257},
    keywords = {Brassica, genome rearrangement, homoeologous exchanges, interspecific hybridization, polyploidy},
    doi = {https://doi.org/10.1111/nph.17225},
    url = {https://nph.onlinelibrary.wiley.com/doi/abs/10.1111/nph.17225},
    eprint = {https://nph.onlinelibrary.wiley.com/doi/pdf/10.1111/nph.17225},
    abstract = {Summary Many flowering plant taxa contain allopolyploids that share one or more genomes in common. In the Brassica genus, crop species Brassica juncea and Brassica carinata share the B genome, with 2n = AABB and 2n = BBCC genome complements, respectively. Hybridization results in 2n = BBAC hybrids, but the fate of these hybrids over generations of self-pollination has never been reported. We produced and characterized B. juncea × B. carinata (2n = BBAC) interspecific hybrids over six generations of self-pollination under selection for high fertility using a combination of genotyping, fertility phenotyping, and cytogenetics techniques. Meiotic pairing behaviour improved from 68\% bivalents in the F1 to 98\% in the S5/S6 generations, and initially low hybrid fertility also increased to parent species levels. The S5/S6 hybrids contained an intact B genome (16 chromosomes) plus a new, stable A/C genome (18–20 chromosomes) resulting from recombination and restructuring of A and C-genome chromosomes. Our results provide the first experimental evidence that two genomes can come together to form a new, restructured genome in hybridization events between two allotetraploid species that share a common genome. This mechanism should be considered in interpreting phylogenies in taxa with multiple allopolyploid species.},
    year = {2021},
    }

  • D. Bohnenkamp, J. Behmann, S. Paulus, U. Steiner, and A. Mahlein, “A Hyperspectral Library of Foliar Diseases of Wheat,” Phytopathology®, p. PHYTO-09-19-0335-R, 2021. doi:10.1094/PHYTO-09-19-0335-R
    [BibTeX] [PDF]

    This work established a hyperspectral library of important foliar diseases of wheat induced by different fungal pathogens, representing a time series from infection to symptom appearance for the purpose of detecting spectral changes. The data were generated under controlled conditions at the leaf scale. The transition from healthy to diseased leaf tissue was assessed, and spectral shifts were identified and used in combination with histological investigations to define developmental stages in pathogenesis for each disease. The spectral signatures of each plant disease that indicate a specific developmental stage during pathogenesis, defined as turning points, were combined into a spectral library. Machine learning analysis methods were applied and compared to test the potential of this library to detect and quantify foliar diseases in hyperspectral images. All evaluated classifiers had high accuracy (≤99\%) for the detection and identification of both biotrophic and necrotrophic fungi. The potential of applying spectral analysis methods in combination with a spectral library for the detection and identification of plant diseases is demonstrated. Further evaluation and development of these algorithms should contribute to a robust detection and identification system for plant diseases at different developmental stages and the promotion and development of site-specific management techniques for plant diseases under field conditions.

    @Article{bohnenkamppp,
    author = {Bohnenkamp, David and Behmann, Jan and Paulus, Stefan and Steiner, Ulrike and Mahlein, Anne-Katrin},
    title = {A Hyperspectral Library of Foliar Diseases of Wheat},
    journal = {Phytopathology®},
    volume = {0},
    number = {0},
    pages = {PHYTO-09-19-0335-R},
    year = 2021,
    doi = {10.1094/PHYTO-09-19-0335-R},
    url = {https://doi.org/10.1094/PHYTO-09-19-0335-R},
    eprint = {https://doi.org/10.1094/PHYTO-09-19-0335-R},
    abstract = { This work established a hyperspectral library of important foliar diseases of wheat induced by different fungal pathogens, representing a time series from infection to symptom appearance for the purpose of detecting spectral changes. The data were generated under controlled conditions at the leaf scale. The transition from healthy to diseased leaf tissue was assessed, and spectral shifts were identified and used in combination with histological investigations to define developmental stages in pathogenesis for each disease. The spectral signatures of each plant disease that indicate a specific developmental stage during pathogenesis, defined as turning points, were combined into a spectral library. Machine learning analysis methods were applied and compared to test the potential of this library to detect and quantify foliar diseases in hyperspectral images. All evaluated classifiers had high accuracy (≤99\%) for the detection and identification of both biotrophic and necrotrophic fungi. The potential of applying spectral analysis methods in combination with a spectral library for the detection and identification of plant diseases is demonstrated. Further evaluation and development of these algorithms should contribute to a robust detection and identification system for plant diseases at different developmental stages and the promotion and development of site-specific management techniques for plant diseases under field conditions. },
    }

  • D. Hu, J. Jing, R. J. Snowdon, A. S. Mason, J. Shen, J. Meng, and J. Zou, “Exploring the gene pool of Brassica napus by genomics-based approaches,” Plant Biotechnology Journal, vol. 19, iss. 9, pp. 1693-1712, 2021. doi:https://doi.org/10.1111/pbi.13636
    [BibTeX] [PDF]

    Summary De novo allopolyploidization in Brassica provides a very successful model for reconstructing polyploid genomes using progenitor species and relatives to broaden crop gene pools and understand genome evolution after polyploidy, interspecific hybridization and exotic introgression. B. napus (AACC), the major cultivated rapeseed species and the third largest oilseed crop in the world, is a young Brassica species with a limited genetic base resulting from its short history of domestication, cultivation, and intensive selection during breeding for target economic traits. However, the gene pool of B. napus has been significantly enriched in recent decades that has been benefit from worldwide effects by the successful introduction of abundant subgenomic variation and novel genomic variation via intraspecific, interspecific and intergeneric crosses. An important question in this respect is how to utilize such variation to breed crops adapted to the changing global climate. Here, we review the genetic diversity, genome structure, and population-level differentiation of the B. napus gene pool in relation to known exotic introgressions from various species of the Brassicaceae, especially those elucidated by recent genome-sequencing projects. We also summarize progress in gene cloning, trait-marker associations, gene editing, molecular marker-assisted selection and genome-wide prediction, and describe the challenges and opportunities of these techniques as molecular platforms to exploit novel genomic variation and their value in the rapeseed gene pool. Future progress will accelerate the creation and manipulation of genetic diversity with genomic-based improvement, as well as provide novel insights into the neo-domestication of polyploid crops with novel genetic diversity from reconstructed genomes.

    @Article{https://doi.org/10.1111/pbi.13636,
    author = {Hu, Dandan and Jing, Jinjie and Snowdon, Rod J. and Mason, Annaliese S. and Shen, Jinxiong and Meng, Jinling and Zou, Jun},
    title = {Exploring the gene pool of Brassica napus by genomics-based approaches},
    journal = {Plant Biotechnology Journal},
    volume = {19},
    number = {9},
    pages = {1693-1712},
    keywords = {polyploid crop, Brassica, gene pool, exotic introgressions, genomic changes, genomic-based improvement},
    doi = {https://doi.org/10.1111/pbi.13636},
    url = {https://onlinelibrary.wiley.com/doi/abs/10.1111/pbi.13636},
    eprint = {https://onlinelibrary.wiley.com/doi/pdf/10.1111/pbi.13636},
    abstract = {Summary De novo allopolyploidization in Brassica provides a very successful model for reconstructing polyploid genomes using progenitor species and relatives to broaden crop gene pools and understand genome evolution after polyploidy, interspecific hybridization and exotic introgression. B. napus (AACC), the major cultivated rapeseed species and the third largest oilseed crop in the world, is a young Brassica species with a limited genetic base resulting from its short history of domestication, cultivation, and intensive selection during breeding for target economic traits. However, the gene pool of B. napus has been significantly enriched in recent decades that has been benefit from worldwide effects by the successful introduction of abundant subgenomic variation and novel genomic variation via intraspecific, interspecific and intergeneric crosses. An important question in this respect is how to utilize such variation to breed crops adapted to the changing global climate. Here, we review the genetic diversity, genome structure, and population-level differentiation of the B. napus gene pool in relation to known exotic introgressions from various species of the Brassicaceae, especially those elucidated by recent genome-sequencing projects. We also summarize progress in gene cloning, trait-marker associations, gene editing, molecular marker-assisted selection and genome-wide prediction, and describe the challenges and opportunities of these techniques as molecular platforms to exploit novel genomic variation and their value in the rapeseed gene pool. Future progress will accelerate the creation and manipulation of genetic diversity with genomic-based improvement, as well as provide novel insights into the neo-domestication of polyploid crops with novel genetic diversity from reconstructed genomes.},
    year = {2021},
    }

  • F. Navarrete, N. Grujic, A. Stirnberg, I. Saado, D. Aleksza, M. Gallei, H. Adi, A. Alcântara, M. Khan, J. Bindics, M. Trujillo, and A. Djamei, “The Pleiades are a cluster of fungal effectors that inhibit host defenses,” PLOS Pathogens, vol. 17, iss. 6, pp. 1-24, 2021. doi:10.1371/journal.ppat.1009641
    [BibTeX] [PDF]

    Biotrophic plant pathogens secrete effector proteins to manipulate the host physiology. Effectors suppress defenses and induce an environment favorable to disease development. Sequence-based prediction of effector function is impeded by their rapid evolution rate. In the maize pathogen Ustilago maydis, effector-coding genes frequently organize in clusters. Here we describe the functional characterization of the pleiades, a cluster of ten effector genes, by analyzing the micro- and macroscopic phenotype of the cluster deletion and expressing these proteins in planta. Deletion of the pleiades leads to strongly impaired virulence and accumulation of reactive oxygen species (ROS) in infected tissue. Eight of the Pleiades suppress the production of ROS upon perception of pathogen associated molecular patterns (PAMPs). Although functionally redundant, the Pleiades target different host components. The paralogs Taygeta1 and Merope1 suppress ROS production in either the cytoplasm or nucleus, respectively. Merope1 targets and promotes the auto-ubiquitination activity of RFI2, a conserved family of E3 ligases that regulates the production of PAMP-triggered ROS burst in plants.

    @Article{10.1371/journal.ppat.1009641,
    doi = {10.1371/journal.ppat.1009641},
    author = {Navarrete, Fernando AND Grujic, Nenad AND Stirnberg, Alexandra AND Saado, Indira AND Aleksza, David AND Gallei, Michelle AND Adi, Hazem AND Alcântara, André AND Khan, Mamoona AND Bindics, Janos AND Trujillo, Marco AND Djamei, Armin},
    journal = {PLOS Pathogens},
    publisher = {Public Library of Science},
    title = {The Pleiades are a cluster of fungal effectors that inhibit host defenses},
    year = {2021},
    month = {06},
    volume = {17},
    url = {https://doi.org/10.1371/journal.ppat.1009641},
    pages = {1-24},
    abstract = {Biotrophic plant pathogens secrete effector proteins to manipulate the host physiology. Effectors suppress defenses and induce an environment favorable to disease development. Sequence-based prediction of effector function is impeded by their rapid evolution rate. In the maize pathogen Ustilago maydis, effector-coding genes frequently organize in clusters. Here we describe the functional characterization of the pleiades, a cluster of ten effector genes, by analyzing the micro- and macroscopic phenotype of the cluster deletion and expressing these proteins in planta. Deletion of the pleiades leads to strongly impaired virulence and accumulation of reactive oxygen species (ROS) in infected tissue. Eight of the Pleiades suppress the production of ROS upon perception of pathogen associated molecular patterns (PAMPs). Although functionally redundant, the Pleiades target different host components. The paralogs Taygeta1 and Merope1 suppress ROS production in either the cytoplasm or nucleus, respectively. Merope1 targets and promotes the auto-ubiquitination activity of RFI2, a conserved family of E3 ligases that regulates the production of PAMP-triggered ROS burst in plants.},
    number = {6},
    }

  • C. Pahmeyer, D. Schäfer, T. Kuhn, and W. Britz, “Data on a synthetic farm population of the German federal state of North Rhine-Westphalia,” Data in Brief, vol. 36, p. 107007, 2021. doi:https://doi.org/10.1016/j.dib.2021.107007
    [BibTeX] [PDF]

    Farm-scale and agent-based models draw typically on detailed and preferably spatially explicit single farm data. Data protection standards however restrict or exclude their access, as for example in Germany. We provide data on a synthetic farm population of the German federal state of North Rhine-Westphalia, mainly based on the German Farm Structure Survey 2016 and plot specific crop data from 2019/2020. The population is derived from farm typology at administrative unit level to which the observed plots are allocated afterwards. The data contains 25,858 farms and covers 1.3 million ha of agricultural land, provided at plot scale in a geospatial vector and at farm scale in tabular format. For each plot, the managing farm (including the estimated farm’s location), the number of livestock, the cultivated crop, as well as the corresponding administration units are indicated. Furthermore, spatial data such as yield information, soil characteristics, as well as monitoring data on environmental status are attached. The provided data allows for diverse analysis on the farm population in the federal state of North Rhine-Westphalia with farm, agent-based or different bio-physical models. Furthermore, it can serve as a test data set for models which require detailed and spatially explicit farm data.

    @Article{pahmeyer2021107007,
    title = {Data on a synthetic farm population of the German federal state of North Rhine-Westphalia},
    journal = {Data in Brief},
    volume = {36},
    pages = {107007},
    year = {2021},
    issn = {2352-3409},
    doi = {https://doi.org/10.1016/j.dib.2021.107007},
    url = {https://www.sciencedirect.com/science/article/pii/S2352340921002912},
    author = {Christoph Pahmeyer and David Schäfer and Till Kuhn and Wolfgang Britz},
    keywords = {Synthetic farm population, Farm typology, Germany, North Rhine-Westphalia, Farm modeling, Agent-based modeling},
    abstract = {Farm-scale and agent-based models draw typically on detailed and preferably spatially explicit single farm data. Data protection standards however restrict or exclude their access, as for example in Germany. We provide data on a synthetic farm population of the German federal state of North Rhine-Westphalia, mainly based on the German Farm Structure Survey 2016 and plot specific crop data from 2019/2020. The population is derived from farm typology at administrative unit level to which the observed plots are allocated afterwards. The data contains 25,858 farms and covers 1.3 million ha of agricultural land, provided at plot scale in a geospatial vector and at farm scale in tabular format. For each plot, the managing farm (including the estimated farm's location), the number of livestock, the cultivated crop, as well as the corresponding administration units are indicated. Furthermore, spatial data such as yield information, soil characteristics, as well as monitoring data on environmental status are attached. The provided data allows for diverse analysis on the farm population in the federal state of North Rhine-Westphalia with farm, agent-based or different bio-physical models. Furthermore, it can serve as a test data set for models which require detailed and spatially explicit farm data.},
    }

  • C. Pahmeyer, T. Kuhn, and W. Britz, “‘Fruchtfolge’: A crop rotation decision support system for optimizing cropping choices with big data and spatially explicit modeling,” Computers and Electronics in Agriculture, vol. 181, p. 105948, 2021. doi:https://doi.org/10.1016/j.compag.2020.105948
    [BibTeX] [PDF]

    Deciding on which crop to plant on a field and how to fertilize it has become increasingly complex as volatile markets, location factors as well as policy restrictions need to be considered simultaneously. To assist farmers in this process, we develop the web-based, open source decision support system ‘Fruchtfolge’ (German for ‘crop rotation’). It provides decision makers with a crop and coarse manure fertilization management recommendation for each field based on the solution of a single farm optimization model. The optimization model accounts for field specific location factors, labor endowments, field-to-farm distances and policy restrictions such as measures linked to the EU Nitrates Directives and the Greening of the EU Common Agricultural Policy. ‘Fruchtfolge’ is user-friendly by automatically including big data related to farm, location and management characteristics and providing instant feedback on alternative management choices. This way, creating a first optimal cropping plan generally requires less than five minutes. We apply the decision support system to a German case study farm which manages fields outside and inside a nitrate sensitive area. In the year 2021, revised fertilization regulations come in force in Germany, which amongst others lowers maximal allowed nitrogen applications relative to crop nutrient needs in nitrate sensitive areas. The regulations provoke profit losses of up to 15\% for the former optimal crop rotation. The optimal adaptation strategy proposed by ‘Fruchfolge’ diminishes this loss to 10\%. The reduction in profit loss clearly underlines the benefits of our support tool to take optimal cropping decisions in a complex environment. Future research should identify barriers of farmers to apply decision support systems and upon availability, integrate more detailed crop and field specific sensor data.

    @Article{pahmeyer2021105948,
    title = {‘Fruchtfolge’: A crop rotation decision support system for optimizing cropping choices with big data and spatially explicit modeling},
    journal = {Computers and Electronics in Agriculture},
    volume = {181},
    pages = {105948},
    year = {2021},
    issn = {0168-1699},
    doi = {https://doi.org/10.1016/j.compag.2020.105948},
    url = {https://www.sciencedirect.com/science/article/pii/S0168169920331537},
    author = {C. Pahmeyer and T. Kuhn and W. Britz},
    keywords = {Big data, Decision Support System, Nitrates Directive, Fertilization Ordinance, Farm level simulation model},
    abstract = {Deciding on which crop to plant on a field and how to fertilize it has become increasingly complex as volatile markets, location factors as well as policy restrictions need to be considered simultaneously. To assist farmers in this process, we develop the web-based, open source decision support system ‘Fruchtfolge’ (German for ‘crop rotation’). It provides decision makers with a crop and coarse manure fertilization management recommendation for each field based on the solution of a single farm optimization model. The optimization model accounts for field specific location factors, labor endowments, field-to-farm distances and policy restrictions such as measures linked to the EU Nitrates Directives and the Greening of the EU Common Agricultural Policy. ‘Fruchtfolge’ is user-friendly by automatically including big data related to farm, location and management characteristics and providing instant feedback on alternative management choices. This way, creating a first optimal cropping plan generally requires less than five minutes. We apply the decision support system to a German case study farm which manages fields outside and inside a nitrate sensitive area. In the year 2021, revised fertilization regulations come in force in Germany, which amongst others lowers maximal allowed nitrogen applications relative to crop nutrient needs in nitrate sensitive areas. The regulations provoke profit losses of up to 15\% for the former optimal crop rotation. The optimal adaptation strategy proposed by ‘Fruchfolge’ diminishes this loss to 10\%. The reduction in profit loss clearly underlines the benefits of our support tool to take optimal cropping decisions in a complex environment. Future research should identify barriers of farmers to apply decision support systems and upon availability, integrate more detailed crop and field specific sensor data.},
    }

  • E. Cardona Santos, H. Storm, and S. Rasch, “The cost-effectiveness of conservation auctions in the presence of asset specificity: An agent-based model,” Land Use Policy, vol. 102, p. 104907, 2021. doi:https://doi.org/10.1016/j.landusepol.2020.104907
    [BibTeX] [PDF]

    Payments for Environmental Services are a financial incentive for land users to conserve and restore ecosystems. One of the challenges in their implementation is to maximize their cost-effectiveness, or put in other words, to maximize the provision of environmental services for a given budget. This study focuses on two aspects that endanger the cost-effectiveness of such schemes: asymmetric information and asset specificity. If land users are better informed about their own provision costs, compared to the agency, they can increase their rents by demanding higher payments. The presence of asset specificity makes land users vulnerable to being harmed by opportunism. To compensate this risk, they could require higher payments or an exante compensation, likely to compromise compliance. Auctions are claimed to reduce informational rents by revealing land users’ true provision costs. However, their costeffectiveness has been shown to deteriorate if they are repeated over time because bidders can learn and adapt their strategies. Social interaction is particularly important in this context, as it allows land users to gather information on the bid cap; and it allows for trust building, which can substitute the costly formulation and enforcement of contracts, and thus reduce contracting costs. So far, there are only few studies analyzing the effect of asset specificity on the cost-effectiveness of auctions. Our study fills this gap using an agent-based model to analyze the cost-effectiveness of uniform and discriminatory one-shot and repeated auctions. In our model, land users are assumed to be embedded in a social network through which they can interact and learn. Our results suggest that repeated auctions can increase the cost-effectiveness of payments schemes in the presence of asset specificity despite of learning effects over time if land users face liquidity constraints and high time preferences.

    @Article{cardonasantos2021104907,
    title = {The cost-effectiveness of conservation auctions in the presence of asset specificity: An agent-based model},
    journal = {Land Use Policy},
    volume = {102},
    pages = {104907},
    year = {2021},
    issn = {0264-8377},
    doi = {https://doi.org/10.1016/j.landusepol.2020.104907},
    url = {https://www.sciencedirect.com/science/article/pii/S026483771931258X},
    author = {Elsa {Cardona Santos} and Hugo Storm and Sebastian Rasch},
    keywords = {Agent-based modeling, Discriminatory auctions, Uniform auctions, Reforestation, Conservation, Asset specificity, Payments for environmental services, Social interaction, Trust},
    abstract = {Payments for Environmental Services are a financial incentive for land users to conserve and restore ecosystems. One of the challenges in their implementation is to maximize their cost-effectiveness, or put in other words, to maximize the provision of environmental services for a given budget. This study focuses on two aspects that endanger the cost-effectiveness of such schemes: asymmetric information and asset specificity. If land users are better informed about their own provision costs, compared to the agency, they can increase their rents by demanding higher payments. The presence of asset specificity makes land users vulnerable to being harmed by opportunism. To compensate this risk, they could require higher payments or an exante compensation, likely to compromise compliance. Auctions are claimed to reduce informational rents by revealing land users’ true provision costs. However, their costeffectiveness has been shown to deteriorate if they are repeated over time because bidders can learn and adapt their strategies. Social interaction is particularly important in this context, as it allows land users to gather information on the bid cap; and it allows for trust building, which can substitute the costly formulation and enforcement of contracts, and thus reduce contracting costs. So far, there are only few studies analyzing the effect of asset specificity on the cost-effectiveness of auctions. Our study fills this gap using an agent-based model to analyze the cost-effectiveness of uniform and discriminatory one-shot and repeated auctions. In our model, land users are assumed to be embedded in a social network through which they can interact and learn. Our results suggest that repeated auctions can increase the cost-effectiveness of payments schemes in the presence of asset specificity despite of learning effects over time if land users face liquidity constraints and high time preferences.},
    }

  • S. Rasch, T. Wünscher, F. Casasola, M. Ibrahim, and H. Storm, “Permanence of PES and the role of social context in the Regional Integrated Silvo-pastoral Ecosystem Management Project in Costa Rica,” Ecological Economics, vol. 185, p. 107027, 2021. doi:https://doi.org/10.1016/j.ecolecon.2021.107027
    [BibTeX] [PDF]

    We present rare, empirical evidence on the permanence of land use changes induced by a payments for ecosystem services (PES) program. A follow-up study was conducted a decade after the end of the Regional Integrated Silvo-pastoral Ecosystem Management Project (RISEMP) in Costa Rica. Econometric analysis found that silvo-pastoral practices persisted in the long term and are not reverted. On average there is also no meaningful intensification of practices after payments ceased. However, there is some heterogeneity on the individual level. We find that farms that increase adoption after the end of the project are farms with slower adoption during the project while some farms that decrease adoption are intense adopters. This indicates a pattern of convergence in the long run. Additionally, we challenge the assumption that payments are mono-causally inducing land use change by investigating non-monetary factors associated practice adoption. We find that not only PES explains adoption of silvo-pastoral practices. While it is challenging to establish clear casual linkages, we find that adoption is associated with the number of social ties to other farmers as well as negatively correlated to the exposure to traditional production paradigms measured as membership, as well as peer membership, in producer organisations.

    @Article{rasch2021107027,
    title = {Permanence of PES and the role of social context in the Regional Integrated Silvo-pastoral Ecosystem Management Project in Costa Rica},
    journal = {Ecological Economics},
    volume = {185},
    pages = {107027},
    year = {2021},
    issn = {0921-8009},
    doi = {https://doi.org/10.1016/j.ecolecon.2021.107027},
    url = {https://www.sciencedirect.com/science/article/pii/S0921800921000859},
    author = {Sebastian Rasch and Tobias Wünscher and Francisco Casasola and Muhammad Ibrahim and Hugo Storm},
    abstract = {We present rare, empirical evidence on the permanence of land use changes induced by a payments for ecosystem services (PES) program. A follow-up study was conducted a decade after the end of the Regional Integrated Silvo-pastoral Ecosystem Management Project (RISEMP) in Costa Rica. Econometric analysis found that silvo-pastoral practices persisted in the long term and are not reverted. On average there is also no meaningful intensification of practices after payments ceased. However, there is some heterogeneity on the individual level. We find that farms that increase adoption after the end of the project are farms with slower adoption during the project while some farms that decrease adoption are intense adopters. This indicates a pattern of convergence in the long run. Additionally, we challenge the assumption that payments are mono-causally inducing land use change by investigating non-monetary factors associated practice adoption. We find that not only PES explains adoption of silvo-pastoral practices. While it is challenging to establish clear casual linkages, we find that adoption is associated with the number of social ties to other farmers as well as negatively correlated to the exposure to traditional production paradigms measured as membership, as well as peer membership, in producer organisations.},
    }

  • Y. Yu, L. Weihermüller, A. Klotzsche, L. Lärm, H. Vereecken, and J. A. Huisman, “Sequential and coupled inversion of horizontal borehole ground penetrating radar data to estimate soil hydraulic properties at the field scale,” Journal of Hydrology, vol. 596, p. 126010, 2021. doi:https://doi.org/10.1016/j.jhydrol.2021.126010
    [BibTeX] [PDF]

    Horizontal borehole ground penetrating radar (GPR) measurements can provide valuable information on soil water content (SWC) dynamics in the vadose zone, and hence show potential to estimate soil hydraulic properties. In this study, the performance of both sequential and coupled inversion workflows to obtain soil hydraulic properties from time-lapse horizontal borehole GPR data obtained during an infiltration experiment were compared using a synthetic modelling study and the analysis of actual field data. The sequential inversion using the vadose zone flow model HYDRUS-1D directly relied on SWC profiles determined from the travel time of GPR direct waves using the straight-wave approximation. The synthetic modelling study showed that sequential inversion did not provide accurate estimates of the soil hydraulic parameters due to interpretation errors in the estimated SWC near the infiltration front and the ground surface. In contrast, the coupled inversion approach, which combined HYDRUS-1D with a forward model of GPR wave propagation (gprMax3D) and GPR travel time information, provided accurate estimates of the hydraulic properties in the synthetic modelling study. The application of the coupled inversion approach to measured borehole GPR data also resulted in plausible estimates of the soil hydraulic parameters. It was concluded that coupled inversion should be preferred over sequential inversion of time-lapse horizontal borehole GPR data in the presence of strong SWC gradients that occur during infiltration events.

    @Article{yu2021126010,
    title = {Sequential and coupled inversion of horizontal borehole ground penetrating radar data to estimate soil hydraulic properties at the field scale},
    journal = {Journal of Hydrology},
    volume = {596},
    pages = {126010},
    year = {2021},
    issn = {0022-1694},
    doi = {https://doi.org/10.1016/j.jhydrol.2021.126010},
    url = {https://www.sciencedirect.com/science/article/pii/S0022169421000573},
    author = {Yi Yu and Lutz Weihermüller and Anja Klotzsche and Lena Lärm and Harry Vereecken and Johan Alexander Huisman},
    keywords = {Ground penetrating radar, Hydrogeophysics, Coupled inversion},
    abstract = {Horizontal borehole ground penetrating radar (GPR) measurements can provide valuable information on soil water content (SWC) dynamics in the vadose zone, and hence show potential to estimate soil hydraulic properties. In this study, the performance of both sequential and coupled inversion workflows to obtain soil hydraulic properties from time-lapse horizontal borehole GPR data obtained during an infiltration experiment were compared using a synthetic modelling study and the analysis of actual field data. The sequential inversion using the vadose zone flow model HYDRUS-1D directly relied on SWC profiles determined from the travel time of GPR direct waves using the straight-wave approximation. The synthetic modelling study showed that sequential inversion did not provide accurate estimates of the soil hydraulic parameters due to interpretation errors in the estimated SWC near the infiltration front and the ground surface. In contrast, the coupled inversion approach, which combined HYDRUS-1D with a forward model of GPR wave propagation (gprMax3D) and GPR travel time information, provided accurate estimates of the hydraulic properties in the synthetic modelling study. The application of the coupled inversion approach to measured borehole GPR data also resulted in plausible estimates of the soil hydraulic parameters. It was concluded that coupled inversion should be preferred over sequential inversion of time-lapse horizontal borehole GPR data in the presence of strong SWC gradients that occur during infiltration events.},
    }

  • J. Henderson, J. Godar, G. P. Frey, J. Börner, and T. Gardner, “The Paraguayan Chaco at a crossroads: drivers of an emerging soybean frontier,” Regional Environmental Change, vol. 21, 2021. doi:10.1007/s10113-021-01804-z
    [BibTeX] [PDF]
    @Article{henderson2021,
    author = {Henderson, J. AND Godar, J. AND Frey, G. P. AND Börner, J. AND Gardner, T.},
    title = {{The Paraguayan Chaco at a crossroads: drivers of an emerging soybean frontier}},
    journal = {Regional Environmental Change},
    volume = {21},
    issue = {3},
    year = {2021},
    doi = {10.1007/s10113-021-01804-z},
    url = {https://link.springer.com/article/10.1007%2Fs10113-021-01804-z#citeas},
    }

  • H. Jorda, K. Huber, A. Kunkel, J. Vanderborght, M. Javaux, C. Oberdörster, K. Hammel, and A. Schnepf, “Mechanistic modeling of pesticide uptake with a 3D plant architecture model,” Environmental Science and Pollution Research, vol. 28, p. 55678–55689, 2021. doi:10.1007/s11356-021-14878-3
    [BibTeX] [PDF]
    @Article{jorda2021espr,
    author = {Jorda, H. AND Huber, K. AND Kunkel, A. AND Vanderborght, J. AND Javaux, M. AND Oberdörster, C. AND Hammel, K. AND Schnepf, A.},
    title = {{Mechanistic modeling of pesticide uptake with a 3D plant architecture model}},
    journal = {Environmental Science and Pollution Research},
    volume = {28},
    issue = {39},
    year = {2021},
    doi = {10.1007/s11356-021-14878-3},
    pages = {55678–55689},
    url = {https://doi.org/10.1007/s11356-021-14878-3},
    }

  • A. Schnepf and X. He, “Rhizosphere 5 – shining light on the world beneath our feet,” Plant and Soil, vol. 461, 2021. doi:10.1007/s11104-021-04942-9
    [BibTeX] [PDF]
    @Article{schnepf2021plso,
    author = {Schnepf, A. AND He, X.},
    title = {{Rhizosphere 5 - shining light on the world beneath our feet}},
    journal = {Plant and Soil},
    volume = {461},
    issue = {1},
    year = {2021},
    doi = {10.1007/s11104-021-04942-9},
    url = {https://doi.org/10.1007/s11104-021-04942-9},
    }

  • K. Zhank, A. S. Mason, M. A. Farooq, and et. al., “Challenges and prospects for a potential allohexaploid Brassica crop,” Theoretical and Applied Genetics, vol. 134, pp. 2711-2726, 2021. doi:10.1007/s00122-021-03845-8
    [BibTeX] [PDF]
    @Article{zhang2021thapge,
    author = {Zhank, K. AND Mason, A. S. AND Farooq, M. A. AND et. al. },
    title = {{Challenges and prospects for a potential allohexaploid Brassica crop}},
    journal = {Theoretical and Applied Genetics},
    volume = {134},
    issue = {9},
    year = {2021},
    doi = {10.1007/s00122-021-03845-8},
    pages = {2711-2726},
    url = {https://doi.org/10.1007/s00122-021-03845-8},
    }

  • J. Behley, M. Garbade, A. Milioto, J. Quenzel, S. Behnke, J. Gall, and C. Stachniss, “Towards 3D LiDAR-based semantic scene understanding of 3D point cloud sequences: The SemanticKITTI Dataset,” The International Journal of Robotics Research, vol. 40, iss. 8-9, pp. 959-967, 2021. doi:10.1177/02783649211006735
    [BibTeX] [PDF]
    @Article{doi:10.1177/02783649211006735,
    author = {Jens Behley and Martin Garbade and Andres Milioto and Jan Quenzel and Sven Behnke and Jürgen Gall and Cyrill Stachniss},
    title = {Towards 3D LiDAR-based semantic scene understanding of 3D point cloud sequences: The SemanticKITTI Dataset},
    journal = {The International Journal of Robotics Research},
    volume = {40},
    number = {8-9},
    pages = {959-967},
    year = {2021},
    doi = {10.1177/02783649211006735},
    url = { https://doi.org/10.1177/02783649211006735
    },
    eprint = { https://doi.org/10.1177/02783649211006735},
    }

  • C. H. Bock, S. J. Pethybridge, J. G. A. Barbedo, P. D. Esker, A. -K. Mahlein, and E. M. Del Ponte, “A phytopathometry glossary for the twenty-first century: towards consistency and precision in intra- and inter-disciplinary dialogues,” Tropical Plant Pathology, 2021. doi:10.1007/s40858-021-00454-0
    [BibTeX] [PDF]
    @Article{bock2021troplapa,
    author = {Bock, C. H. AND Pethybridge, S. J. AND Barbedo, J. G. A. AND Esker, P. D. AND Mahlein, A.-K. AND Del Ponte, E. M.},
    title = {{A phytopathometry glossary for the twenty-first century: towards consistency and precision in intra- and inter-disciplinary dialogues}},
    journal = {Tropical Plant Pathology},
    year = {2021},
    doi = {10.1007/s40858-021-00454-0},
    url = {ttps://doi.org/10.1007/s40858-021-00454-0},
    }

  • N. Behrmann, J. Gall, and M. Noroozi, “Unsupervised Video Representation Learning by Bidirectional Feature Prediction,” in Winter Conference on Applications of Computer Vision , 2021.
    [BibTeX] [PDF]
    @InProceedings{behrmann2021apcv,
    author = {Behrmann, N. AND Gall, J. AND Noroozi, M.},
    title = {{Unsupervised Video Representation Learning by Bidirectional Feature Prediction}},
    booktitle = {Winter Conference on Applications of Computer Vision},
    year = {2021},
    url = {https://pages.iai.uni-bonn.de/gall_juergen/download/video_representation.pdf},
    }

  • S. Morandage, J. Vanderborght, M. Zörner, G. Cai, D. Leitner, H. Vereecken, and A. Schnepf, “Root architecture development in stony soils,” Vadose Zone Journal, vol. 20, iss. 4, p. e20133, 2021. doi:https://doi.org/10.1002/vzj2.20133
    [BibTeX] [PDF]

    Abstract Soils with high stone content represent a challenge to root development, as each stone is an obstacle to root growth. A high stone content also affects soil properties such as temperature or water content, which in turn affects root growth. We investigated the effects of all soil properties combined on root development in the field using both experiments and modeling. Field experiments were carried out in rhizotron facilities during two consecutive growing seasons (wheat [Triticum aestivum L.] and maize [Zea mays L.]) in silty loam soils with high (>50\%) and low (<4\%) stone contents. We extended the CPlantBox root architecture model to explicitly consider the presence of stones and simulated root growth on the plot scale over the whole vegetation period. We found that a linear increase of stone content resulted in a linear decrease of rooting depth across all stone contents and developmental stages considered, whereas rooting depth was only sensitive to cracks below a certain crack density and at earlier growth stages. Moreover, the impact of precipitation-influenced soil strength had a relatively stronger impact on simulated root arrival curves during the vegetation periods than soil temperature. Resulting differences between stony and non-stony soil of otherwise the same crop and weather conditions show similar trends as the differences observed in the rhizotron facilities. The combined belowground effects resulted in differences in characteristic root system measures of up to 48\%. In future work, comparison of absolute values will require including shoot effects—in particular, different carbon availabilities.

    @Article{https://doi.org/10.1002/vzj2.20133,
    author = {Morandage, Shehan and Vanderborght, Jan and Zörner, Mirjam and Cai, Gaochao and Leitner, Daniel and Vereecken, Harry and Schnepf, Andrea},
    title = {Root architecture development in stony soils},
    journal = {Vadose Zone Journal},
    volume = {20},
    number = {4},
    pages = {e20133},
    doi = {https://doi.org/10.1002/vzj2.20133},
    url = {https://acsess.onlinelibrary.wiley.com/doi/abs/10.1002/vzj2.20133},
    eprint = {https://acsess.onlinelibrary.wiley.com/doi/pdf/10.1002/vzj2.20133},
    abstract = {Abstract Soils with high stone content represent a challenge to root development, as each stone is an obstacle to root growth. A high stone content also affects soil properties such as temperature or water content, which in turn affects root growth. We investigated the effects of all soil properties combined on root development in the field using both experiments and modeling. Field experiments were carried out in rhizotron facilities during two consecutive growing seasons (wheat [Triticum aestivum L.] and maize [Zea mays L.]) in silty loam soils with high (>50\%) and low (<4\%) stone contents. We extended the CPlantBox root architecture model to explicitly consider the presence of stones and simulated root growth on the plot scale over the whole vegetation period. We found that a linear increase of stone content resulted in a linear decrease of rooting depth across all stone contents and developmental stages considered, whereas rooting depth was only sensitive to cracks below a certain crack density and at earlier growth stages. Moreover, the impact of precipitation-influenced soil strength had a relatively stronger impact on simulated root arrival curves during the vegetation periods than soil temperature. Resulting differences between stony and non-stony soil of otherwise the same crop and weather conditions show similar trends as the differences observed in the rhizotron facilities. The combined belowground effects resulted in differences in characteristic root system measures of up to 48\%. In future work, comparison of absolute values will require including shoot effects—in particular, different carbon availabilities.},
    year = {2021},
    }

  • V. Roslinsky, K. C. Falk, R. Gaebelein, A. S. Mason, and C. Eynck, "Development of B. carinata with super-high erucic acid content through interspecific hybridization," Theoretical and Applied Genetics, vol. 134, pp. 3167-3181, 2021. doi:10.1007/s00122-021-03883-2
    [BibTeX] [PDF]
    @Article{roslinsky2021thap,
    author = {Roslinsky, V. AND Falk, K. C. AND Gaebelein, R. AND Mason, A. S. AND Eynck, C.},
    title = {{Development of B. carinata with super-high erucic acid content through interspecific hybridization}},
    journal = {Theoretical and Applied Genetics},
    volume = {134},
    issue = {10},
    year = {2021},
    doi = {10.1007/s00122-021-03883-2},
    pages = {3167-3181},
    url = {https://doi.org/10.1007/s00122-021-03883-2},
    }

  • E. Riemer, D. Qiu, D. Laha, R. K. Harmel, P. Gaugler, V. Gaugler, M. Frei, M. Hajirezaei, N. P. Laha, L. Krusenbaum, R. Schneider, A. Saiardi, D. Fiedler, H. J. Jessen, G. Schaaf, and R. F. H. Giehl, "ITPK1 is an InsP6/ADP phosphotransferase that controls phosphate signaling in Arabidopsis," Molecular Plant, 2021. doi:https://doi.org/10.1016/j.molp.2021.07.011
    [BibTeX] [PDF]

    In plants, phosphate (Pi) homeostasis is regulated by the interaction of PHR transcription factors with stand-alone SPX proteins, which act as sensors for inositol pyrophosphates. In this study, we combined different methods to obtain a comprehensive picture of how inositol (pyro)phosphate metabolism is regulated by Pi and dependent on the inositol phosphate kinase ITPK1. We found that inositol pyrophosphates are more responsive to Pi than lower inositol phosphates, a response conserved across kingdoms. Using the capillary electrophoresis electrospray ionization mass spectrometry (CE-ESI-MS) we could separate different InsP7 isomers in Arabidopsis and rice, and identify 4/6-InsP7 and a PP-InsP4 isomer hitherto not reported in plants. We found that the inositol pyrophosphates 1/3-InsP7, 5-InsP7, and InsP8 increase several fold in shoots after Pi resupply and that tissue-specific accumulation of inositol pyrophosphates relies on ITPK1 activities and MRP5-dependent InsP6 compartmentalization. Notably, ITPK1 is critical for Pi-dependent 5-InsP7 and InsP8 synthesis in planta and its activity regulates Pi starvation responses in a PHR-dependent manner. Furthermore, we demonstrated that ITPK1-mediated conversion of InsP6 to 5-InsP7 requires high ATP concentrations and that Arabidopsis ITPK1 has an ADP phosphotransferase activity to dephosphorylate specifically 5-InsP7 under low ATP. Collectively, our study provides new insights into Pi-dependent changes in nutritional and energetic states with the synthesis of regulatory inositol pyrophosphates.

    @Article{riemer2021,
    title = {ITPK1 is an InsP6/ADP phosphotransferase that controls phosphate signaling in Arabidopsis},
    journal = {Molecular Plant},
    year = {2021},
    issn = {1674-2052},
    doi = {https://doi.org/10.1016/j.molp.2021.07.011},
    url = {https://www.sciencedirect.com/science/article/pii/S167420522100277X},
    author = {Esther Riemer and Danye Qiu and Debabrata Laha and Robert K. Harmel and Philipp Gaugler and Verena Gaugler and Michael Frei and Mohammad-Reza Hajirezaei and Nargis Parvin Laha and Lukas Krusenbaum and Robin Schneider and Adolfo Saiardi and Dorothea Fiedler and Henning J. Jessen and Gabriel Schaaf and Ricardo F.H. Giehl},
    keywords = {inositol phosphates, inositol pyrophosphates, phosphate homeostasis, phosphate signaling, inositol 1,3,4-trisphosphate 5/6-kinase 1, diphosphoinositol pentakisphosphate kinase},
    abstract = {In plants, phosphate (Pi) homeostasis is regulated by the interaction of PHR transcription factors with stand-alone SPX proteins, which act as sensors for inositol pyrophosphates. In this study, we combined different methods to obtain a comprehensive picture of how inositol (pyro)phosphate metabolism is regulated by Pi and dependent on the inositol phosphate kinase ITPK1. We found that inositol pyrophosphates are more responsive to Pi than lower inositol phosphates, a response conserved across kingdoms. Using the capillary electrophoresis electrospray ionization mass spectrometry (CE-ESI-MS) we could separate different InsP7 isomers in Arabidopsis and rice, and identify 4/6-InsP7 and a PP-InsP4 isomer hitherto not reported in plants. We found that the inositol pyrophosphates 1/3-InsP7, 5-InsP7, and InsP8 increase several fold in shoots after Pi resupply and that tissue-specific accumulation of inositol pyrophosphates relies on ITPK1 activities and MRP5-dependent InsP6 compartmentalization. Notably, ITPK1 is critical for Pi-dependent 5-InsP7 and InsP8 synthesis in planta and its activity regulates Pi starvation responses in a PHR-dependent manner. Furthermore, we demonstrated that ITPK1-mediated conversion of InsP6 to 5-InsP7 requires high ATP concentrations and that Arabidopsis ITPK1 has an ADP phosphotransferase activity to dephosphorylate specifically 5-InsP7 under low ATP. Collectively, our study provides new insights into Pi-dependent changes in nutritional and energetic states with the synthesis of regulatory inositol pyrophosphates.},
    }

  • A. Dreier, J. Janßen, H. Kuhlmann, and L. Klingbeil, "Quality Analysis of Direct Georeferencing in Aspects of Absolute Accuracy and Precision for a UAV-Based Laser Scanning System," Remote Sensing, vol. 13, iss. 18, 2021. doi:10.3390/rs13183564
    [BibTeX] [PDF]

    The use of UAV-based laser scanning systems is increasing due to the rapid development in sensor technology, especially in applications such as topographic surveys or forestry. One advantage of these multi-sensor systems is the possibility of direct georeferencing of the derived 3D point clouds in a global reference frame without additional information from Ground Control Points (GCPs). This paper addresses the quality analysis of direct georeferencing of a UAV-based laser scanning system focusing on the absolute accuracy and precision of the system. The system investigated is based on the RIEGL miniVUX-SYS and the evaluation uses the estimated point clouds compared to a reference point cloud from Terrestrial Laser Scanning (TLS) for two different study areas. The precision is estimated by multiple repetitions of the same measurement and the use of artificial objects, such as targets and tables, resulting in a standard deviation of <1.2 cm for the horizontal and vertical directions. The absolute accuracy is determined using a point-based evaluation, which results in the RMSE being <2 cm for the horizontal direction and <4 cm for the vertical direction, compared to the TLS reference. The results are consistent for the two different study areas with similar evaluation approaches but different flight planning and processing. In addition, the influence of different Global Navigation Satellite System (GNSS) master stations is investigated and no significant difference was found between Virtual Reference Stations (VRS) and a dedicated master station. Furthermore, to control the orientation of the point cloud, a parameter-based analysis using planes in object space was performed, which showed a good agreement with the reference within the noise level of the point cloud. The calculated quality parameters are all smaller than the manufacturer’s specifications and can be transferred to other multi-sensor systems.

    @Article{rs13183564,
    author = {Dreier, Ansgar and Janßen, Jannik and Kuhlmann, Heiner and Klingbeil, Lasse},
    title = {Quality Analysis of Direct Georeferencing in Aspects of Absolute Accuracy and Precision for a UAV-Based Laser Scanning System},
    journal = {Remote Sensing},
    volume = {13},
    year = {2021},
    number = {18},
    article-number= {3564},
    url = {https://www.mdpi.com/2072-4292/13/18/3564},
    issn = {2072-4292},
    abstract = {The use of UAV-based laser scanning systems is increasing due to the rapid development in sensor technology, especially in applications such as topographic surveys or forestry. One advantage of these multi-sensor systems is the possibility of direct georeferencing of the derived 3D point clouds in a global reference frame without additional information from Ground Control Points (GCPs). This paper addresses the quality analysis of direct georeferencing of a UAV-based laser scanning system focusing on the absolute accuracy and precision of the system. The system investigated is based on the RIEGL miniVUX-SYS and the evaluation uses the estimated point clouds compared to a reference point cloud from Terrestrial Laser Scanning (TLS) for two different study areas. The precision is estimated by multiple repetitions of the same measurement and the use of artificial objects, such as targets and tables, resulting in a standard deviation of <1.2 cm for the horizontal and vertical directions. The absolute accuracy is determined using a point-based evaluation, which results in the RMSE being <2 cm for the horizontal direction and <4 cm for the vertical direction, compared to the TLS reference. The results are consistent for the two different study areas with similar evaluation approaches but different flight planning and processing. In addition, the influence of different Global Navigation Satellite System (GNSS) master stations is investigated and no significant difference was found between Virtual Reference Stations (VRS) and a dedicated master station. Furthermore, to control the orientation of the point cloud, a parameter-based analysis using planes in object space was performed, which showed a good agreement with the reference within the noise level of the point cloud. The calculated quality parameters are all smaller than the manufacturer’s specifications and can be transferred to other multi-sensor systems.},
    doi = {10.3390/rs13183564},
    }

  • L. Drees, L. V. Junker-Frohn, J. Kierdorf, and R. Roscher, "Temporal prediction and evaluation of Brassica growth in the field using conditional generative adversarial networks," Computers and Electronics in Agriculture, vol. 190, p. 106415, 2021. doi:https://doi.org/10.1016/j.compag.2021.106415
    [BibTeX] [PDF]

    Farmers frequently assess plant growth and performance as basis for making decisions when to take action in the field, such as fertilization, weed control, or harvesting. The prediction of plant growth is a major challenge, as it is affected by numerous and highly variable environmental factors. This paper proposes a novel monitoring approach that comprises high-throughput imaging sensor measurements and their automatic analysis to predict future plant growth. Our approach’s core is a novel machine learning-based generative growth model based on conditional generative adversarial networks, which is able to predict the future appearance of individual plants. In experiments with RGB time series images of laboratory-grown Arabidopsis thaliana and field-grown cauliflower plants, we show that our approach produces realistic, reliable, and reasonable images of future growth stages. The automatic interpretation of the generated images through neural network-based instance segmentation allows the derivation of various phenotypic traits that describe plant growth.

    @Article{drees2021106415,
    title = {Temporal prediction and evaluation of Brassica growth in the field using conditional generative adversarial networks},
    journal = {Computers and Electronics in Agriculture},
    volume = {190},
    pages = {106415},
    year = {2021},
    issn = {0168-1699},
    doi = {https://doi.org/10.1016/j.compag.2021.106415},
    url = {https://www.sciencedirect.com/science/article/pii/S0168169921004324},
    author = {Lukas Drees and Laura Verena Junker-Frohn and Jana Kierdorf and Ribana Roscher},
    keywords = {Generative adversarial networks, Agriculture, Cauliflower, Prediction, Plant growth},
    abstract = {Farmers frequently assess plant growth and performance as basis for making decisions when to take action in the field, such as fertilization, weed control, or harvesting. The prediction of plant growth is a major challenge, as it is affected by numerous and highly variable environmental factors. This paper proposes a novel monitoring approach that comprises high-throughput imaging sensor measurements and their automatic analysis to predict future plant growth. Our approach’s core is a novel machine learning-based generative growth model based on conditional generative adversarial networks, which is able to predict the future appearance of individual plants. In experiments with RGB time series images of laboratory-grown Arabidopsis thaliana and field-grown cauliflower plants, we show that our approach produces realistic, reliable, and reasonable images of future growth stages. The automatic interpretation of the generated images through neural network-based instance segmentation allows the derivation of various phenotypic traits that describe plant growth.},
    }

  • B. Siegmann, M. P. Cendrero-Mateo, S. Cogliati, A. Damm, J. Gamon, D. Herrera, C. Jedmowski, L. V. Junker-Frohn, T. Kraska, O. Muller, P. Rademske, C. van der Tol, J. Quiros-Vargas, P. Yang, and U. Rascher, "Downscaling of far-red solar-induced chlorophyll fluorescence of different crops from canopy to leaf level using a diurnal data set acquired by the airborne imaging spectrometer HyPlant," Remote Sensing of Environment, vol. 264, p. 112609, 2021. doi:https://doi.org/10.1016/j.rse.2021.112609
    [BibTeX] [PDF]

    Remote sensing-based measurements of solar-induced chlorophyll fluorescence (SIF) are useful for assessing plant functioning at different spatial and temporal scales. SIF is the most direct measure of photosynthesis and is therefore considered important to advance capacity for the monitoring of gross primary production (GPP) while it has also been suggested that its yield facilitates the early detection of vegetation stress. However, due to the influence of different confounding effects, the apparent SIF signal measured at canopy level differs from the fluorescence emitted at leaf level, which makes its physiological interpretation challenging. One of these effects is the scattering of SIF emitted from leaves on its way through the canopy. The escape fraction (fesc) describes the scattering of SIF within the canopy and corresponds to the ratio of apparent SIF at canopy level to SIF at leaf level. In the present study, the fluorescence correction vegetation index (FCVI) was used to determine fesc of far-red SIF for three structurally different crops (sugar beet, winter wheat, and fruit trees) from a diurnal data set recorded by the airborne imaging spectrometer HyPlant. This unique data set, for the first time, allowed a joint analysis of spatial and temporal dynamics of structural effects and thus the downscaling of far-red SIF from canopy (SIF760canopy) to leaf level (SIF760leaf). For a homogeneous crop such as winter wheat, it seems to be sufficient to determine fesc once a day to reliably scale SIF760 from canopy to leaf level. In contrast, for more complex canopies such as fruit trees, calculating fesc for each observation time throughout the day is strongly recommended. The compensation for structural effects, in combination with normalizing SIF760 to remove the effect of incoming radiation, further allowed the estimation of SIF emission efficiency (εSIF) at leaf level, a parameter directly related to the diurnal variations of plant photosynthetic efficiency.

    @Article{siegmann2021112609,
    title = {Downscaling of far-red solar-induced chlorophyll fluorescence of different crops from canopy to leaf level using a diurnal data set acquired by the airborne imaging spectrometer HyPlant},
    journal = {Remote Sensing of Environment},
    volume = {264},
    pages = {112609},
    year = {2021},
    issn = {0034-4257},
    doi = {https://doi.org/10.1016/j.rse.2021.112609},
    url = {https://www.sciencedirect.com/science/article/pii/S0034425721003291},
    author = {Bastian Siegmann and Maria Pilar Cendrero-Mateo and Sergio Cogliati and Alexander Damm and John Gamon and David Herrera and Christoph Jedmowski and Laura Verena Junker-Frohn and Thorsten Kraska and Onno Muller and Patrick Rademske and Christiaan {van der Tol} and Juan Quiros-Vargas and Peiqi Yang and Uwe Rascher},
    keywords = {Solar-induced chlorophyll fluorescence, SIF, HyPlant, Diurnal course, Fluorescence correction vegetation index, FCVI, Fluorescence escape fraction, Photosynthetically active radiation},
    abstract = {Remote sensing-based measurements of solar-induced chlorophyll fluorescence (SIF) are useful for assessing plant functioning at different spatial and temporal scales. SIF is the most direct measure of photosynthesis and is therefore considered important to advance capacity for the monitoring of gross primary production (GPP) while it has also been suggested that its yield facilitates the early detection of vegetation stress. However, due to the influence of different confounding effects, the apparent SIF signal measured at canopy level differs from the fluorescence emitted at leaf level, which makes its physiological interpretation challenging. One of these effects is the scattering of SIF emitted from leaves on its way through the canopy. The escape fraction (fesc) describes the scattering of SIF within the canopy and corresponds to the ratio of apparent SIF at canopy level to SIF at leaf level. In the present study, the fluorescence correction vegetation index (FCVI) was used to determine fesc of far-red SIF for three structurally different crops (sugar beet, winter wheat, and fruit trees) from a diurnal data set recorded by the airborne imaging spectrometer HyPlant. This unique data set, for the first time, allowed a joint analysis of spatial and temporal dynamics of structural effects and thus the downscaling of far-red SIF from canopy (SIF760canopy) to leaf level (SIF760leaf). For a homogeneous crop such as winter wheat, it seems to be sufficient to determine fesc once a day to reliably scale SIF760 from canopy to leaf level. In contrast, for more complex canopies such as fruit trees, calculating fesc for each observation time throughout the day is strongly recommended. The compensation for structural effects, in combination with normalizing SIF760 to remove the effect of incoming radiation, further allowed the estimation of SIF emission efficiency (εSIF) at leaf level, a parameter directly related to the diurnal variations of plant photosynthetic efficiency.},
    }

  • A. Ahmadi, M. Halstead, and C. McCool, "Virtual Temporal Samples for Recurrent Neural Networks: applied to semantic segmentation in agriculture," in Pattern Recognition: 43rd DAGM German Conference , 2021, pp. 574-588.
    [BibTeX]
    @InProceedings{ahmadi2021virtual,
    title = {Virtual Temporal Samples for Recurrent Neural Networks: applied to semantic segmentation in agriculture},
    booktitle = {Pattern Recognition: 43rd DAGM German Conference},
    author = {Alireza Ahmadi and Michael Halstead and Chris McCool},
    year = {2021},
    pages = {574-588},
    }

  • A. Barreto, P. Lottes, F. R. Ispizua Yamati, S. Baumgarten, N. A. Wolf, C. Stachniss, A. Mahlein, and S. Paulus, "Automatic UAV-based counting of seedlings in sugar-beet field and extension to maize and strawberry," Computers and Electronics in Agriculture, vol. 191, p. 106493, 2021. doi:https://doi.org/10.1016/j.compag.2021.106493
    [BibTeX] [PDF]

    Counting crop seedlings is a time-demanding activity involved in diverse agricultural practices like plant cultivating, experimental trials, plant breeding procedures, and weed control. Unmanned Aerial Vehicles (UAVs) carrying RGB cameras are novel tools for automatic field mapping, and the analysis of UAV images by deep learning methods can provide relevant agronomic information. UAV-based camera systems and a deep learning image analysis pipeline are implemented for a fully automated plant counting in sugar beet, maize, and strawberry fields in the present study. Five locations were monitored at different growth stages, and the crop number per plot was automatically predicted by using a fully convolutional network (FCN) pipeline. Our FCN-based approach is a single model for jointly determining both the exact stem location of crop and weed plants and a pixel-wise plant classification considering crop, weed, and soil. To determinate the approach performance, predicted crop counting was compared to visually assessed ground truth data. Results show that UAV-based counting of sugar-beet plants delivers forecast errors lower than 4.6%, and the main factors for performance are related to the intra-row distance and the growth stage. The pipeline’s extension to other crops is possible; the errors of the predictions are lower than 4% under practical field conditions for maize and strawberry fields. This work highlight the feasibility of automatic crop counting, which can reduce manual effort to the farmers.

    @Article{barreto2021106493,
    title = {Automatic UAV-based counting of seedlings in sugar-beet field and extension to maize and strawberry},
    journal = {Computers and Electronics in Agriculture},
    volume = {191},
    pages = {106493},
    year = {2021},
    issn = {0168-1699},
    doi = {https://doi.org/10.1016/j.compag.2021.106493},
    url = {https://www.sciencedirect.com/science/article/pii/S016816992100510X},
    author = {Abel Barreto and Philipp Lottes and Facundo Ramón {Ispizua Yamati} and Stephen Baumgarten and Nina Anastasia Wolf and Cyrill Stachniss and Anne-Katrin Mahlein and Stefan Paulus},
    keywords = {Deep learning, FCN, UAV, Sugar beet, Plant segmentation, Time-series, Intra-row distance, Growth stage},
    abstract = {Counting crop seedlings is a time-demanding activity involved in diverse agricultural practices like plant cultivating, experimental trials, plant breeding procedures, and weed control. Unmanned Aerial Vehicles (UAVs) carrying RGB cameras are novel tools for automatic field mapping, and the analysis of UAV images by deep learning methods can provide relevant agronomic information. UAV-based camera systems and a deep learning image analysis pipeline are implemented for a fully automated plant counting in sugar beet, maize, and strawberry fields in the present study. Five locations were monitored at different growth stages, and the crop number per plot was automatically predicted by using a fully convolutional network (FCN) pipeline. Our FCN-based approach is a single model for jointly determining both the exact stem location of crop and weed plants and a pixel-wise plant classification considering crop, weed, and soil. To determinate the approach performance, predicted crop counting was compared to visually assessed ground truth data. Results show that UAV-based counting of sugar-beet plants delivers forecast errors lower than 4.6%, and the main factors for performance are related to the intra-row distance and the growth stage. The pipeline’s extension to other crops is possible; the errors of the predictions are lower than 4% under practical field conditions for maize and strawberry fields. This work highlight the feasibility of automatic crop counting, which can reduce manual effort to the farmers.},
    }

  • P. Welke, F. Alkhoury, C. Bauckhage, and S. Wrobel, "Decision Snippet Features," in 2020 25th International Conference on Pattern Recognition (ICPR) , 2021, pp. 4260-4267. doi:10.1109/ICPR48806.2021.9412025
    [BibTeX]
    @InProceedings{9412025,
    author = {Welke, Pascal and Alkhoury, Fouad and Bauckhage, Christian and Wrobel, Stefan},
    booktitle = {2020 25th International Conference on Pattern Recognition (ICPR)},
    title = {Decision Snippet Features},
    year = {2021},
    volume = {},
    number = {},
    pages = {4260-4267},
    doi = {10.1109/ICPR48806.2021.9412025},
    }

  • T. Zaenker, C. Lehnert, C. McCool, and M. Bennewitz, "Combining Local and Global Viewpoint Planning for Fruit Coverage," in Proc.~of the European Conference on Mobile Robots (ECMR) , 2021.
    [BibTeX]
    @InProceedings{zaenker21ecmr,
    author = {T. Zaenker and C. Lehnert and C. McCool and M. Bennewitz},
    title = {Combining Local and Global Viewpoint Planning for Fruit Coverage},
    booktitle = {Proc.~of the European Conference on Mobile Robots (ECMR)},
    year = 2021,
    }

  • N. Dengler, T. Zaenker, F. Verdoja, and M. Bennewitz, "Online Object-Oriented Semantic Mapping and Map Updating," in Proc.~of the European Conference on Mobile Robots (ECMR) , 2021.
    [BibTeX]
    @InProceedings{dengler21ecmr,
    author = {N. Dengler and T. Zaenker and F. Verdoja and M. Bennewitz},
    title = {Online Object-Oriented Semantic Mapping and Map Updating},
    booktitle = {Proc.~of the European Conference on Mobile Robots (ECMR)},
    year = 2021,
    }

  • T. Zaenker, C. Smitt, C. McCool, and M. Bennewitz, "Viewpoint Planning for Fruit Size and Position Estimation," in Proc.~of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) , 2021.
    [BibTeX]
    @InProceedings{zaenker21iros,
    author = {T. Zaenker and C. Smitt and C. McCool and M. Bennewitz},
    title = {Viewpoint Planning for Fruit Size and Position Estimation},
    booktitle = {Proc.~of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
    year = 2021,
    }

  • C. Smitt, M. Halstead, T. Zaenker, M. Bennewitz, and C. McCool, "PATHoBot: A Robot for Glasshouse Crop Phenotyping and Intervention," in Proc.~of the IEEE International Conference on Robotics & Automation (ICRA) , 2021.
    [BibTeX]
    @InProceedings{mccool21icra,
    author = {C. Smitt and M. Halstead and T. Zaenker and M. Bennewitz and C. McCool},
    title = {{PATHoBot}: {A} Robot for Glasshouse Crop Phenotyping and Intervention},
    booktitle = {Proc.~of the IEEE International Conference on Robotics \& Automation (ICRA)},
    year = 2021,
    }

  • A. Bonerath, J. Haunert, J. S. B. Mitchell, and B. Niedermann, "Shortcut Hulls: Vertex-restricted Outer Simplifications of Polygons," in Proceedings of the 33rd Canadian Conference on Computational Geometry , 2021, pp. 12-23.
    [BibTeX] [PDF]
    @InProceedings{bhmn_2021,
    author = {Annika Bonerath and Jan-Henrik Haunert and Joseph S. B. Mitchell and Benjamin Niedermann},
    booktitle = {Proceedings of the 33rd Canadian Conference on Computational Geometry},
    editor = {Meng He and Don Sheehy},
    pages = {12-23},
    title = {Shortcut Hulls: Vertex-restricted Outer Simplifications of Polygons},
    url = {https://projects.cs.dal.ca/cccg2021/wordpress/wp-content/uploads/2021/08/CCCG2021.pdf},
    year = {2021},
    }

  • R. Baatz, H. J. Hendricks Franssen, E. Euskirchen, D. Sihi, M. Dietze, S. Ciavatta, K. Fennel, H. Beck, G. De Lannoy, V. R. N. Pauwels, A. Raiho, C. Montzka, M. Williams, U. Mishra, C. Poppe, S. Zacharias, A. Lausch, L. Samaniego, K. Van Looy, H. Bogena, M. Adamescu, M. Mirtl, A. Fox, K. Goergen, B. S. Naz, Y. Zeng, and H. Vereecken, "Reanalysis in Earth System Science: Toward Terrestrial Ecosystem Reanalysis," Reviews of Geophysics, vol. 59, iss. 3, p. e2020RG000715, 2021. doi:https://doi.org/10.1029/2020RG000715
    [BibTeX] [PDF]

    Abstract A reanalysis is a physically consistent set of optimally merged simulated model states and historical observational data, using data assimilation. High computational costs for modeled processes and assimilation algorithms has led to Earth system specific reanalysis products for the atmosphere, the ocean and the land separately. Recent developments include the advanced uncertainty quantification and the generation of biogeochemical reanalysis for land and ocean. Here, we review atmospheric and oceanic reanalyzes, and more in detail biogeochemical ocean and terrestrial reanalyzes. In particular, we identify land surface, hydrologic and carbon cycle reanalyzes which are nowadays produced in targeted projects for very specific purposes. Although a future joint reanalysis of land surface, hydrologic, and carbon processes represents an analysis of important ecosystem variables, biotic ecosystem variables are assimilated only to a very limited extent. Continuous data sets of ecosystem variables are needed to explore biotic-abiotic interactions and the response of ecosystems to global change. Based on the review of existing achievements, we identify five major steps required to develop terrestrial ecosystem reanalysis to deliver continuous data streams on ecosystem dynamics.

    @Article{https://doi.org/10.1029/2020rg000715,
    author = {Baatz, R. and Hendricks Franssen, H. J. and Euskirchen, E. and Sihi, D. and Dietze, M. and Ciavatta, S. and Fennel, K. and Beck, H. and De Lannoy, G. and Pauwels, V. R. N. and Raiho, A. and Montzka, C. and Williams, M. and Mishra, U. and Poppe, C. and Zacharias, S. and Lausch, A. and Samaniego, L. and Van Looy, K. and Bogena, H. and Adamescu, M. and Mirtl, M. and Fox, A. and Goergen, K. and Naz, B. S. and Zeng, Y. and Vereecken, H.},
    title = {Reanalysis in Earth System Science: Toward Terrestrial Ecosystem Reanalysis},
    journal = {Reviews of Geophysics},
    volume = {59},
    number = {3},
    pages = {e2020RG000715},
    keywords = {reanalysis, ecosystem reanalysis, land surface reanalysis, data assimilation, hydrologic reanalysis, carbon cycle reanalysis},
    doi = {https://doi.org/10.1029/2020RG000715},
    url = {https://agupubs.onlinelibrary.wiley.com/doi/abs/10.1029/2020RG000715},
    eprint = {https://agupubs.onlinelibrary.wiley.com/doi/pdf/10.1029/2020RG000715},
    note = {e2020RG000715 2020RG000715},
    abstract = {Abstract A reanalysis is a physically consistent set of optimally merged simulated model states and historical observational data, using data assimilation. High computational costs for modeled processes and assimilation algorithms has led to Earth system specific reanalysis products for the atmosphere, the ocean and the land separately. Recent developments include the advanced uncertainty quantification and the generation of biogeochemical reanalysis for land and ocean. Here, we review atmospheric and oceanic reanalyzes, and more in detail biogeochemical ocean and terrestrial reanalyzes. In particular, we identify land surface, hydrologic and carbon cycle reanalyzes which are nowadays produced in targeted projects for very specific purposes. Although a future joint reanalysis of land surface, hydrologic, and carbon processes represents an analysis of important ecosystem variables, biotic ecosystem variables are assimilated only to a very limited extent. Continuous data sets of ecosystem variables are needed to explore biotic-abiotic interactions and the response of ecosystems to global change. Based on the review of existing achievements, we identify five major steps required to develop terrestrial ecosystem reanalysis to deliver continuous data streams on ecosystem dynamics.},
    year = {2021},
    }

  • D. Schunck, F. Magistri, R. A. Rosu, A. Cornelißen, N. Chebrolu, S. Paulus, L. J. S. an d Behnke, C. Stachniss, H. Kuhlmann, and L. Klingbeil, "Pheno4D: A spatio-temporal dataset of maize and tomato plant point clouds for phenotyping and advanced plant analysis ," PLOS ONE, vol. 16, iss. 8, pp. 1-18, 2021. doi:10.1371/journal.pone.0256340
    [BibTeX] [PDF]

    Understanding the growth and development of individual plants is of central importance in modern agriculture, crop breeding, and crop science. To this end, using 3D data for plant analysis has gained attention over the last years. High-resolution point clouds offer the potential to derive a variety of plant traits, such as plant height, biomass, as well as the number and size of relevant plant organs. Periodically scanning the plants even allows for performing spatio-temporal growth analysis. However, highly accurate 3D point clouds from plants recorded at different growth stages are rare, and acquiring this kind of data is costly. Besides, advanced plant analysis methods from machine learning require annotated training data and thus generate intense manual labor before being able to perform an analysis. To address these issues, we present with this dataset paper a multi-temporal dataset featuring high-resolution registered point clouds of maize and tomato plants, which we manually labeled for computer vision tasks, such as for instance segmentation and 3D reconstruction, providing approximately 260 million labeled 3D points. To highlight the usability of the data and to provide baselines for other researchers, we show a variety of applications ranging from point cloud segmentation to non-rigid registration and surface reconstruction. We believe that our dataset will help to develop new algorithms to advance the research for plant phenotyping, 3D reconstruction, non-rigid registration, and deep learning on raw point clouds. The dataset is freely accessible at https://www.ipb.uni-bonn.de/data/pheno4d/.

    @Article{schunck2021plosone,
    author = {D. Schunck and F. Magistri and R.A. Rosu and A. Corneli{\ss}en and N. Chebrolu and S. Paulus and J. L\'eon an\ d S. Behnke and C. Stachniss and H. Kuhlmann and L. Klingbeil},
    title = {{Pheno4D: A spatio-temporal dataset of maize and tomato plant point clouds for phenotyping and advanced plant analysis }},
    journal = {PLOS ONE},
    publisher = {Public Library of Science},
    year = 2021,
    month = {08},
    volume = {16},
    url = {https://doi.org/10.1371/journal.pone.0256340},
    pages = {1-18},
    abstract = {Understanding the growth and development of individual plants is of central importance in modern agriculture, crop breeding, and crop science. To this end, using 3D data for plant analysis has gained attention over the last years. High-resolution point clouds offer the potential to derive a variety of plant traits, such as plant height, biomass, as well as the number and size of relevant plant organs. Periodically scanning the plants even allows for performing spatio-temporal growth analysis. However, highly accurate 3D point clouds from plants recorded at different growth stages are rare, and acquiring this kind of data is costly. Besides, advanced plant analysis methods from machine learning require annotated training data and thus generate intense manual labor before being able to perform an analysis. To address these issues, we present with this dataset paper a multi-temporal dataset featuring high-resolution registered point clouds of maize and tomato plants, which we manually labeled for computer vision tasks, such as for instance segmentation and 3D reconstruction, providing approximately 260 million labeled 3D points. To highlight the usability of the data and to provide baselines for other researchers, we show a variety of applications ranging from point cloud segmentation to non-rigid registration and surface reconstruction. We believe that our dataset will help to develop new algorithms to advance the research for plant phenotyping, 3D reconstruction, non-rigid registration, and deep learning on raw point clouds. The dataset is freely accessible at https://www.ipb.uni-bonn.de/data/pheno4d/.},
    number = {8},
    doi = {10.1371/journal.pone.0256340},
    }

  • A. T. Leite-Filho, B. S. Soares-Filho, J. L. Davis, G. M. Abrahão, and J. Börner, "Deforestation reduces rainfall and agricultural revenues in the Brazilian Amazon," Nature Communications, vol. 12, iss. 1, p. 2591, 2021. doi:10.1038/s41467-021-22840-7
    [BibTeX] [PDF]

    It has been suggested that rainfall in the Amazon decreases if forest loss exceeds some threshold, but the specific value of this threshold remains uncertain. Here, we investigate the relationship between historical deforestation and rainfall at different geographical scales across the Southern Brazilian Amazon (SBA). We also assess impacts of deforestation policy scenarios on the region's agriculture. Forest loss of up to 55–60{\%} within 28 km grid cells enhances rainfall, but further deforestation reduces rainfall precipitously. This threshold is lower at larger scales (45–50{\%} at 56 km and 25–30{\%} at 112 km grid cells), while rainfall decreases linearly within 224 km grid cells. Widespread deforestation results in a hydrological and economic negative-sum game, because lower rainfall and agricultural productivity at larger scales outdo local gains. Under a weak governance scenario, SBA may lose 56{\%} of its forests by 2050. Reducing deforestation prevents agricultural losses in SBA up to US{\$} 1 billion annually.

    @Article{312312312312,
    abstract = {It has been suggested that rainfall in the Amazon decreases if forest loss exceeds some threshold, but the specific value of this threshold remains uncertain. Here, we investigate the relationship between historical deforestation and rainfall at different geographical scales across the Southern Brazilian Amazon (SBA). We also assess impacts of deforestation policy scenarios on the region's agriculture. Forest loss of up to 55--60{\%} within 28 km grid cells enhances rainfall, but further deforestation reduces rainfall precipitously. This threshold is lower at larger scales (45--50{\%} at 56 km and 25--30{\%} at 112 km grid cells), while rainfall decreases linearly within 224 km grid cells. Widespread deforestation results in a hydrological and economic negative-sum game, because lower rainfall and agricultural productivity at larger scales outdo local gains. Under a weak governance scenario, SBA may lose 56{\%} of its forests by 2050. Reducing deforestation prevents agricultural losses in SBA up to US{\$} 1 billion annually.},
    author = {Leite-Filho, Argemiro Teixeira and Soares-Filho, Britaldo Silveira and Davis, Juliana Leroy and Abrah{\~a}o, Gabriel Medeiros and B{\"o}rner, Jan},
    da = {2021/05/10},
    date-added = {2021-06-20 19:44:53 +0000},
    date-modified = {2021-06-20 19:44:53 +0000},
    doi = {10.1038/s41467-021-22840-7},
    id = {Leite-Filho2021},
    isbn = {2041-1723},
    journal = {Nature Communications},
    number = {1},
    pages = {2591},
    title = {Deforestation reduces rainfall and agricultural revenues in the Brazilian Amazon},
    ty = {JOUR},
    url = {https://doi.org/10.1038/s41467-021-22840-7},
    volume = {12},
    year = {2021},
    }

  • D. Schulz, H. Yin, B. Tischbein, S. Verleysdonk, R. Adamou, and N. Kumar, "Land use mapping using Sentinel-1 and Sentinel-2 time series in a heterogeneous landscape in Niger, Sahel," ISPRS Journal of Photogrammetry and Remote Sensing, vol. 178, pp. 97-111, 2021. doi:https://doi.org/10.1016/j.isprsjprs.2021.06.005
    [BibTeX] [PDF]

    Land use maps describe the spatial distribution of natural resources, cultural landscapes, and human settlements, serving as an important planning tool for decision makers. In the Sahel area, such information is valuable for risk management and mitigation in challenging sectors like food security, flood control, and urban planning. Due to its uniform quality across large areas in regular time steps, remote sensing imageries are essential input for producing land use maps. However, spatially and temporally heterogeneous landscapes in Sahel make classification of landscape features difficult. Our overall goal is to create an accurate, high resolution land use map covering Niamey, the capital of Niger and its surroundings which represents the unique landscape features in the Sahel using Sentinel-1 and Sentinel-2 archives. We assessed the performance of three commonly used classifiers (i.e. Maximum Likelihood (ML), Support Vector Machine (SVM) and Random Forest (RF)) for land use classification. To understand the utility of different features from Sentinel-1 and Sentinel-2 imagery for classification, we performed feature selection and compared mapping accuracies with and without feature selection. To leverage the strength of each classifier, we developed a classifier ensemble (CE) map based on the mapping accuracy of each land use class and each classifier. The results of this study showed that the performance of individual classifiers depends on feature selection method and accuracies can be improved by combining different classifiers. The ensemble map had an overall accuracy of 72+-3.9 percent and it was found superior in terms of accuracy particularly with respect to built-up areas compared to the existing global land cover products in the study area. Our classification scheme also better characterized the regional environment in the Sahel. For example, we mapped rice and bare rocks that have important regional implication, which are not included in the existing products. Overall, our approach highlights the potentiality of combining multi-modal imageries and multiple classifiers for mapping a heterogenous environment such as the Sahel with high spatial resolution.

    @Article{schulz202197,
    title = {Land use mapping using Sentinel-1 and Sentinel-2 time series in a heterogeneous landscape in Niger, Sahel},
    journal = {ISPRS Journal of Photogrammetry and Remote Sensing},
    volume = {178},
    pages = {97-111},
    year = {2021},
    issn = {0924-2716},
    doi = {https://doi.org/10.1016/j.isprsjprs.2021.06.005},
    url = {https://www.sciencedirect.com/science/article/pii/S0924271621001635},
    author = {Dario Schulz and He Yin and Bernhard Tischbein and Sarah Verleysdonk and Rabani Adamou and Navneet Kumar},
    keywords = {Classifier ensemble, Land cover, Feature selection, West Africa, Seasonal change},
    abstract = {Land use maps describe the spatial distribution of natural resources, cultural landscapes, and human settlements, serving as an important planning tool for decision makers. In the Sahel area, such information is valuable for risk management and mitigation in challenging sectors like food security, flood control, and urban planning. Due to its uniform quality across large areas in regular time steps, remote sensing imageries are essential input for producing land use maps. However, spatially and temporally heterogeneous landscapes in Sahel make classification of landscape features difficult. Our overall goal is to create an accurate, high resolution land use map covering Niamey, the capital of Niger and its surroundings which represents the unique landscape features in the Sahel using Sentinel-1 and Sentinel-2 archives. We assessed the performance of three commonly used classifiers (i.e. Maximum Likelihood (ML), Support Vector Machine (SVM) and Random Forest (RF)) for land use classification. To understand the utility of different features from Sentinel-1 and Sentinel-2 imagery for classification, we performed feature selection and compared mapping accuracies with and without feature selection. To leverage the strength of each classifier, we developed a classifier ensemble (CE) map based on the mapping accuracy of each land use class and each classifier. The results of this study showed that the performance of individual classifiers depends on feature selection method and accuracies can be improved by combining different classifiers. The ensemble map had an overall accuracy of 72+-3.9 percent and it was found superior in terms of accuracy particularly with respect to built-up areas compared to the existing global land cover products in the study area. Our classification scheme also better characterized the regional environment in the Sahel. For example, we mapped rice and bare rocks that have important regional implication, which are not included in the existing products. Overall, our approach highlights the potentiality of combining multi-modal imageries and multiple classifiers for mapping a heterogenous environment such as the Sahel with high spatial resolution.},
    }

  • M. Gerullis, T. Heckelei, and S. Rasch, "Toward understanding the governance of varietal and genetic diversity," Ecology and Society, vol. 26, iss. 2, 2021. doi:10.5751/ES-12333-260228
    [BibTeX]
    @Article{gerullis2021toward,
    title = {Toward understanding the governance of varietal and genetic diversity},
    author = {Gerullis, Maria and Heckelei, Thomas and Rasch, Sebastian},
    journal = {Ecology and Society},
    volume = {26},
    number = {2},
    year = {2021},
    publisher = {The Resilience Alliance},
    doi = {10.5751/ES-12333-260228},
    }

  • A. Brugger, P. Schramowski, S. Paulus, U. Steiner, K. Kersting, and A. Mahlein, "Spectral signatures in the UV-range can be combined with secondary plant metabolites by deep learning to characterise barley – powdery mildew interaction," Plant Pathology, 2021. doi:https://doi.org/10.1111/ppa.13411
    [BibTeX] [PDF]
    @Article{bruggerpp,
    author = {Brugger, Anna and Schramowski, Patrick and Paulus, Stefan and Steiner, Ulrike and Kersting, Kristian and Mahlein, Anne-Katrin},
    year = 2021,
    title = {Spectral signatures in the UV-range can be combined with secondary plant metabolites by deep learning to characterise barley – powdery mildew interaction},
    journal = {Plant Pathology},
    keywords = {Blumeria graminis f.sp. hordei, deep learning, Hordeum vulgare, Hyperspectral imaging, secondary plant metabolites, UV-range},
    doi = {https://doi.org/10.1111/ppa.13411},
    url = {https://bsppjournals.onlinelibrary.wiley.com/doi/abs/10.1111/ppa.13411},
    eprint = {https://bsppjournals.onlinelibrary.wiley.com/doi/pdf/10.1111/ppa.13411},
    }

  • T. A. Marton and H. Storm, "The case of organic dairy conversion in Norway: Assessment of multivariate neighbourhood effects," Q Open, vol. 1, iss. 1, 2021. doi:10.1093/qopen/qoab009
    [BibTeX] [PDF]

    {This study examines the impact of neighbourhood effects and individual farm characteristics on the decision process of organic dairy conversion in Norway, using a unique, spatially explicit farm-level panel set comprising information at the population level from 2003 to 2015. Our results reveal a positive spatial spillover of neighbouring conversion, confirming previous findings. Additionally, we demonstrate that neighbouring organic dairy reversion (i.e. switching back to conventional dairy farming) and organic dairy exits (ceasing to farm altogether) exert notable negative spatial spillovers on organic conversion decisions that have not yet been shown in the literature. If organic dairy production is an important policy goal, such negative spatial spillover requires consideration within policy design and extension.}

    @Article{10.1093-qopen-qoab009,
    author = {Marton, Tibor A and Storm, Hugo},
    title = "The case of organic dairy conversion in Norway: Assessment of multivariate neighbourhood effects",
    journal = {Q Open},
    volume = {1},
    number = {1},
    year = {2021},
    month = {05},
    abstract = "{This study examines the impact of neighbourhood effects and individual farm characteristics on the decision process of organic dairy conversion in Norway, using a unique, spatially explicit farm-level panel set comprising information at the population level from 2003 to 2015. Our results reveal a positive spatial spillover of neighbouring conversion, confirming previous findings. Additionally, we demonstrate that neighbouring organic dairy reversion (i.e. switching back to conventional dairy farming) and organic dairy exits (ceasing to farm altogether) exert notable negative spatial spillovers on organic conversion decisions that have not yet been shown in the literature. If organic dairy production is an important policy goal, such negative spatial spillover requires consideration within policy design and extension.}",
    issn = {2633-9048},
    doi = {10.1093/qopen/qoab009},
    url = {https://doi.org/10.1093/qopen/qoab009},
    note = {qoab009},
    eprint = {https://academic.oup.com/qopen/article-pdf/1/1/qoab009/38391645/qoab009.pdf},
    }

  • S. J. Seidel, T. Gaiser, H. E. Ahrends, H. Hüging, S. Siebert, S. L. Bauke, M. I. Gocke, M. Koch, K. Schweitzer, G. Schaaf, and F. Ewert, "Crop response to P fertilizer omission under a changing climate - Experimental and modeling results over 115 years of a long-term fertilizer experiment," Field Crops Research, vol. 268, p. 108174, 2021. doi:https://doi.org/10.1016/j.fcr.2021.108174
    [BibTeX] [PDF]
    @Article{seidel2021108174,
    title = {Crop response to P fertilizer omission under a changing climate - Experimental and modeling results over 115 years of a long-term fertilizer experiment},
    journal = {Field Crops Research},
    volume = {268},
    pages = {108174},
    year = {2021},
    issn = {0378-4290},
    doi = {https://doi.org/10.1016/j.fcr.2021.108174},
    url = {https://www.sciencedirect.com/science/article/pii/S0378429021001209},
    author = {S.J. Seidel and T. Gaiser and H.E. Ahrends and H. Hüging and S. Siebert and S.L. Bauke and M.I. Gocke and M. Koch and K. Schweitzer and G. Schaaf and F. Ewert},
    keywords = {Long-term field experiment, Climate change, Nutrient availability, Soil phosphorus simulation, Crop modeling},
    }

  • O. Spykman, A. Gabriel, M. Ptacek, and M. Gandorfer, "Farmers’ perspectives on field crop robots – Evidence from Bavaria, Germany," Computers and Electronics in Agriculture, vol. 186, p. 106176, 2021. doi:https://doi.org/10.1016/j.compag.2021.106176
    [BibTeX] [PDF]

    Farmers’ attitudes toward field crop robots in a European setting have hardly been studied despite an increasing availability of the technology. Given the relevance of robots for small-scale agriculture, however, their acceptability in regions dominated by small-scale agriculture such as Bavaria, Germany, is of particular interest. Based on a sample of 174 farmers, an exploratory investigation of factors influencing the preference for large or small field crop robots in general and in specific settings and for mode of operation was carried out. Data were gathered using questionnaires at two events including lectures and field demonstrations and analyzed using bivariate tests. Farm size, farming system (organic/conventional), and occupational structure (part-time/full-time) were relevant attributes influencing the evaluation of advantages and disadvantages of field crop robots. Generally, respondents from larger farms focus more on financial benefits from robots and prefer large autonomous tractors. Conversely, small-scale or organic farmers consider environmental benefits of field crop robots relatively more important and favor small robots. Organic farming also positively correlates with the intent to purchase field crop robots within the next five years. More farmers can generally imagine owning small robots as opposed to an autonomous tractor in ten years, but at the same time view autonomous tractors as more suitable for most specified agronomic tasks. Non-purchase options such as contractor services and machinery sharing represent the preferred modes of robot deployment.

    @Article{spykman2021106176,
    title = {Farmers’ perspectives on field crop robots – Evidence from Bavaria, Germany},
    journal = {Computers and Electronics in Agriculture},
    volume = {186},
    pages = {106176},
    year = {2021},
    issn = {0168-1699},
    doi = {https://doi.org/10.1016/j.compag.2021.106176},
    url = {https://www.sciencedirect.com/science/article/pii/S0168169921001939},
    author = {O. Spykman and A. Gabriel and M. Ptacek and M. Gandorfer},
    keywords = {Farmer survey, Attitude, Autonomous, Digitalization, Field crop robot},
    abstract = {Farmers’ attitudes toward field crop robots in a European setting have hardly been studied despite an increasing availability of the technology. Given the relevance of robots for small-scale agriculture, however, their acceptability in regions dominated by small-scale agriculture such as Bavaria, Germany, is of particular interest. Based on a sample of 174 farmers, an exploratory investigation of factors influencing the preference for large or small field crop robots in general and in specific settings and for mode of operation was carried out. Data were gathered using questionnaires at two events including lectures and field demonstrations and analyzed using bivariate tests. Farm size, farming system (organic/conventional), and occupational structure (part-time/full-time) were relevant attributes influencing the evaluation of advantages and disadvantages of field crop robots. Generally, respondents from larger farms focus more on financial benefits from robots and prefer large autonomous tractors. Conversely, small-scale or organic farmers consider environmental benefits of field crop robots relatively more important and favor small robots. Organic farming also positively correlates with the intent to purchase field crop robots within the next five years. More farmers can generally imagine owning small robots as opposed to an autonomous tractor in ten years, but at the same time view autonomous tractors as more suitable for most specified agronomic tasks. Non-purchase options such as contractor services and machinery sharing represent the preferred modes of robot deployment.},
    }

  • F. Görlich, E. Marks, A. Mahlein, K. König, P. Lottes, and C. Stachniss, "UAV-Based Classification of Cercospora Leaf Spot Using RGB Images," Drones, vol. 5, iss. 2, 2021. doi:10.3390/drones5020034
    [BibTeX] [PDF]

    Plant diseases can impact crop yield. Thus, the detection of plant diseases using sensors that can be mounted on aerial vehicles is in the interest of farmers to support decision-making in integrated pest management and to breeders for selecting tolerant or resistant genotypes. This paper investigated the detection of Cercospora leaf spot (CLS), caused by Cercospora beticola in sugar beet using RGB imagery. We proposed an approach to tackle the CLS detection problem using fully convolutional neural networks, which operate directly on RGB images captured by a UAV. This efficient approach does not require complex multi- or hyper-spectral sensors, but provides reliable results and high sensitivity. We provided a detection pipeline for pixel-wise semantic segmentation of CLS symptoms, healthy vegetation, and background so that our approach can automatically quantify the grade of infestation. We thoroughly evaluated our system using multiple UAV datasets recorded from different sugar beet trial fields. The dataset consisted of a training and a test dataset and originated from different fields. We used it to evaluate our approach under realistic conditions and analyzed its generalization capabilities to unseen environments. The obtained results correlated to visual estimation by human experts significantly. The presented study underlined the potential of high-resolution RGB imaging and convolutional neural networks for plant disease detection under field conditions. The demonstrated procedure is particularly interesting for applications under practical conditions, as no complex and cost-intensive measuring system is required.

    @Article{goerlich2021drones,
    author = {Görlich, Florian and Marks, Elias and Mahlein, Anne-Katrin and König, Kathrin and Lottes, Philipp and Stachniss, Cyrill},
    title = {{UAV-Based Classification of Cercospora Leaf Spot Using RGB Images}},
    journal = {Drones},
    volume = {5},
    year = {2021},
    number = {2},
    article-number= {34},
    url = {https://www.mdpi.com/2504-446X/5/2/34/pdf},
    issn = {2504-446X},
    abstract = {Plant diseases can impact crop yield. Thus, the detection of plant diseases using sensors that can be mounted on aerial vehicles is in the interest of farmers to support decision-making in integrated pest management and to breeders for selecting tolerant or resistant genotypes. This paper investigated the detection of Cercospora leaf spot (CLS), caused by Cercospora beticola in sugar beet using RGB imagery. We proposed an approach to tackle the CLS detection problem using fully convolutional neural networks, which operate directly on RGB images captured by a UAV. This efficient approach does not require complex multi- or hyper-spectral sensors, but provides reliable results and high sensitivity. We provided a detection pipeline for pixel-wise semantic segmentation of CLS symptoms, healthy vegetation, and background so that our approach can automatically quantify the grade of infestation. We thoroughly evaluated our system using multiple UAV datasets recorded from different sugar beet trial fields. The dataset consisted of a training and a test dataset and originated from different fields. We used it to evaluate our approach under realistic conditions and analyzed its generalization capabilities to unseen environments. The obtained results correlated to visual estimation by human experts significantly. The presented study underlined the potential of high-resolution RGB imaging and convolutional neural networks for plant disease detection under field conditions. The demonstrated procedure is particularly interesting for applications under practical conditions, as no complex and cost-intensive measuring system is required.},
    doi = {10.3390/drones5020034},
    }

  • C. Latka, T. Heckelei, A. Kuhn, H. Witzke, and L. Kornher, "CAP measures towards environmental sustainability—Trade opportunities for Africa?," Q Open, vol. 1, iss. 1, 2021. doi:10.1093/qopen/qoab003
    [BibTeX] [PDF]

    {Environmental sustainability is a core aspect of the proposed future EU Common Agricultural Policy (CAP). Policy changes must not compromise socioeconomic development in low-income countries, whereas the extensification of EU agriculture may also create trade opportunities abroad. We apply a global agricultural-economic model to assess EU–African trade-related impacts of potential, environmentally motivated CAP changes. Restrictions on livestock density and nitrogen application reveal reduced EU production levels of meat. This lowers the EU's agricultural environmental burden and share in agricultural trade flows to Africa. However, overall food supply in Africa is not projected to deteriorate substantially, as imports from other world regions and increasing domestic production fill the gap. While this weakens the global emission reduction potential, net livestock producers in Africa may benefit from increasing producer prices. How far potentials for domestic production and trade can be used in African regions depends at least partly on their competitiveness vis-á-vis substituting importers.}

    @Article{10.1093/qopen/qoab003,
    author = {Latka, Catharina and Heckelei, Thomas and Kuhn, Arnim and Witzke, Heinz-Peter and Kornher, Lukas},
    title = "{CAP measures towards environmental sustainability—Trade opportunities for Africa?}",
    journal = {Q Open},
    volume = {1},
    number = {1},
    year = {2021},
    month = {03},
    abstract = "{Environmental sustainability is a core aspect of the proposed future EU Common Agricultural Policy (CAP). Policy changes must not compromise socioeconomic development in low-income countries, whereas the extensification of EU agriculture may also create trade opportunities abroad. We apply a global agricultural-economic model to assess EU–African trade-related impacts of potential, environmentally motivated CAP changes. Restrictions on livestock density and nitrogen application reveal reduced EU production levels of meat. This lowers the EU's agricultural environmental burden and share in agricultural trade flows to Africa. However, overall food supply in Africa is not projected to deteriorate substantially, as imports from other world regions and increasing domestic production fill the gap. While this weakens the global emission reduction potential, net livestock producers in Africa may benefit from increasing producer prices. How far potentials for domestic production and trade can be used in African regions depends at least partly on their competitiveness vis-á-vis substituting importers.}",
    issn = {2633-9048},
    doi = {10.1093/qopen/qoab003},
    note = {qoab003},
    url = {https://academic.oup.com/qopen/article-pdf/1/1/qoab003/37766860/qoab003.pdf},
    }

  • A. Haupenthal, M. Brax, J. Bentz, H. F. Jungkunst, K. Schützenmeister, and E. Kroener, "Plants control soil gas exchanges possibly via mucilage," Journal of Plant Nutrition and Soil Science, 2021. doi:https://doi.org/10.1002/jpln.202000496
    [BibTeX] [PDF]

    Abstract Background: Gaseous matter exchanges in soil are determined by the connectivity of the pore system which is easily clogged by fresh root exudates. However, it remains unclear how a hydrogel (e.g., mucilage) affects soil pore tortuosity and gas diffusion properties when drying. Aims: The aim of this viewpoint study is to extend the understanding of gas exchange processes in the rhizosphere by (a) relating it to the patterns formed by drying mucilage within pore space and (b) to give a concept of the effect of drying mucilage on soil gas diffusivity using the combination of experimental evidence and simulations. Methods: To describe the effect of mucilage on soil gas exchanges, we performed gas diffusion experiments on dry soil–mucilage samples and took images of glass beads mixed with mucilage to visualize the formation of mucilage after drying, using Environmental Scanning Electron Microscopy. Finally, we set up simulations to characterize the geometric distribution of mucilage within soil during the drying process. Results: Experiments of gas diffusion show that mucilage decreases gas diffusion coefficient in dry soil without significantly altering bulk density and porosity. Electron microscopy indicates that during drying mucilage forms filaments and interconnected structures throughout the pore space reducing gas phase connectivity. The evolution of these geometric structures is explained via pore scale modelling based on identifying the elastic strength of rhizodeposition during soil drying. Conclusion: Our results suggest that releasing mucilage may be a plant adaption strategy to actively alter gas diffusion in soil.

    @Article{https://doi.org/10.1002/jpln.202000496,
    author = {Haupenthal, Adrian and Brax, Mathilde and Bentz, Jonas and Jungkunst, Hermann F. and Schützenmeister, Klaus and Kroener, Eva},
    title = {Plants control soil gas exchanges possibly via mucilage},
    journal = {Journal of Plant Nutrition and Soil Science},
    keywords = {gas diffusion coefficient, liquid bridges, mucilage, pore connectivity, pore scale simulation, respiration, rhizosphere},
    doi = {https://doi.org/10.1002/jpln.202000496},
    year = {2021},
    url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/jpln.202000496},
    eprint = {https://onlinelibrary.wiley.com/doi/pdf/10.1002/jpln.202000496},
    abstract = {Abstract Background: Gaseous matter exchanges in soil are determined by the connectivity of the pore system which is easily clogged by fresh root exudates. However, it remains unclear how a hydrogel (e.g., mucilage) affects soil pore tortuosity and gas diffusion properties when drying. Aims: The aim of this viewpoint study is to extend the understanding of gas exchange processes in the rhizosphere by (a) relating it to the patterns formed by drying mucilage within pore space and (b) to give a concept of the effect of drying mucilage on soil gas diffusivity using the combination of experimental evidence and simulations. Methods: To describe the effect of mucilage on soil gas exchanges, we performed gas diffusion experiments on dry soil–mucilage samples and took images of glass beads mixed with mucilage to visualize the formation of mucilage after drying, using Environmental Scanning Electron Microscopy. Finally, we set up simulations to characterize the geometric distribution of mucilage within soil during the drying process. Results: Experiments of gas diffusion show that mucilage decreases gas diffusion coefficient in dry soil without significantly altering bulk density and porosity. Electron microscopy indicates that during drying mucilage forms filaments and interconnected structures throughout the pore space reducing gas phase connectivity. The evolution of these geometric structures is explained via pore scale modelling based on identifying the elastic strength of rhizodeposition during soil drying. Conclusion: Our results suggest that releasing mucilage may be a plant adaption strategy to actively alter gas diffusion in soil.},
    }

  • P. Yu, X. He, M. Baer, S. Beirinckx, T. Tian, Y. A. T. Moya, X. Zhang, M. Deichmann, F. P. Frey, V. Bresgen, C. Li, B. S. Razavi, G. Schaaf, N. von Wirén, Z. Su, M. Bucher, K. Tsuda, S. Goormachtig, X. Chen, and F. Hochholdinger, "Plant flavones enrich rhizosphere Oxalobacteraceae to improve maize performance under nitrogen deprivation," Nature Plants, p. 1–19, 2021. doi:10.1038/s41477-021-00897-y
    [BibTeX]

    {Beneficial interactions between plant roots and rhizosphere microorganisms are pivotal for plant fitness. Nevertheless, the molecular mechanisms controlling the feedback between root architecture and microbial community structure remain elusive in maize. Here, we demonstrate that transcriptomic gradients along the longitudinal root axis associate with specific shifts in rhizosphere microbial diversity. Moreover, we have established that root-derived flavones predominantly promote the enrichment of bacteria of the taxa Oxalobacteraceae in the rhizosphere, which in turn promote maize growth and nitrogen acquisition. Genetic experiments demonstrate that LRT1-mediated lateral root development coordinates the interactions of the root system with flavone-dependent Oxalobacteraceae under nitrogen deprivation. In summary, these experiments reveal the genetic basis of the reciprocal interactions between root architecture and the composition and diversity of specific microbial taxa in the rhizosphere resulting in improved plant performance. These findings may open new avenues towards the breeding of high-yielding and nutrient-efficient crops by exploiting their interaction with beneficial soil microorganisms. The link between rhizosphere microbial community, root architecture and performance in nitrogen-poor soils is comprehensively investigated in maize, and the role of exuded flavone to promote specific beneficial bacterial taxa is characterized.}

    @Article{10.1038/s41477-021-00897-y,
    year = {2021},
    title = {{Plant flavones enrich rhizosphere Oxalobacteraceae to improve maize performance under nitrogen deprivation}},
    author = {Yu, Peng and He, Xiaoming and Baer, Marcel and Beirinckx, Stien and Tian, Tian and Moya, Yudelsy A T and Zhang, Xuechen and Deichmann, Marion and Frey, Felix P and Bresgen, Verena and Li, Chunjian and Razavi, Bahar S and Schaaf, Gabriel and Wirén, Nicolaus von and Su, Zhen and Bucher, Marcel and Tsuda, Kenichi and Goormachtig, Sofie and Chen, Xinping and Hochholdinger, Frank},
    journal = {Nature Plants},
    doi = {10.1038/s41477-021-00897-y},
    abstract = {{Beneficial interactions between plant roots and rhizosphere microorganisms are pivotal for plant fitness. Nevertheless, the molecular mechanisms controlling the feedback between root architecture and microbial community structure remain elusive in maize. Here, we demonstrate that transcriptomic gradients along the longitudinal root axis associate with specific shifts in rhizosphere microbial diversity. Moreover, we have established that root-derived flavones predominantly promote the enrichment of bacteria of the taxa Oxalobacteraceae in the rhizosphere, which in turn promote maize growth and nitrogen acquisition. Genetic experiments demonstrate that LRT1-mediated lateral root development coordinates the interactions of the root system with flavone-dependent Oxalobacteraceae under nitrogen deprivation. In summary, these experiments reveal the genetic basis of the reciprocal interactions between root architecture and the composition and diversity of specific microbial taxa in the rhizosphere resulting in improved plant performance. These findings may open new avenues towards the breeding of high-yielding and nutrient-efficient crops by exploiting their interaction with beneficial soil microorganisms. The link between rhizosphere microbial community, root architecture and performance in nitrogen-poor soils is comprehensively investigated in maize, and the role of exuded flavone to promote specific beneficial bacterial taxa is characterized.}},
    pages = {1--19},
    }

  • M. Popović, F. Thomas, S. Papatheodorou, N. Funk, T. Vidal-Calleja, and S. Leutenegger, "Volumetric Occupancy Mapping With Probabilistic Depth Completion for Robotic Navigation," IEEE Robotics and Automation Letters, 2021.
    [BibTeX]
    @Article{popovic2021,
    author = {Marija Popovi{\'c} and Florian Thomas and Sotiris Papatheodorou and Nils Funk and Teresa Vidal-Calleja and Stefan Leutenegger},
    title = {{Volumetric Occupancy Mapping With Probabilistic Depth Completion for Robotic Navigation}},
    journal = {{IEEE} Robotics and Automation Letters},
    year = {2021},
    }

  • S. Dadshani, B. Mathew, A. Ballvora, A. S. Mason, and J. Léon, "Detection of breeding signatures in wheat using a linkage disequilibrium-corrected mapping approach," Scientific Reports, vol. 11, iss. 1, p. 1–12, 2021.
    [BibTeX]
    @Article{dadshani2021detection,
    title = {Detection of breeding signatures in wheat using a linkage disequilibrium-corrected mapping approach},
    author = {Dadshani, Said and Mathew, Boby and Ballvora, Agim and Mason, Annaliese S and L{\'e}on, Jens},
    journal = {Scientific Reports},
    volume = {11},
    number = {1},
    pages = {1--12},
    year = {2021},
    publisher = {Nature Publishing Group},
    }

  • E. Martinsson and H. Hansson, "Adjusting eco-efficiency to greenhouse gas emissions targets at farm level – The case of Swedish dairy farms," Journal of Environmental Management, vol. 287, p. 112313, 2021. doi:https://doi.org/10.1016/j.jenvman.2021.112313
    [BibTeX] [PDF]

    The purpose of this paper is to adjust the measure of eco-efficiency to account for specific sustainability targets at farm level. We assess eco-efficiency and adjust the scores according to a target of absolute levels of greenhouse gas (GHG) emissions, using Data Envelopment Analysis (DEA) and data from Swedish dairy farms as an illustrative example. In particular, the Swedish target of net-zero emissions in 2045 and vision of a fossil free economy are used to specify the GHG emission target used for assessing the adjusted eco-efficiency scores. We test for possible factors associated with the adjusted and unadjusted eco-efficiency using OLS-regression analysis. The study is based on data from the farm accounting data network (FADN) in year 2016 and considers the environmental pressures nutrients and contribution to global warming. Adjusted as well as unadjusted eco-efficiency scores suggest that Swedish dairy farms are highly inefficient, and that economic value added could increase by 64% (adj) or 67% (unadj) for conventional farms and by 42% (adj) or 41% (unadj) for organic farms at the same level of environmental pressure. Findings further suggest that adjusting the scores towards absolute levels of GHG emissions increases industry average efficiency. Comparing the unadjusted and adjusted efficiency scores using Spearman rank correlation indicates similar efficiency rankings between the unadjusted and adjusted scores. However, findings also indicate that adjusted and unadjusted eco-efficiency scores are associated with different influencing factors, which lends empirical support to the idea that the two types of efficiency scores are conceptually different. Policy recommendations can be made based on insights from the second stage analysis of possible influencing factors. In particular, adjusted eco-efficiency is associated with higher intensity of farming defined as output per livestock unit. Further, adjusted eco-efficiency is associated with a higher number of livestock units in conventional farms and with lower levels of labour per livestock unit in organic farms.

    @Article{martinsson2021112313,
    title = {Adjusting eco-efficiency to greenhouse gas emissions targets at farm level – The case of Swedish dairy farms},
    journal = {Journal of Environmental Management},
    volume = {287},
    pages = {112313},
    year = {2021},
    issn = {0301-4797},
    doi = {https://doi.org/10.1016/j.jenvman.2021.112313},
    url = {https://www.sciencedirect.com/science/article/pii/S0301479721003753},
    author = {Elin Martinsson and Helena Hansson},
    keywords = {Common agricultural policy, Eco-efficiency, Livestock farming, Planetary boundaries, Sweden},
    abstract = {The purpose of this paper is to adjust the measure of eco-efficiency to account for specific sustainability targets at farm level. We assess eco-efficiency and adjust the scores according to a target of absolute levels of greenhouse gas (GHG) emissions, using Data Envelopment Analysis (DEA) and data from Swedish dairy farms as an illustrative example. In particular, the Swedish target of net-zero emissions in 2045 and vision of a fossil free economy are used to specify the GHG emission target used for assessing the adjusted eco-efficiency scores. We test for possible factors associated with the adjusted and unadjusted eco-efficiency using OLS-regression analysis. The study is based on data from the farm accounting data network (FADN) in year 2016 and considers the environmental pressures nutrients and contribution to global warming. Adjusted as well as unadjusted eco-efficiency scores suggest that Swedish dairy farms are highly inefficient, and that economic value added could increase by 64% (adj) or 67% (unadj) for conventional farms and by 42% (adj) or 41% (unadj) for organic farms at the same level of environmental pressure. Findings further suggest that adjusting the scores towards absolute levels of GHG emissions increases industry average efficiency. Comparing the unadjusted and adjusted efficiency scores using Spearman rank correlation indicates similar efficiency rankings between the unadjusted and adjusted scores. However, findings also indicate that adjusted and unadjusted eco-efficiency scores are associated with different influencing factors, which lends empirical support to the idea that the two types of efficiency scores are conceptually different. Policy recommendations can be made based on insights from the second stage analysis of possible influencing factors. In particular, adjusted eco-efficiency is associated with higher intensity of farming defined as output per livestock unit. Further, adjusted eco-efficiency is associated with a higher number of livestock units in conventional farms and with lower levels of labour per livestock unit in organic farms.},
    }

  • N. Chebrolu, F. Magistri, T. Läbe, and C. Stachniss, "Registration of Spatio-Temporal Point Clouds of Plants for Phenotyping," PLOS ONE, 2021.
    [BibTeX] [PDF]
    @Article{chebrolu2021plosone,
    author = {N. Chebrolu and F. Magistri and T. L{\"a}be and C. Stachniss},
    title = {{Registration of Spatio-Temporal Point Clouds of Plants for Phenotyping}},
    journal = {PLOS ONE},
    year = 2021,
    url = {https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0247243&type=printable},
    }

  • J. Weyler, A. Milioto, T. Falck, J. Behley, and C. Stachniss, "Joint Plant Instance Detection and Leaf Count Estimation for In-Field Plant Phenotyping," IEEE Robotics and Automation Letters (RA-L), 2021.
    [BibTeX] [Video]
    @Article{weyler2021ral,
    author = {J. Weyler and A. Milioto and T. Falck and J. Behley and C. Stachniss},
    title = {{Joint Plant Instance Detection and Leaf Count Estimation for In-Field Plant Phenotyping}},
    journal = {IEEE Robotics and Automation Letters (RA-L)},
    year = 2021,
    videourl = {https://youtu.be/Is18Rey625I},
    }

  • L. Wiesmann, A. Milioto, X. Chen, C. Stachniss, and J. Behley, "Deep Compression for Dense Point Cloud Maps," IEEE Robotics and Automation Letters (RA-L), 2021.
    [BibTeX] [PDF]
    @Article{wiesmann2021ral,
    author = {L. Wiesmann and A. Milioto and X. Chen and C. Stachniss and J. Behley},
    title = {{Deep Compression for Dense Point Cloud Maps}},
    journal = {IEEE Robotics and Automation Letters (RA-L)},
    year = 2021,
    url = {https://www.ipb.uni-bonn.de/wp-content/papercite-data/pdf/wiesmann2021ral.pdf},
    }

  • N. Chebrolu, T. Läbe, O. Vysotska, J. Behley, and C. Stachniss, "Adaptive Robust Kernels for Non-Linear Least Squares Problems," IEEE Robotics and Automation Letters (RA-L), 2021.
    [BibTeX] [PDF]
    @Article{chebrolu2021ral,
    author = {N. Chebrolu and T. L\"{a}be and O. Vysotska and J. Behley and C. Stachniss},
    title = {{Adaptive Robust Kernels for Non-Linear Least Squares Problems}},
    journal = {IEEE Robotics and Automation Letters (RA-L)},
    year = 2021,
    url = {https://www.ipb.uni-bonn.de/wp-content/papercite-data/pdf/chebrolu2021ral.pdf},
    }

  • F. Magistri, N. Chebrolu, J. Behley, and C. Stachniss, "Towards In-Field Phenotyping Exploiting Differentiable Rendering with Self-Consistency Loss," in Proc. of the IEEE Intl. Conf. on Robotics & Automation (ICRA) , 2021.
    [BibTeX] [PDF] [Video]
    @InProceedings{magistri2021icra,
    author = {F. Magistri and N. Chebrolu and J. Behley and C. Stachniss},
    title = {{Towards In-Field Phenotyping Exploiting Differentiable Rendering with Self-Consistency Loss}},
    booktitle = {Proc. of the IEEE Intl. Conf. on Robotics \& Automation (ICRA)},
    year = 2021,
    videourl = {https://youtu.be/MF2A4ihY2lE},
    url = {https://www.ipb.uni-bonn.de/wp-content/papercite-data/pdf/magistri2021icra.pdf},
    }

  • I. Vizzo, X. Chen, N. Chebrolu, J. Behley, and C. Stachniss, "Poisson Surface Reconstruction for LiDAR Odometry and Mapping," in Proc. of the IEEE Intl. Conf. on Robotics & Automation (ICRA) , 2021.
    [BibTeX]
    @InProceedings{vizzo2021icra,
    author = {I. Vizzo and X. Chen and N. Chebrolu and J. Behley and C. Stachniss},
    title = {{Poisson Surface Reconstruction for LiDAR Odometry and Mapping}},
    booktitle = {Proc. of the IEEE Intl. Conf. on Robotics \& Automation (ICRA)},
    year = 2021,
    }

  • X. Chen, I. Vizzo, T. Läbe, J. Behley, and C. Stachniss, "Range Image-based LiDAR Localization for Autonomous Vehicles," in Proc. of the IEEE Intl. Conf. on Robotics & Automation (ICRA) , 2021.
    [BibTeX]
    @InProceedings{chen2021icra,
    author = {X. Chen and I. Vizzo and T. L{\"a}be and J. Behley and C. Stachniss},
    title = {{Range Image-based LiDAR Localization for Autonomous Vehicles}},
    booktitle = {Proc. of the IEEE Intl. Conf. on Robotics \& Automation (ICRA)},
    year = 2021,
    }

  • A. Reinke, X. Chen, and C. Stachniss, "Simple But Effective Redundant Odometry for Autonomous Vehicles," in Proc. of the IEEE Intl. Conf. on Robotics & Automation (ICRA) , 2021.
    [BibTeX]
    @InProceedings{reinke2021icra,
    title = {{Simple But Effective Redundant Odometry for Autonomous Vehicles}},
    author = {A. Reinke and X. Chen and C. Stachniss},
    booktitle = {Proc. of the IEEE Intl. Conf. on Robotics \& Automation (ICRA)},
    year = {2021},
    }

  • J. Behley, A. Milioto, and C. Stachniss, "A Benchmark for LiDAR-based Panoptic Segmentation based on KITTI," in Proc. of the IEEE Intl. Conf. on Robotics & Automation (ICRA) , 2021.
    [BibTeX]
    @InProceedings{behley2021icra,
    author = {J. Behley and A. Milioto and C. Stachniss},
    title = {{A Benchmark for LiDAR-based Panoptic Segmentation based on KITTI}},
    booktitle = {Proc. of the IEEE Intl. Conf. on Robotics \& Automation (ICRA)},
    year = 2021,
    }

  • C. Carbone, D. Albani, F. Magistri, D. Ognibene, C. Stachniss, G. Kootstra, D. Nardi, and V. Trianni, "Monitoring and Mapping of Crop Fields with UAV Swarms Based on Information Gain," in Proc. of the Intl. Symp. on Distributed Autonomous Robotic Systems (DARS) , 2021.
    [BibTeX]
    @InProceedings{carbone2021dars,
    author = {C. Carbone and D. Albani and F. Magistri and D. Ognibene and C. Stachniss and G. Kootstra and D. Nardi and V. Trianni},
    title = {{Monitoring and Mapping of Crop Fields with UAV Swarms Based on Information Gain}},
    booktitle = {Proc. of the Intl. Symp. on Distributed Autonomous Robotic Systems (DARS)},
    year = 2021,
    }

  • J. S. Bates, C. Montzka, M. Schmidt, and F. Jonard, "Estimating Canopy Density Parameters Time-Series for Winter Wheat Using UAS Mounted LiDAR," Remote Sensing, vol. 13, iss. 4, 2021. doi:10.3390/rs13040710
    [BibTeX] [PDF]

    Monitoring of canopy density with related metrics such as leaf area index (LAI) makes a significant contribution to understanding and predicting processes in the soil–plant–atmosphere system and to indicating crop health and potential yield for farm management. Remote sensing methods using optical sensors that rely on spectral reflectance to calculate LAI have become more mainstream due to easy entry and availability. Methods with vegetation indices (VI) based on multispectral reflectance data essentially measure the green area index (GAI) or response to chlorophyll content of the canopy surface and not the entire aboveground biomass that may be present from non-green elements that are key to fully assessing the carbon budget. Methods with light detection and ranging (LiDAR) have started to emerge using gap fraction (GF) to estimate the plant area index (PAI) based on canopy density. These LiDAR methods have the main advantage of being sensitive to both green and non-green plant elements. They have primarily been applied to forest cover with manned airborne LiDAR systems (ALS) and have yet to be used extensively with crops such as winter wheat using LiDAR on unmanned aircraft systems (UAS). This study contributes to a better understanding of the potential of LiDAR as a tool to estimate canopy structure in precision farming. The LiDAR method proved to have a high to moderate correlation in spatial variation to the multispectral method. The LiDAR-derived PAI values closely resemble the SunScan Ceptometer GAI ground measurements taken early in the growing season before major stages of senescence. Later in the growing season, when the canopy density was at its highest, a possible overestimation may have occurred. This was most likely due to the chosen flight parameters not providing the best depictions of canopy density with consideration of the LiDAR’s perspective, as the ground-based destructive measurements provided lower values of PAI. Additionally, a distinction between total LiDAR-derived PAI, multispectral-derived GAI, and brown area index (BAI) is made to show how the active and passive optical sensor methods used in this study can complement each other throughout the growing season.

    @Article{rs13040710,
    author = {Bates, Jordan Steven and Montzka, Carsten and Schmidt, Marius and Jonard, François},
    title = {Estimating Canopy Density Parameters Time-Series for Winter Wheat Using UAS Mounted LiDAR},
    journal = {Remote Sensing},
    volume = {13},
    year = {2021},
    number = {4},
    article-number= {710},
    url = {https://www.mdpi.com/2072-4292/13/4/710},
    issn = {2072-4292},
    abstract = {Monitoring of canopy density with related metrics such as leaf area index (LAI) makes a significant contribution to understanding and predicting processes in the soil–plant–atmosphere system and to indicating crop health and potential yield for farm management. Remote sensing methods using optical sensors that rely on spectral reflectance to calculate LAI have become more mainstream due to easy entry and availability. Methods with vegetation indices (VI) based on multispectral reflectance data essentially measure the green area index (GAI) or response to chlorophyll content of the canopy surface and not the entire aboveground biomass that may be present from non-green elements that are key to fully assessing the carbon budget. Methods with light detection and ranging (LiDAR) have started to emerge using gap fraction (GF) to estimate the plant area index (PAI) based on canopy density. These LiDAR methods have the main advantage of being sensitive to both green and non-green plant elements. They have primarily been applied to forest cover with manned airborne LiDAR systems (ALS) and have yet to be used extensively with crops such as winter wheat using LiDAR on unmanned aircraft systems (UAS). This study contributes to a better understanding of the potential of LiDAR as a tool to estimate canopy structure in precision farming. The LiDAR method proved to have a high to moderate correlation in spatial variation to the multispectral method. The LiDAR-derived PAI values closely resemble the SunScan Ceptometer GAI ground measurements taken early in the growing season before major stages of senescence. Later in the growing season, when the canopy density was at its highest, a possible overestimation may have occurred. This was most likely due to the chosen flight parameters not providing the best depictions of canopy density with consideration of the LiDAR’s perspective, as the ground-based destructive measurements provided lower values of PAI. Additionally, a distinction between total LiDAR-derived PAI, multispectral-derived GAI, and brown area index (BAI) is made to show how the active and passive optical sensor methods used in this study can complement each other throughout the growing season.},
    doi = {10.3390/rs13040710},
    }

  • L. Shang, T. Heckelei, M. K. Gerullis, J. Börner, and S. Rasch, "Adoption and diffusion of digital farming technologies - integrating farm-level evidence and system interaction," Agricultural Systems, vol. 190, p. 103074, 2021. doi:https://doi.org/10.1016/j.agsy.2021.103074
    [BibTeX] [PDF]
    @Article{shang2021103074,
    title = {Adoption and diffusion of digital farming technologies - integrating farm-level evidence and system interaction},
    journal = {Agricultural Systems},
    volume = {190},
    pages = {103074},
    year = {2021},
    issn = {0308-521X},
    doi = {https://doi.org/10.1016/j.agsy.2021.103074},
    url = {https://www.sciencedirect.com/science/article/pii/S0308521X21000275},
    author = {Linmei Shang and Thomas Heckelei and Maria K. Gerullis and Jan Börner and Sebastian Rasch},
    }

  • S. Gedicke, A. Bonerath, B. Niedermann, and J. -H. Haunert, "Zoomless Maps: External Labeling Methods for the Interactive Exploration of Dense Point Sets at a Fixed Map Scale," IEEE Transactions on Visualization and Computer Graphics, vol. 27, iss. 2, pp. 1247-1256, 2021. doi:10.1109/TVCG.2020.3030399
    [BibTeX]
    @Article{9222088,
    author = {S. {Gedicke} and A. {Bonerath} and B. {Niedermann} and J. -H. {Haunert}},
    journal = {IEEE Transactions on Visualization and Computer Graphics},
    title = {Zoomless Maps: External Labeling Methods for the Interactive Exploration of Dense Point Sets at a Fixed Map Scale},
    year = {2021},
    volume = {27},
    number = {2},
    pages = {1247-1256},
    doi = {10.1109/TVCG.2020.3030399},
    }

  • S. Hadir, T. Gaiser, H. Hüging, M. Athmann, D. Pfarr, R. Kemper, F. Ewert, and S. Seidel, "Sugar Beet Shoot and Root Phenotypic Plasticity to Nitrogen, Phosphorus, Potassium and Lime Omission," Agriculture, vol. 11, iss. 1, 2021. doi:10.3390/agriculture11010021
    [BibTeX] [PDF]

    In low input agriculture, a thorough understanding of the plant-nutrient interactions plays a central role. This study aims to investigate the effects of nitrogen (N), phosphorus (P), and potassium (K) and liming omission on shoot growth as well as on topsoil root biomass, growth and morphology (tuber and fibrous roots) of sugar beet grown under field conditions at the Dikopshof long-term fertilizer experiment (Germany). Classical shoot observation methods were combined with root morphology and link measurements using an image analysis program. Omission of the nutrients N, P and K as well as of liming led to a significant decrease in shoot growth. Tuber yield was lowest for the unfertilized and the K omission treatment. The root shoot ratio was highest in the N deficient treatment. In the K omission treatment, a strategic change from a less herringbone root type (early stage) to a more herringbone root type (late stage), which is more efficient for the acquisition of mobile nutrients, was observed. By contrast, a change from a more herringbone (early stage) to a less herringbone root type (late stage) which is less expensive to produce and maintain was observed in the unfertilized treatment. We conclude that sugar beet alters its root morphology as a nutrient acquisition strategy.

    @Article{agriculture11010021,
    author = {Hadir, Sofia and Gaiser, Thomas and Hüging, Hubert and Athmann, Miriam and Pfarr, Daniel and Kemper, Roman and Ewert, Frank and Seidel, Sabine},
    title = {Sugar Beet Shoot and Root Phenotypic Plasticity to Nitrogen, Phosphorus, Potassium and Lime Omission},
    journal = {Agriculture},
    volume = {11},
    year = {2021},
    number = {1},
    article-number= {21},
    url = {https://www.mdpi.com/2077-0472/11/1/21},
    issn = {2077-0472},
    abstract = {In low input agriculture, a thorough understanding of the plant-nutrient interactions plays a central role. This study aims to investigate the effects of nitrogen (N), phosphorus (P), and potassium (K) and liming omission on shoot growth as well as on topsoil root biomass, growth and morphology (tuber and fibrous roots) of sugar beet grown under field conditions at the Dikopshof long-term fertilizer experiment (Germany). Classical shoot observation methods were combined with root morphology and link measurements using an image analysis program. Omission of the nutrients N, P and K as well as of liming led to a significant decrease in shoot growth. Tuber yield was lowest for the unfertilized and the K omission treatment. The root shoot ratio was highest in the N deficient treatment. In the K omission treatment, a strategic change from a less herringbone root type (early stage) to a more herringbone root type (late stage), which is more efficient for the acquisition of mobile nutrients, was observed. By contrast, a change from a more herringbone (early stage) to a less herringbone root type (late stage) which is less expensive to produce and maintain was observed in the unfertilized treatment. We conclude that sugar beet alters its root morphology as a nutrient acquisition strategy.},
    doi = {10.3390/agriculture11010021},
    }

  • D. Wallach, T. Palosuo, P. Thorburn, Z. Hochman, F. Andrianasolo, S. Asseng, B. Basso, S. Buis, N. Crout, B. Dumont, R. Ferrise, T. Gaiser, S. Gayler, S. Hireman, S. Hoek, H. Horan, G. Hoogenboom, M. Huang, M. Jabloun, P. -E. Jasson, Q. Jing, E. Justes, C. K. Kersebaum, M. Launay, E. Lewan, Q. Luo, B. Maestrini, M. Moriondo, G. Padovan, J. E. Olesen, A. Poyda, E. Priesack, Q. B. Pullens J.W.M, N. Schütze, V. Shelia, A. Souissi, X. Specka, A. K. Srivastava, T. Stella, T. Streck, G. Trombi, E. Wallor, J. Wang, T. H. D. Weber, L. Weihermüller, A. de Wit, T. Wöhling, L. Xiao, C. Zhao, Y. Zhu, and S. J. Seidel, "Multi-model evaluation of phenology prediction for wheat in Australia," Agricultural and Forest Meteorology, vol. 124, pp. 298-299, 2021. doi:https://doi.org/10.1016/j.agrformet.2020.108289
    [BibTeX]
    @Article{wallach2021agrformet,
    title = "Multi-model evaluation of phenology prediction for wheat in Australia",
    journal = "Agricultural and Forest Meteorology",
    volume = "124",
    pages = "298-299",
    year = "2021",
    issn = "1161-0301",
    doi = "https://doi.org/10.1016/j.agrformet.2020.108289",
    author = {Wallach, D. and Palosuo, T. and Thorburn, P. and Hochman, Z. and Andrianasolo, F. and Asseng, S. and Basso, B. and Buis, S. and Crout, N. and Dumont, B. and Ferrise, R. and Gaiser, T. and Gayler, S. and Hireman, S. and Hoek, S. and Horan, H. and Hoogenboom, G. and Huang, M. and Jabloun, M. and Jasson, P.-E. and Jing, Q. and Justes, E. and Kersebaum, C.K. and Launay, M. and Lewan, E. and Luo, Q. and Maestrini, B. and Moriondo, M. and Padovan, G. and Olesen, J.E. and Poyda, A. and Priesack, E. and Pullens, J.W.M, Qian, B. and Schütze, N. and Shelia, V. and Souissi, A. and Specka, X. and Srivastava, A.K. and Stella, T. and Streck, T. and Trombi, G. and Wallor, E. and Wang, J. and Weber, T.H.D. and Weihermüller, L. and de Wit, A. and Wöhling, T. and Xiao, L. and Zhao, C. and Zhu, Y. and Seidel, S. J.},
    }

  • D. Wallach, T. Palosuo, P. Thorburn, E. Gourdain, S. Asseng, B. Basso, S. Buis, N. Crout, C. Dibari, B. Dumont, R. Ferrise, T. Gaiser, C. Garcia, S. Gayler, A. Ghahramani, Z. Hochman, S. Hoek, G. Hoogenboom, H. Horan, M. Huang, M. Jabloun, Q. Jing, E. Justes, K. C. Kersebaum, A. Klosterhalfen, M. Launay, Q. Luo, B. Maestrini, H. Mielenz, M. Moriondo, H. Nariman Zadeh, J. E. Olesen, A. Poyda, E. Priesack, J. W. M. Pullens, B. Qian, N. Schütze, V. Shelia, A. Souissi, X. Specka, A. K. Srivastava, T. Stella, T. Streck, G. Trombi, E. Wallor, J. Wang, T. K. D. Weber, L. Weihermüller, A. de Wit, T. Wöhling, L. Xiao, C. Zhao, Y. Zhu, and S. J. Seidel, "How well do crop modeling groups predict wheat phenology, given calibration data from the target population?," European Journal of Agronomy, vol. 124, p. 126195, 2021. doi:https://doi.org/10.1016/j.eja.2020.126195
    [BibTeX] [PDF]

    Predicting phenology is essential for adapting varieties to different environmental conditions and for crop management. Therefore, it is important to evaluate how well different crop modeling groups can predict phenology. Multiple evaluation studies have been previously published, but it is still difficult to generalize the findings from such studies since they often test some specific aspect of extrapolation to new conditions, or do not test on data that is truly independent of the data used for calibration. In this study, we analyzed the prediction of wheat phenology in Northern France under observed weather and current management, which is a problem of practical importance for wheat management. The results of 27 modeling groups are evaluated, where modeling group encompasses model structure, i.e. the model equations, the calibration method and the values of those parameters not affected by calibration. The data for calibration and evaluation are sampled from the same target population, thus extrapolation is limited. The calibration and evaluation data have neither year nor site in common, to guarantee rigorous evaluation of prediction for new weather and sites. The best modeling groups, and also the mean and median of the simulations, have a mean absolute error (MAE) of about 3 days, which is comparable to the measurement error. Almost all models do better than using average number of days or average sum of degree days to predict phenology. On the other hand, there are important differences between modeling groups, due to model structural differences and to differences between groups using the same model structure, which emphasizes that model structure alone does not completely determine prediction accuracy. In addition to providing information for our specific environments and varieties, these results are a useful contribution to a knowledge base of how well modeling groups can predict phenology, when provided with calibration data from the target population.

    @Article{wallach2021126195,
    title = "How well do crop modeling groups predict wheat phenology, given calibration data from the target population?",
    journal = "European Journal of Agronomy",
    volume = "124",
    pages = "126195",
    year = "2021",
    issn = "1161-0301",
    doi = "https://doi.org/10.1016/j.eja.2020.126195",
    url = "http://www.sciencedirect.com/science/article/pii/S1161030120302021",
    author = "Daniel Wallach and Taru Palosuo and Peter Thorburn and Emmanuelle Gourdain and Senthold Asseng and Bruno Basso and Samuel Buis and Neil Crout and Camilla Dibari and Benjamin Dumont and Roberto Ferrise and Thomas Gaiser and Cécile Garcia and Sebastian Gayler and Afshin Ghahramani and Zvi Hochman and Steven Hoek and Gerrit Hoogenboom and Heidi Horan and Mingxia Huang and Mohamed Jabloun and Qi Jing and Eric Justes and Kurt Christian Kersebaum and Anne Klosterhalfen and Marie Launay and Qunying Luo and Bernardo Maestrini and Henrike Mielenz and Marco Moriondo and Hasti {Nariman Zadeh} and Jørgen Eivind Olesen and Arne Poyda and Eckart Priesack and Johannes Wilhelmus Maria Pullens and Budong Qian and Niels Schütze and Vakhtang Shelia and Amir Souissi and Xenia Specka and Amit Kumar Srivastava and Tommaso Stella and Thilo Streck and Giacomo Trombi and Evelyn Wallor and Jing Wang and Tobias K.D. Weber and Lutz Weihermüller and Allard {de Wit} and Thomas Wöhling and Liujun Xiao and Chuang Zhao and Yan Zhu and Sabine J. Seidel",
    keywords = "Crop model, Phenology prediction, Model evaluation, Wheat",
    abstract = "Predicting phenology is essential for adapting varieties to different environmental conditions and for crop management. Therefore, it is important to evaluate how well different crop modeling groups can predict phenology. Multiple evaluation studies have been previously published, but it is still difficult to generalize the findings from such studies since they often test some specific aspect of extrapolation to new conditions, or do not test on data that is truly independent of the data used for calibration. In this study, we analyzed the prediction of wheat phenology in Northern France under observed weather and current management, which is a problem of practical importance for wheat management. The results of 27 modeling groups are evaluated, where modeling group encompasses model structure, i.e. the model equations, the calibration method and the values of those parameters not affected by calibration. The data for calibration and evaluation are sampled from the same target population, thus extrapolation is limited. The calibration and evaluation data have neither year nor site in common, to guarantee rigorous evaluation of prediction for new weather and sites. The best modeling groups, and also the mean and median of the simulations, have a mean absolute error (MAE) of about 3 days, which is comparable to the measurement error. Almost all models do better than using average number of days or average sum of degree days to predict phenology. On the other hand, there are important differences between modeling groups, due to model structural differences and to differences between groups using the same model structure, which emphasizes that model structure alone does not completely determine prediction accuracy. In addition to providing information for our specific environments and varieties, these results are a useful contribution to a knowledge base of how well modeling groups can predict phenology, when provided with calibration data from the target population.",
    }

  • C. Latka, M. Kuiper, S. Frank, T. Heckelei, P. Havlík, H. Witzke, A. Leip, H. D. Cui, A. Kuijsten, J. M. Geleijnse, and M. van Dijk, "Paying the price for environmentally sustainable and healthy EU diets," Global Food Security, vol. 28, p. 100437, 2021. doi:https://doi.org/10.1016/j.gfs.2020.100437
    [BibTeX] [PDF]

    We review consumer-side interventions and their effectiveness to support a transition to healthier and more environmentally sustainable diets and identify taxes/subsidies as relevant instruments. To quantify the scope of necessary tax levels to achieve dietary recommendations on EU average, we apply three established economic models. Our business-as-usual food intake projections stress the need for policy intervention to resolve continued divergence from nutrition guidelines. Our findings suggest that food group specific taxes are effective in reaching nutrition and environmental sustainability targets. However, considerable tax levels are required to achieve the targeted consumption shifts, inducing a discussion about alternative policy designs and current model limitations. A coherent policy package is suggested to approach nutrition and sustainability objectives simultaneously.

    @Article{latka2021100437,
    title = "Paying the price for environmentally sustainable and healthy EU diets",
    journal = "Global Food Security",
    volume = "28",
    pages = "100437",
    year = "2021",
    issn = "2211-9124",
    doi = "https://doi.org/10.1016/j.gfs.2020.100437",
    url = "http://www.sciencedirect.com/science/article/pii/S2211912420300912",
    author = "Catharina Latka and Marijke Kuiper and Stefan Frank and Thomas Heckelei and Petr Havlík and Heinz-Peter Witzke and Adrian Leip and Hao David Cui and Anneleen Kuijsten and Johanna M. Geleijnse and Michiel {van Dijk}",
    abstract = "We review consumer-side interventions and their effectiveness to support a transition to healthier and more environmentally sustainable diets and identify taxes/subsidies as relevant instruments. To quantify the scope of necessary tax levels to achieve dietary recommendations on EU average, we apply three established economic models. Our business-as-usual food intake projections stress the need for policy intervention to resolve continued divergence from nutrition guidelines. Our findings suggest that food group specific taxes are effective in reaching nutrition and environmental sustainability targets. However, considerable tax levels are required to achieve the targeted consumption shifts, inducing a discussion about alternative policy designs and current model limitations. A coherent policy package is suggested to approach nutrition and sustainability objectives simultaneously.",
    }

2020

  • A. P. Wasson, K. A. Nagel, S. Tracy, and M. Watt, "Beyond digging: noninvasive root and rhizosphere phenotyping," Trends in plant science, vol. 25, iss. 1, p. 119–120, 2020.
    [BibTeX]
    @Article{wasson2020beyond,
    title = {Beyond digging: noninvasive root and rhizosphere phenotyping},
    author = {Wasson, Anton P and Nagel, Kerstin A and Tracy, Saoirse and Watt, Michelle},
    journal = {Trends in plant science},
    volume = {25},
    number = {1},
    pages = {119--120},
    year = {2020},
    publisher = {Elsevier},
    }

  • F. Seiffarth, T. Horváth, and S. Wrobel, "Maximum margin separations in finite closure systems," in Joint European Conference on Machine Learning and Knowledge Discovery in Databases , 2020, p. 3–18.
    [BibTeX]
    @InProceedings{seiffarth2020maximum,
    title = {Maximum margin separations in finite closure systems},
    author = {Seiffarth, Florian and Horv{\'a}th, Tam{\'a}s and Wrobel, Stefan},
    booktitle = {Joint European Conference on Machine Learning and Knowledge Discovery in Databases},
    pages = {3--18},
    year = {2020},
    organization = {Springer},
    }

  • S. Ehosioke, F. Nguyen, S. Rao, T. Kremer, R. E. Placencia-Gomez, J. A. Huisman, A. Kemna, M. Javaux, and S. Garré, "Sensing the electrical properties of roots: A review," Vadose zone journal, 2020.
    [BibTeX]
    @Article{ehosioke2020sensing,
    title = {Sensing the electrical properties of roots: A review},
    author = {Ehosioke, Solomon and Nguyen, Fr{\'e}d{\'e}ric and Rao, Sathanarayan and Kremer, Thomas and Placencia-Gomez, Roguer Edmundo and Huisman, Johan Alexander and Kemna, Andreas and Javaux, Mathieu and Garr{\'e}, Sarah},
    journal = {Vadose zone journal},
    year = {2020},
    publisher = {Soil Science Society of America},
    }

  • J. S. Bates, C. Montzka, M. Schmidt, and F. Jonard, "Winter Wheat LAI Estimation Using UAV Mounted LiDAR," in 12th GeoMundus Conference (virtual) , 2020.
    [BibTeX]
    @InProceedings{bates2020winter,
    title = {Winter Wheat LAI Estimation Using UAV Mounted LiDAR},
    author = {Bates, Jordan Steven and Montzka, Carsten and Schmidt, Marius and Jonard, Fran{\c{c}}ois},
    booktitle = {12th GeoMundus Conference (virtual)},
    year = {2020},
    }

  • F. He, B. Thiele, S. Santhiraraja-Abresch, M. Watt, T. Kraska, A. Ulbrich, and A. J. Kuhn, "Effects of root temperature on the plant growth and food quality of Chinese broccoli (Brassica oleracea var. alboglabra Bailey)," Agronomy, vol. 10, iss. 5, p. 702, 2020.
    [BibTeX]
    @Article{he2020effects,
    title = {Effects of root temperature on the plant growth and food quality of Chinese broccoli (Brassica oleracea var. alboglabra Bailey)},
    author = {He, Fang and Thiele, Bj{\"o}rn and Santhiraraja-Abresch, Sharin and Watt, Michelle and Kraska, Thorsten and Ulbrich, Andreas and Kuhn, Arnd J},
    journal = {Agronomy},
    volume = {10},
    number = {5},
    pages = {702},
    year = {2020},
    publisher = {Multidisciplinary Digital Publishing Institute},
    }

  • P. Welke, "Efficient Frequent Subgraph Mining in Transactional Databases," in 2020 IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA) , 2020, p. 307–314.
    [BibTeX]
    @InProceedings{welke2020efficient,
    title = {Efficient Frequent Subgraph Mining in Transactional Databases},
    author = {Welke, Pascal},
    booktitle = {2020 IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA)},
    pages = {307--314},
    year = {2020},
    organization = {IEEE},
    }

  • N. P. Laha, Y. W. Dhir, R. F. Giehl, E. M. Schäfer, P. Gaugler, Z. H. Shishavan, H. Gulabani, H. Mao, N. Zheng, N. von Wirén, and others, "ITPK1-Dependent Inositol Polyphosphates Regulate Auxin Responses in Arabidopsis thaliana," bioRxiv, 2020.
    [BibTeX]
    @Article{laha2020itpk1,
    title = {ITPK1-Dependent Inositol Polyphosphates Regulate Auxin Responses in Arabidopsis thaliana},
    author = {Laha, Nargis Parvin and Dhir, Yashika Walia and Giehl, Ricardo FH and Sch{\"a}fer, Eva Maria and Gaugler, Philipp and Shishavan, Zhaleh Haghighat and Gulabani, Hitika and Mao, Haibin and Zheng, Ning and von Wir{\'e}n, Nicolaus and others},
    journal = {bioRxiv},
    year = {2020},
    publisher = {Cold Spring Harbor Laboratory},
    }

  • A. Mehler, W. Hemati, P. Welke, M. Konca, and T. Uslu, "Multiple texts as a limiting factor in online learning: Quantifying (dis-) similarities of knowledge networks across languages," arXiv preprint arXiv:2008.02047, 2020.
    [BibTeX]
    @Article{mehler2020multiple,
    title = {Multiple texts as a limiting factor in online learning: Quantifying (dis-) similarities of knowledge networks across languages},
    author = {Mehler, Alexander and Hemati, Wahed and Welke, Pascal and Konca, Maxim and Uslu, Tolga},
    journal = {arXiv preprint arXiv:2008.02047},
    year = {2020},
    }

  • M. Halstead, S. Denman, C. Fookes, and C. McCool, "Fruit Detection in the Wild: The Impact of Varying Conditions and Cultivar," in 2020 Digital Image Computing: Techniques and Applications (DICTA) , 2020, pp. 1-8. doi:10.1109/DICTA51227.2020.9363407
    [BibTeX]
    @InProceedings{9363407,
    author = {Halstead, Michael and Denman, Simon and Fookes, Clinton and McCool, Chris},
    booktitle = {2020 Digital Image Computing: Techniques and Applications (DICTA)},
    title = {Fruit Detection in the Wild: The Impact of Varying Conditions and Cultivar},
    year = {2020},
    volume = {},
    number = {},
    pages = {1-8},
    doi = {10.1109/DICTA51227.2020.9363407},
    }

  • L. Drees, J. Kusche, and R. Roscher, "MULTI-MODAL DEEP LEARNING WITH SENTINEL-3 OBSERVATIONS FOR THE DETECTION OF OCEANIC INTERNAL WAVES," ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol. V-2-2020, p. 813–820, 2020. doi:10.5194/isprs-annals-V-2-2020-813-2020
    [BibTeX] [PDF]
    @Article{isprs-annals-v-2-2020-813-2020,
    author = {Drees, L. and Kusche, J. and Roscher, R.},
    title = {MULTI-MODAL DEEP LEARNING WITH SENTINEL-3 OBSERVATIONS FOR THE DETECTION OF OCEANIC INTERNAL WAVES},
    journal = {ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences},
    volume = {V-2-2020},
    year = {2020},
    pages = {813--820},
    url = {https://www.isprs-ann-photogramm-remote-sens-spatial-inf-sci.net/V-2-2020/813/2020/},
    doi = {10.5194/isprs-annals-V-2-2020-813-2020},
    }

  • O. -H. Kwon, J. Tanke, and J. Gall, "Recursive Bayesian Filtering for Multiple Human Pose Tracking from Multiple Cameras," in Asian Conference on Computer Vision (ACCV'20) , 2020.
    [BibTeX] [PDF]
    @InProceedings{kwon2020accv,
    author = {Kwon, O.-H. AND Tanke, J. AND Gall, J.},
    title = {{Recursive Bayesian Filtering for Multiple Human Pose Tracking from Multiple Cameras}},
    booktitle = {Asian Conference on Computer Vision (ACCV'20)},
    year = {2020},
    url = {https://openaccess.thecvf.com/content/ACCV2020/papers/Kwon_Recursive_Bayesian_Filtering_for_Multiple_Human_Pose_Tracking_from_Multiple_ACCV_2020_paper.pdf},
    }

  • C. Lehnert, C. McCool, I. Sa, and T. Perez, "Performance improvements of a sweet pepper harvesting robot in protected cropping environments," Journal of Field Robotics, vol. 37, iss. 7, pp. 1197-1223, 2020. doi:https://doi.org/10.1002/rob.21973
    [BibTeX] [PDF]

    Abstract Using robots to harvest sweet peppers in protected cropping environments has remained unsolved despite considerable effort by the research community over several decades. In this paper, we present the robotic harvester, Harvey, designed for sweet peppers in protected cropping environments that achieved a 76.5\% success rate on 68 fruit (within a modified scenario) which improves upon our prior work which achieved 58\% on 24 fruit and related sweet pepper harvesting work which achieved 33\% on 39 fruit (for their best tool in a modified scenario). This improvement was primarily achieved through the introduction of a novel peduncle segmentation system using an efficient deep convolutional neural network, in conjunction with three-dimensional postfiltering to detect the critical cutting location. We benchmark the peduncle segmentation against prior art demonstrating an improvement in performance with a F1 score of 0.564 compared to 0.302. The robotic harvester uses a perception pipeline to detect a target sweet pepper and an appropriate grasp and cutting pose used to determine the trajectory of a multimodal harvesting tool to grasp the sweet pepper and cut it from the plant. A novel decoupling mechanism enables the gripping and cutting operations to be performed independently. We perform an in-depth analysis of the full robotic harvesting system to highlight bottlenecks and failure points that future work could address.

    @Article{https://doi.org/10.1002/rob.21973,
    author = {Lehnert, Chris and McCool, Chris and Sa, Inkyu and Perez, Tristan},
    title = {Performance improvements of a sweet pepper harvesting robot in protected cropping environments},
    journal = {Journal of Field Robotics},
    volume = {37},
    number = {7},
    pages = {1197-1223},
    keywords = {agriculture, grasping, manipulators, perception, robotic harvesting},
    doi = {https://doi.org/10.1002/rob.21973},
    url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/rob.21973},
    eprint = {https://onlinelibrary.wiley.com/doi/pdf/10.1002/rob.21973},
    abstract = {Abstract Using robots to harvest sweet peppers in protected cropping environments has remained unsolved despite considerable effort by the research community over several decades. In this paper, we present the robotic harvester, Harvey, designed for sweet peppers in protected cropping environments that achieved a 76.5\% success rate on 68 fruit (within a modified scenario) which improves upon our prior work which achieved 58\% on 24 fruit and related sweet pepper harvesting work which achieved 33\% on 39 fruit (for their best tool in a modified scenario). This improvement was primarily achieved through the introduction of a novel peduncle segmentation system using an efficient deep convolutional neural network, in conjunction with three-dimensional postfiltering to detect the critical cutting location. We benchmark the peduncle segmentation against prior art demonstrating an improvement in performance with a F1 score of 0.564 compared to 0.302. The robotic harvester uses a perception pipeline to detect a target sweet pepper and an appropriate grasp and cutting pose used to determine the trajectory of a multimodal harvesting tool to grasp the sweet pepper and cut it from the plant. A novel decoupling mechanism enables the gripping and cutting operations to be performed independently. We perform an in-depth analysis of the full robotic harvesting system to highlight bottlenecks and failure points that future work could address.},
    year = {2020},
    }

  • H. E. Ahrends, S. Siebert, E. E. Rezaei, S. J. Seidel, H. Hüging, F. Ewert, T. Döring, V. Rueda-Ayala, W. Eugster, and T. Gaiser, "Nutrient supply affects the yield stability of major European crops—a 50 year study," Environmental Research Letters, vol. 16, iss. 1, p. 14003, 2020. doi:10.1088/1748-9326/abc849
    [BibTeX] [PDF]

    Yield stability is important for food security and a sustainable crop production, especially under changing climatic conditions. It is well known that the variability of yields is linked to changes in meteorological conditions. However, little is known about the long-term effects of agronomic management strategies, such as the supply of important nutrients. We analysed the stability of four major European crops grown between 1955 and 2008 at a long-term fertilization experiment located in Germany. Six fertilizer treatments ranged from no fertilization over the omission of individual macronutrients to complete mineral fertilization with all major macronutrients (nitrogen, phosphorus, potassium and calcium). Yield stability was estimated for each crop × treatment combination using the relative yield deviation in each year from the corresponding (nonlinear) trend value (relative yield anomalies (RYA)). Stability was lowest for potato, followed by sugar beet and winter wheat and highest for winter rye. Stability was highest when soils had received all nutrients with the standard deviation of RYA being two to three times lower than for unfertilized plots. The omission of nitrogen and potassium was associated with a decrease in yield stability and a decrease in the number of simultaneous positive and negative yield anomalies among treatments. Especially in root crops nutrient supply strongly influenced both annual yield anomalies and changes in anomalies over time. During the second half of the observation period yield stability decreased for sugar beet and increased for winter wheat. Potato yields were more stable during the second period, but only under complete nutrient supply. The critical role of potassium supply for yield stability suggests potential links to changes in the water balance during the last decades. Results demonstrate the need to explicitly consider the response of crops to long-term nutrient supply for understanding and predicting changes in yield stability.

    @Article{ahrends_2020,
    doi = {10.1088/1748-9326/abc849},
    url = {https://doi.org/10.1088/1748-9326/abc849},
    year = 2020,
    month = {dec},
    publisher = {{IOP} Publishing},
    volume = {16},
    number = {1},
    pages = {014003},
    author = {Hella Ellen Ahrends and Stefan Siebert and Ehsan Eyshi Rezaei and Sabine Julia Seidel and Hubert Hüging and Frank Ewert and Thomas Döring and Victor Rueda-Ayala and Werner Eugster and Thomas Gaiser},
    title = {Nutrient supply affects the yield stability of major European crops{\textemdash}a 50 year study},
    journal = {Environmental Research Letters},
    abstract = {Yield stability is important for food security and a sustainable crop production, especially under changing climatic conditions. It is well known that the variability of yields is linked to changes in meteorological conditions. However, little is known about the long-term effects of agronomic management strategies, such as the supply of important nutrients. We analysed the stability of four major European crops grown between 1955 and 2008 at a long-term fertilization experiment located in Germany. Six fertilizer treatments ranged from no fertilization over the omission of individual macronutrients to complete mineral fertilization with all major macronutrients (nitrogen, phosphorus, potassium and calcium). Yield stability was estimated for each crop × treatment combination using the relative yield deviation in each year from the corresponding (nonlinear) trend value (relative yield anomalies (RYA)). Stability was lowest for potato, followed by sugar beet and winter wheat and highest for winter rye. Stability was highest when soils had received all nutrients with the standard deviation of RYA being two to three times lower than for unfertilized plots. The omission of nitrogen and potassium was associated with a decrease in yield stability and a decrease in the number of simultaneous positive and negative yield anomalies among treatments. Especially in root crops nutrient supply strongly influenced both annual yield anomalies and changes in anomalies over time. During the second half of the observation period yield stability decreased for sugar beet and increased for winter wheat. Potato yields were more stable during the second period, but only under complete nutrient supply. The critical role of potassium supply for yield stability suggests potential links to changes in the water balance during the last decades. Results demonstrate the need to explicitly consider the response of crops to long-term nutrient supply for understanding and predicting changes in yield stability.},
    }

  • T. Boas, H. Bogena, T. Grünwald, B. Heinesch, D. Ryu, M. Schmidt, H. Vereecken, A. Western, and H. -J. Hendricks-Franssen, "Improving the representation of cropland sites in the Community Land Model (CLM) version 5.0," Geoscientific Model Development Discussions, vol. 2020, p. 1–37, 2020. doi:10.5194/gmd-2020-241
    [BibTeX] [PDF]
    @Article{gmd-2020-241,
    author = {Boas, T. and Bogena, H. and Gr\"unwald, T. and Heinesch, B. and Ryu, D. and Schmidt, M. and Vereecken, H. and Western, A. and Hendricks-Franssen, H.-J.},
    title = {Improving the representation of cropland sites in the Community Land Model (CLM) version 5.0},
    journal = {Geoscientific Model Development Discussions},
    volume = {2020},
    year = {2020},
    pages = {1--37},
    url = {https://gmd.copernicus.org/preprints/gmd-2020-241/},
    doi = {10.5194/gmd-2020-241},
    }

  • P. Welke, F. Seiffarth, M. Kamp, and S. Wrobel, "HOPS: Probabilistic Subtree Mining for Small and Large Graphs," in Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining , New York, NY, USA, 2020, p. 1275–1284. doi:10.1145/3394486.3403180
    [BibTeX] [PDF]

    Frequent subgraph mining, i.e., the identification of relevant patterns in graph databases, is a well-known data mining problem with high practical relevance, since next to summarizing the data, the resulting patterns can also be used to define powerful domain-specific similarity functions for prediction. In recent years, significant progress has been made towards subgraph mining algorithms that scale to complex graphs by focusing on tree patterns and probabilistically allowing a small amount of incompleteness in the result. Nonetheless, the complexity of the pattern matching component used for deciding subtree isomorphism on arbitrary graphs has significantly limited the scalability of existing approaches. In this paper, we adapt sampling techniques from mathematical combinatorics to the problem of probabilistic subtree mining in arbitrary databases of many small to medium-size graphs or a single large graph. By restricting on tree patterns, we provide an algorithm that approximately counts or decides subtree isomorphism for arbitrary transaction graphs in sub-linear time with one-sided error. Our empirical evaluation on a range of benchmark graph datasets shows that the novel algorithm substantially outperforms state-of-the-art approaches both in the task of approximate counting of embeddings in single large graphs and in probabilistic frequent subtree mining in large databases of small to medium sized graphs.

    @InProceedings{10.1145/3394486.3403180,
    author = {Welke, Pascal and Seiffarth, Florian and Kamp, Michael and Wrobel, Stefan},
    title = {HOPS: Probabilistic Subtree Mining for Small and Large Graphs},
    year = {2020},
    isbn = {9781450379984},
    publisher = {Association for Computing Machinery},
    address = {New York, NY, USA},
    url = {https://doi.org/10.1145/3394486.3403180},
    doi = {10.1145/3394486.3403180},
    abstract = {Frequent subgraph mining, i.e., the identification of relevant patterns in graph databases, is a well-known data mining problem with high practical relevance, since next to summarizing the data, the resulting patterns can also be used to define powerful domain-specific similarity functions for prediction. In recent years, significant progress has been made towards subgraph mining algorithms that scale to complex graphs by focusing on tree patterns and probabilistically allowing a small amount of incompleteness in the result. Nonetheless, the complexity of the pattern matching component used for deciding subtree isomorphism on arbitrary graphs has significantly limited the scalability of existing approaches. In this paper, we adapt sampling techniques from mathematical combinatorics to the problem of probabilistic subtree mining in arbitrary databases of many small to medium-size graphs or a single large graph. By restricting on tree patterns, we provide an algorithm that approximately counts or decides subtree isomorphism for arbitrary transaction graphs in sub-linear time with one-sided error. Our empirical evaluation on a range of benchmark graph datasets shows that the novel algorithm substantially outperforms state-of-the-art approaches both in the task of approximate counting of embeddings in single large graphs and in probabilistic frequent subtree mining in large databases of small to medium sized graphs.},
    booktitle = {Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining},
    pages = {1275–1284},
    numpages = {10},
    location = {Virtual Event, CA, USA},
    series = {KDD '20},
    }

  • A. Bonerath, B. Niedermann, J. Diederich, Y. Orgeig, J. Oehrlein, and J. Haunert, "A Time-Windowed Data Structure for Spatial Density Maps," in Proceedings of the 28th International Conference on Advances in Geographic Information Systems , New York, NY, USA, 2020, p. 15–24. doi:10.1145/3397536.3422242
    [BibTeX] [PDF]

    The visualization of spatio-temporal data helps researchers understand global processes such as animal migration. In particular, interactively restricting the data to different time windows reveals new insights into the short-term and long-term changes of the research data. Inspired by this use case, we consider the visualization of point data annotated with time stamps. We pick up classical, grid-based density maps as the underlying visualization technique and enhance them with an efficient data structure for arbitrarily specified time-window queries. The running time of the queries is logarithmic in the total number of points and linear in the number of actually colored cells. In experiments on real-world data we show that the data structure answers time-window queries within milliseconds, which supports the interactive exploration of large point sets. Further, the data structure can be used to visualize additional decision problems, e.g., it can answer whether the sum or maximum of additional weights given with the points exceed a certain threshold. We have defined the data structure general enough to also support multiple thresholds expressed by different colors.

    @InProceedings{bhn-twdssdm-20,
    abstract = {The visualization of spatio-temporal data helps researchers understand global processes such as animal migration. In particular, interactively restricting the data to different time windows reveals new insights into the short-term and long-term changes of the research data. Inspired by this use case, we consider the visualization of point data annotated with time stamps. We pick up classical, grid-based density maps as the underlying visualization technique and enhance them with an efficient data structure for arbitrarily specified time-window queries. The running time of the queries is logarithmic in the total number of points and linear in the number of actually colored cells. In experiments on real-world data we show that the data structure answers time-window queries within milliseconds, which supports the interactive exploration of large point sets. Further, the data structure can be used to visualize additional decision problems, e.g., it can answer whether the sum or maximum of additional weights given with the points exceed a certain threshold. We have defined the data structure general enough to also support multiple thresholds expressed by different colors.},
    address = {New York, NY, USA},
    author = {Bonerath, Annika and Niedermann, Benjamin and Diederich, Jim and Orgeig, Yannick and Oehrlein, Johannes and Haunert, Jan-Henrik},
    booktitle = {Proceedings of the 28th International Conference on Advances in Geographic Information Systems},
    doi = {10.1145/3397536.3422242},
    isbn = {9781450380195},
    keywords = {density maps, time-windowed data structure, point set},
    location = {Seattle, WA, USA},
    numpages = {10},
    pages = {15–24},
    publisher = {Association for Computing Machinery},
    series = {SIGSPATIAL '20},
    title = {A Time-Windowed Data Structure for Spatial Density Maps},
    url = {https://doi.org/10.1145/3397536.3422242},
    year = {2020},
    }

  • M. T. de Moraes, Debiasi H., J. C. Franchini, A. A. Mastroberti, R. Levien, D. Leitner, and A. Schnepf, "Soil compaction impacts soybean root growth in an Oxisol from subtropical Brazil," Soil & Tillage Research, vol. 200, 2020.
    [BibTeX]
    @Article{moraes2020str,
    title = "Soil compaction impacts soybean root growth in an Oxisol from subtropical Brazil",
    journal = "Soil \& Tillage Research",
    volume = "200",
    year = "2020",
    author = "de Moraes, M. T. and Debiasi, H., and Franchini, J. C. and Mastroberti, A. A. and Levien, R. and Leitner, D. and Schnepf, A.",
    }

  • N. Chebrolu, T. Läbe, and C. Stachniss, "Spatio-Temporal Non-Rigid Registration of 3D Point Clouds of Plants," in Proc. of the IEEE Intl. Conf. on Robotics & Automation (ICRA) , 2020.
    [BibTeX] [PDF] [Video]
    @InProceedings{chebrolu2020icra,
    title = {Spatio-Temporal Non-Rigid Registration of 3D Point Clouds of Plants},
    author = {N. Chebrolu and T. Läbe and C. Stachniss},
    booktitle = {Proc. of the IEEE Intl. Conf. on Robotics \& Automation (ICRA)},
    year = {2020},
    url = {http://www.ipb.uni-bonn.de/pdfs/chebrolu2020icra.pdf},
    videourl = {https://www.youtube.com/watch?v=uGkep_aelBc},
    }

  • A. Ahmadi, L. Nardi, N. Chebrolu, and C. Stachniss, "Visual Servoing-based Navigation for Monitoring Row-Crop Fields," in Proc. of the IEEE Intl. Conf. on Robotics & Automation (ICRA) , 2020.
    [BibTeX] [PDF] [Code] [Video]
    @InProceedings{ahmadi2020icra,
    title = {Visual Servoing-based Navigation for Monitoring Row-Crop Fields},
    author = {A. Ahmadi and L. Nardi and N. Chebrolu and C. Stachniss},
    booktitle = {Proc. of the IEEE Intl. Conf. on Robotics \& Automation (ICRA)},
    year = {2020},
    url = {http://arxiv.org/pdf/1909.12754},
    codeurl = {https://github.com/PRBonn/visual-crop-row-navigation},
    videourl = {https://youtu.be/0qg6n4sshHk},
    }

  • X. Wu, S. Aravecchia, P. Lottes, C. Stachniss, and C. Pradalier, "Robotic Weed Control Using Automated Weed and Crop Classification," Journal of Field Robotics, vol. 37, pp. 322-340, 2020.
    [BibTeX] [PDF]
    @Article{wu2020jfr,
    title = {Robotic Weed Control Using Automated Weed and Crop Classification},
    author = {X. Wu and S. Aravecchia and P. Lottes and C. Stachniss and C. Pradalier},
    journal = {Journal of Field Robotics},
    year = {2020},
    volume = {37},
    numer = {2},
    pages = {322-340},
    url = {http://www.ipb.uni-bonn.de/pdfs/wu2020jfr.pdf},
    }

  • L. Nardi and C. Stachniss, "Long-Term Robot Navigation in Indoor Environments Estimating Patterns in Traversability Changes," in Proc. of the IEEE Intl. Conf. on Robotics & Automation (ICRA) , 2020.
    [BibTeX] [PDF] [Video]
    @InProceedings{nardi2020icra,
    title = {Long-Term Robot Navigation in Indoor Environments Estimating Patterns in Traversability Changes},
    author = {L. Nardi and C. Stachniss},
    booktitle = {Proc. of the IEEE Intl. Conf. on Robotics \& Automation (ICRA)},
    year = {2020},
    url = {http://arxiv.org/pdf/1909.12733},
    videourl = {https://www.youtube.com/watch?v=9lNcA3quzwU},
    }

  • X. Chen, T. Läbe, A. Milioto, T. Röhling, O. Vysotska, A. Haag, J. Behley, and C. Stachniss, "OverlapNet: Loop Closing for LiDAR-based SLAM," in Proceedings of Robotics: Science and Systems (RSS) , 2020.
    [BibTeX] [PDF] [Code] [Video]
    @InProceedings{chen2020rss,
    author = {X. Chen and T. L\"abe and A. Milioto and T. R\"ohling and O. Vysotska and A. Haag and J. Behley and C. Stachniss},
    title = {{OverlapNet: Loop Closing for LiDAR-based SLAM}},
    booktitle = {Proceedings of Robotics: Science and Systems (RSS)},
    year = {2020},
    codeurl = {https://github.com/PRBonn/OverlapNet/},
    videourl = {https://youtu.be/YTfliBco6aw},
    url = {https://www.ipb.uni-bonn.de/wp-content/papercite-data/pdf/chen2020rss.pdf},
    }

  • A. Milioto, J. Behley, C. McCool, and C. Stachniss, "LiDAR Panoptic Segmentation for Autonomous Driving." 2020.
    [BibTeX] [PDF] [Video]
    @InProceedings{milioto2020iros,
    author = {A. Milioto and J. Behley and C. McCool and C. Stachniss},
    title = {{LiDAR Panoptic Segmentation for Autonomous Driving}},
    booktitle = iros,
    year = {2020},
    videourl = {https://www.youtube.com/watch?v=C9CTQSosr9I},
    url = {https://www.ipb.uni-bonn.de/wp-content/papercite-data/pdf/milioto2020iros.pdf},
    }

  • S. R. Tracy, K. A. Nagel, J. A. Postma, H. Fassbender, A. Wasson, and M. Watt, "Crop Improvement from Phenotyping Roots: Highlights Reveal Expanding Opportunities," Trends in Plant Science, vol. 25, iss. 1, pp. 105-118, 2020. doi:https://doi.org/10.1016/j.tplants.2019.10.015
    [BibTeX] [PDF]

    Root systems determine the water and nutrients for photosynthesis and harvested products, underpinning agricultural productivity. We highlight 11 programs that integrated root traits into germplasm for breeding, relying on phenotyping. Progress was successful but slow. Today’s phenotyping technologies will speed up root trait improvement. They combine multiple new alleles in germplasm for target environments, in parallel. Roots and shoots are detected simultaneously and nondestructively, seed to seed measures are automated, and field and laboratory technologies are increasingly linked. Available simulation models can aid all phenotyping decisions. This century will see a shift from single root traits to rhizosphere selections that can be managed dynamically on farms and a shift to phenotype-based improvement to accommodate the dynamic complexity of whole crop systems.

    @Article{tracy2020105,
    title = "Crop Improvement from Phenotyping Roots: Highlights Reveal Expanding Opportunities",
    journal = "Trends in Plant Science",
    volume = "25",
    number = "1",
    pages = "105 - 118",
    year = "2020",
    issn = "1360-1385",
    doi = "https://doi.org/10.1016/j.tplants.2019.10.015",
    url = "http://www.sciencedirect.com/science/article/pii/S1360138519302845",
    author = "Saoirse R. Tracy and Kerstin A. Nagel and Johannes A. Postma and Heike Fassbender and Anton Wasson and Michelle Watt",
    keywords = "root architecture, simulation model, combinatorial stresses, rhizosphere, agronomy, soil, breeding, water, climate, imaging",
    abstract = "Root systems determine the water and nutrients for photosynthesis and harvested products, underpinning agricultural productivity. We highlight 11 programs that integrated root traits into germplasm for breeding, relying on phenotyping. Progress was successful but slow. Today’s phenotyping technologies will speed up root trait improvement. They combine multiple new alleles in germplasm for target environments, in parallel. Roots and shoots are detected simultaneously and nondestructively, seed to seed measures are automated, and field and laboratory technologies are increasingly linked. Available simulation models can aid all phenotyping decisions. This century will see a shift from single root traits to rhizosphere selections that can be managed dynamically on farms and a shift to phenotype-based improvement to accommodate the dynamic complexity of whole crop systems.",
    }

  • D. Wallach, T. Palosuo, P. Thorburn, Z. Hochman, E. Gourdain, F. Andrianasolo, S. Asseng, B. Basso, S. Buis, N. Crout, C. Dibari, B. Dumont, R. Ferrise, T. Gaiser, C. Garcia, S. Gayler, A. Ghahramani, S. Hiremath, S. Hoek, H. Horan, G. Hoogenboom, M. Huang, M. Jabloun, P. Jansson, Q. Jing, E. Justes, K. C. Kersebaum, A. Klosterhalfen, M. Launay, E. Lewan, Q. Luo, B. Maestrini, H. Mielenz, M. Moriondo, H. N. Zadeh, G. Padovan, J. E. Olesen, A. Poyda, E. Priesack, J. W. M. Pullens, B. Qian, N. Schütze, V. Shelia, A. Souissi, X. Specka, A. K. Srivastava, T. Stella, T. Streck, G. Trombi, E. Wallor, J. Wang, T. K. D. Weber, L. Weihermüller, A. de Wit, T. Wöhling, L. Xiao, C. Zhao, Y. Zhu, and S. J. Seidel, "The chaos in calibrating crop models," bioRxiv, 2020. doi:10.1101/2020.09.12.294744
    [BibTeX] [PDF]

    Calibration, the estimation of model parameters based on fitting the model to experimental data, is among the first steps in essentially every application of crop models and process models in other fields and has an important impact on simulated values. The goal of this study is to develop a comprehensive list of the decisions involved in calibration and to identify the range of choices made in practice, as groundwork for developing guidelines for crop model calibration starting with phenology. Three groups of decisions are identified; the criterion for choosing the parameter values, the choice of parameters to estimate and numerical aspects of parameter estimation. It is found that in practice there is a large diversity of choices for every decision, even among modeling groups using the same model structure. These findings are relevant to process models in other fields.HighlightsWe documented calibration procedures in two multi-model studiesGroups differ in criteria for best parameters, parameters to estimate and softwareThere are important differences even between groups using the same model structureCompeting Interest StatementThe authors have declared no competing interest.

    @Article{wallach2020.09.12.294744,
    author = {Wallach, Daniel and Palosuo, Taru and Thorburn, Peter and Hochman, Zvi and Gourdain, Emmanuelle and Andrianasolo, Fety and Asseng, Senthold and Basso, Bruno and Buis, Samuel and Crout, Neil and Dibari, Camilla and Dumont, Benjamin and Ferrise, Roberto and Gaiser, Thomas and Garcia, Cecile and Gayler, Sebastian and Ghahramani, Afshin and Hiremath, Santosh and Hoek, Steven and Horan, Heidi and Hoogenboom, Gerrit and Huang, Mingxia and Jabloun, Mohamed and Jansson, Per-Erik and Jing, Qi and Justes, Eric and Kersebaum, Kurt Christian and Klosterhalfen, Anne and Launay, Marie and Lewan, Elisabet and Luo, Qunying and Maestrini, Bernardo and Mielenz, Henrike and Moriondo, Marco and Zadeh, Hasti Nariman and Padovan, Gloria and Olesen, J{\o}rgen Eivind and Poyda, Arne and Priesack, Eckart and Pullens, Johannes Wilhelmus Maria and Qian, Budong and Sch{\"u}tze, Niels and Shelia, Vakhtang and Souissi, Amir and Specka, Xenia and Srivastava, Amit Kumar and Stella, Tommaso and Streck, Thilo and Trombi, Giacomo and Wallor, Evelyn and Wang, Jing and Weber, Tobias K.D. and Weiherm{\"u}ller, Lutz and de Wit, Allard and W{\"o}hling, Thomas and Xiao, Liujun and Zhao, Chuang and Zhu, Yan and Seidel, Sabine J.},
    title = {The chaos in calibrating crop models},
    elocation-id = {2020.09.12.294744},
    year = {2020},
    doi = {10.1101/2020.09.12.294744},
    publisher = {Cold Spring Harbor Laboratory},
    abstract = {Calibration, the estimation of model parameters based on fitting the model to experimental data, is among the first steps in essentially every application of crop models and process models in other fields and has an important impact on simulated values. The goal of this study is to develop a comprehensive list of the decisions involved in calibration and to identify the range of choices made in practice, as groundwork for developing guidelines for crop model calibration starting with phenology. Three groups of decisions are identified; the criterion for choosing the parameter values, the choice of parameters to estimate and numerical aspects of parameter estimation. It is found that in practice there is a large diversity of choices for every decision, even among modeling groups using the same model structure. These findings are relevant to process models in other fields.HighlightsWe documented calibration procedures in two multi-model studiesGroups differ in criteria for best parameters, parameters to estimate and softwareThere are important differences even between groups using the same model structureCompeting Interest StatementThe authors have declared no competing interest.},
    url = {https://www.biorxiv.org/content/early/2020/09/14/2020.09.12.294744},
    eprint = {https://www.biorxiv.org/content/early/2020/09/14/2020.09.12.294744.full.pdf},
    journal = {bioRxiv},
    }

  • O. Zatsarynna, J. Sawatzky, and J. Gall, "Discovering Latent Classes for Semi-Supervised Semantic Segmentation," in DAGM German Conference on Pattern Recognition (GCPR) , 2020.
    [BibTeX] [PDF]
    @InProceedings{zatsarynna2020gcpr,
    author = {Zatsarynna, O. and Sawatzky, J. and Gall, J.},
    title = {{Discovering Latent Classes for Semi-Supervised Semantic Segmentation}},
    booktitle = {DAGM German Conference on Pattern Recognition (GCPR)},
    year = 2020,
    url = {http://pages.iai.uni-bonn.de/gall_juergen/download/jgall_latentclasses_gcpr2020.pdf},
    }

  • W. Amelung, D. Bossio, W. de Vries, I. Kögel-Knabner, J. Lehmann, R. Amundson, R. Bol, C. Collins, R. Lal, J. Leifeld, B. Minasny, G. Pan, K. Paustian, C. Rumpel, J. Sanderman, J. W. van Groenigen, S. Mooney, B. van Wesemael, M. Wander, and A. Chabbi, "Towards a global-scale soil climate mitigation strategy," Nature Communications, vol. 11, 2020. doi:10.1038/s41467-020-18887-7
    [BibTeX] [PDF]

    {Sustainable soil carbon sequestration practices need to be rapidly scaled up and implemented to contribute to climate change mitigation. We highlight that the major potential for carbon sequestration is in cropland soils, especially those with large yield gaps and/or large historic soil organic carbon losses. The implementation of soil carbon sequestration measures requires a diverse set of options, each adapted to local soil conditions and management opportunities, and accounting for site-specific trade-offs. We propose the establishment of a soil information system containing localised information on soil group, degradation status, crop yield gap, and the associated carbon-sequestration potentials, as well as the provision of incentives and policies to translate management options into region- and soil-specific practices.}

    @Article{amelung2020,
    author = {W. Amelung and D. Bossio and W. de Vries and I. Kögel-Knabner and J. Lehmann and R. Amundson and R. Bol and C. Collins and R. Lal and J. Leifeld and B. Minasny and G. Pan and K. Paustian and C. Rumpel and J. Sanderman and J. W. van Groenigen and S. Mooney and B. van Wesemael and M. Wander and A. Chabbi},
    title = "{Towards a global-scale soil climate mitigation strategy}",
    journal = {Nature Communications},
    volume = {11},
    year = {2020},
    month = {10},
    abstract = "{Sustainable soil carbon sequestration practices need to be rapidly scaled up and implemented to contribute to climate change mitigation. We highlight that the major potential for carbon sequestration is in cropland soils, especially those with large yield gaps and/or large historic soil organic carbon losses. The implementation of soil carbon sequestration measures requires a diverse set of options, each adapted to local soil conditions and management opportunities, and accounting for site-specific trade-offs. We propose the establishment of a soil information system containing localised information on soil group, degradation status, crop yield gap, and the associated carbon-sequestration potentials, as well as the provision of incentives and policies to translate management options into region- and soil-specific practices.}",
    doi = {10.1038/s41467-020-18887-7},
    url = {https://doi.org/10.1038/s41467-020-18887-7},
    }

  • H. Webber, G. Lischeid, M. Sommer, R. Finger, C. Nendel, T. Gaiser, and F. Ewert, "No perfect storm for crop yield failure in Germany," Environmental Research Letters, vol. 15, iss. 10, p. 104012, 2020. doi:10.1088/1748-9326/aba2a4
    [BibTeX] [PDF]

    Large-scale crop yield failures are increasingly associated with food price spikes and food insecurity and are a large source of income risk for farmers. While the evidence linking extreme weather to yield failures is clear, consensus on the broader set of weather drivers and conditions responsible for recent yield failures is lacking. We investigate this for the case of four major crops in Germany over the past 20 years using a combination of machine learning and process-based modelling. Our results confirm that years associated with widespread yield failures across crops were generally associated with severe drought, such as in 2018 and to a lesser extent 2003. However, for years with more localized yield failures and large differences in spatial patterns of yield failures between crops, no single driver or combination of drivers was identified. Relatively large residuals of unexplained variation likely indicate the importance of non-weather related factors, such as management (pest, weed and nutrient management and possible interactions with weather) explaining yield failures. Models to inform adaptation planning at farm, market or policy levels are here suggested to require consideration of cumulative resource capture and use, as well as effects of extreme events, the latter largely missing in process-based models. However, increasingly novel combinations of weather events under climate change may limit the extent to which data driven methods can replace process-based models in risk assessments.

    @Article{webber_2020,
    doi = {10.1088/1748-9326/aba2a4},
    url = {https://doi.org/10.1088%2F1748-9326%2Faba2a4},
    year = 2020,
    month = {sep},
    publisher = {{IOP} Publishing},
    volume = {15},
    number = {10},
    pages = {104012},
    author = {Heidi Webber and Gunnar Lischeid and Michael Sommer and Robert Finger and Claas Nendel and Thomas Gaiser and Frank Ewert},
    title = {No perfect storm for crop yield failure in Germany},
    journal = {Environmental Research Letters},
    abstract = {Large-scale crop yield failures are increasingly associated with food price spikes and food insecurity and are a large source of income risk for farmers. While the evidence linking extreme weather to yield failures is clear, consensus on the broader set of weather drivers and conditions responsible for recent yield failures is lacking. We investigate this for the case of four major crops in Germany over the past 20 years using a combination of machine learning and process-based modelling. Our results confirm that years associated with widespread yield failures across crops were generally associated with severe drought, such as in 2018 and to a lesser extent 2003. However, for years with more localized yield failures and large differences in spatial patterns of yield failures between crops, no single driver or combination of drivers was identified. Relatively large residuals of unexplained variation likely indicate the importance of non-weather related factors, such as management (pest, weed and nutrient management and possible interactions with weather) explaining yield failures. Models to inform adaptation planning at farm, market or policy levels are here suggested to require consideration of cumulative resource capture and use, as well as effects of extreme events, the latter largely missing in process-based models. However, increasingly novel combinations of weather events under climate change may limit the extent to which data driven methods can replace process-based models in risk assessments.},
    }

  • J. Yi, L. Krusenbaum, P. Unger, H. Hüging, S. J. Seidel, G. Schaaf, and J. Gall, "Deep Learning for Non-Invasive Diagnosis of Nutrient Deficiencies in Sugar Beet Using RGB Images," Sensors, vol. 12, 2020. doi:10.3390/s20205893
    [BibTeX] [PDF]

    {In order to enable timely actions to prevent major losses of crops caused by lack of nutrients and, hence, increase the potential yield throughout the growing season while at the same time prevent excess fertilization with detrimental environmental consequences, early, non-invasive, and on-site detection of nutrient deficiency is required. Current non-invasive methods for assessing the nutrient status of crops deal in most cases with nitrogen (N) deficiency only and optical sensors to diagnose N deficiency, such as chlorophyll meters or canopy reflectance sensors, do not monitor N, but instead measure changes in leaf spectral properties that may or may not be caused by N deficiency. In this work, we study how well nutrient deficiency symptoms can be recognized in RGB images of sugar beets. To this end, we collected the Deep Nutrient Deficiency for Sugar Beet (DND-SB) dataset, which contains 5648 images of sugar beets growing on a long-term fertilizer experiment with nutrient deficiency plots comprising N, phosphorous (P), and potassium (K) deficiency, as well as the omission of liming (Ca), full fertilization, and no fertilization at all. We use the dataset to analyse the performance of five convolutional neural networks for recognizing nutrient deficiency symptoms and discuss their limitations.}

    @Article{yi2020deep,
    author = {Yi, Jinhui and Krusenbaum, Lukas and Unger, Paula and Hüging, Hubert and Seidel, Sabine J and Schaaf, Gabriel and Gall, Juergen},
    title = "{Deep Learning for Non-Invasive Diagnosis of Nutrient Deficiencies in Sugar Beet Using RGB Images}",
    journal = {Sensors},
    volume = {12},
    year = {2020},
    month = {10},
    abstract = "{In order to enable timely actions to prevent major losses of crops caused by lack of nutrients and, hence, increase the potential yield throughout the growing season while at the same time prevent excess fertilization with detrimental environmental consequences, early, non-invasive, and on-site detection of nutrient deficiency is required. Current non-invasive methods for assessing the nutrient status of crops deal in most cases with nitrogen (N) deficiency only and optical sensors to diagnose N deficiency, such as chlorophyll meters or canopy reflectance sensors, do not monitor N, but instead measure changes in leaf spectral properties that may or may not be caused by N deficiency. In this work, we study how well nutrient deficiency symptoms can be recognized in RGB images of sugar beets. To this end, we collected the Deep Nutrient Deficiency for Sugar Beet (DND-SB) dataset, which contains 5648 images of sugar beets growing on a long-term fertilizer experiment with nutrient deficiency plots comprising N, phosphorous (P), and potassium (K) deficiency, as well as the omission of liming (Ca), full fertilization, and no fertilization at all. We use the dataset to analyse the performance of five convolutional neural networks for recognizing nutrient deficiency symptoms and discuss their limitations.}",
    doi = {10.3390/s20205893},
    url = {https://doi.org/10.3390/s20205893},
    }

  • B. H. Gebrekidan, T. Heckelei, and S. Rasch, "Characterizing Farmers and Farming System in Kilombero Valley Floodplain, Tanzania," Sustainability, vol. 12, 2020. doi:10.3390/su12177114
    [BibTeX] [PDF]

    {Recognizing the diversity of farmers is crucial for the success of agricultural, rural, or environmental programs and policies aimed at the sustainable use of natural resources. In this study, based on survey data collected in the Kilombero Valley Floodplain (KVF) in Tanzania, we design a typology of farmers to describe the range of farm types and farming systems systematically, and to understand their livelihood and land use behavior. The KVF is the largest, low-altitude, seasonally-flooded, freshwater wetland in East Africa. Despite its values, KVF is a very fragile ecosystem threatened by current and future human interventions. We apply multivariate statistical analysis (a combination of principal component analysis and cluster analysis) to identify farm groups that are homogenous within and heterogeneous between groups. Three farm types were identified: “Monocrop rice producer”, “Diversifier”, and “Agropastoralist”. Monocrop rice producers are the dominant farm types, accounting for 65 percent of the farm households in the valley, characterized by more than 80 percent of the land allocated to rice, showing strong market participation and high utilization of labor. Diversifiers, on the other hand, allocate more land to maize and vegetables. Agropastoralists account for 7 percent of the surveyed farmers and differ from the other two groups by, on average, larger land ownership, a combination of livestock and crop production, and larger household sizes. This typology represents the diversity of farmers in KVF concerning their land use and livelihood strategy, and will allow to target policy interventions. Besides, it may also inform further research about the diverse landscape of floodplain farming, through the classification and interpretation of different socio-economic positions of farm households.}

    @Article{gebrekidan2020,
    author = {Gebrekidan, Bisrat Haile and Heckelei, Thomas and Rasch, Sebastian},
    title = "{Characterizing Farmers and Farming System in Kilombero Valley Floodplain, Tanzania}",
    journal = {Sustainability},
    volume = {12},
    year = {2020},
    month = {08},
    abstract = "{Recognizing the diversity of farmers is crucial for the success of agricultural, rural, or environmental programs and policies aimed at the sustainable use of natural resources. In this study, based on survey data collected in the Kilombero Valley Floodplain (KVF) in Tanzania, we design a typology of farmers to describe the range of farm types and farming systems systematically, and to understand their livelihood and land use behavior. The KVF is the largest, low-altitude, seasonally-flooded, freshwater wetland in East Africa. Despite its values, KVF is a very fragile ecosystem threatened by current and future human interventions. We apply multivariate statistical analysis (a combination of principal component analysis and cluster analysis) to identify farm groups that are homogenous within and heterogeneous between groups. Three farm types were identified: “Monocrop rice producer”, “Diversifier”, and “Agropastoralist”. Monocrop rice producers are the dominant farm types, accounting for 65 percent of the farm households in the valley, characterized by more than 80 percent of the land allocated to rice, showing strong market participation and high utilization of labor. Diversifiers, on the other hand, allocate more land to maize and vegetables. Agropastoralists account for 7 percent of the surveyed farmers and differ from the other two groups by, on average, larger land ownership, a combination of livestock and crop production, and larger household sizes. This typology represents the diversity of farmers in KVF concerning their land use and livelihood strategy, and will allow to target policy interventions. Besides, it may also inform further research about the diverse landscape of floodplain farming, through the classification and interpretation of different socio-economic positions of farm households.}",
    doi = {10.3390/su12177114},
    url = {https://doi.org/10.3390/su12177114},
    }

  • T. Zaenker, F. Verdoja, and V. Kyrki, "Hypermap Mapping Framework and its Application to Autonomous Semantic Exploration," in 2020 IEEE Conference on Multisensor Fusion and Integration , 2020.
    [BibTeX] [PDF]
    @InProceedings{zaenker2020hypermap,
    title = {Hypermap Mapping Framework and its Application to Autonomous Semantic Exploration},
    author = {Zaenker, Tobias and Verdoja, Francesco and Kyrki, Ville},
    booktitle = {2020 IEEE Conference on Multisensor Fusion and Integration},
    year = {2020},
    organization = {IEEE},
    url = {https://arxiv.org/pdf/1909.09526.pdf},
    }

  • C. Gebauer and M. Bennewitz, "Penalized Bootstrapping for Reinforcement Learning in Robot Control," in 2nd International Conference on Machine Learning & Applications , 2020.
    [BibTeX] [PDF]
    @InProceedings{gebauer2020penalized,
    title = {Penalized Bootstrapping for Reinforcement Learning in Robot Control},
    author = {Gebauer, Christopher and Bennewitz, Maren},
    booktitle = {2nd International Conference on Machine Learning \& Applications},
    year = {2020},
    url = {https://www.hrl.uni-bonn.de/publications/papers/gebauer20deeprl.pdf},
    }

  • P. Regier, L. Gesing, and M. Bennewitz, "Deep Reinforcement Learning for Navigation in Cluttered Environments," in 2nd International Conference on Machine Learning & Applications , 2020.
    [BibTeX] [PDF]
    @InProceedings{regier2020deep,
    title = {Deep Reinforcement Learning for Navigation in Cluttered Environments},
    author = {Regier, Peter and Gesing, Lukas and Bennewitz, Maren},
    booktitle = {2nd International Conference on Machine Learning \& Applications},
    year = {2020},
    url = {https://www.hrl.uni-bonn.de/publications/papers/regier20cmla.pdf},
    }

  • D. Gogoll, P. Lottes, J. Weyler, N. Petrinic, and C. Stachniss, "Unsupervised Domain Adaptation for Transferring Plant Classification Systems to New Field Environments, Crops, and Robots," in iros , 2020.
    [BibTeX] [PDF] [Video]
    @InProceedings{gogoll2020iros,
    author = {D. Gogoll and P. Lottes and J. Weyler and N. Petrinic and C. Stachniss},
    title = {{Unsupervised Domain Adaptation for Transferring Plant Classification Systems to New Field Environments, Crops, and Robots}},
    booktitle = iros,
    year = {2020},
    url = {http://www.ipb.uni-bonn.de/pdfs/gogoll2020iros.pdf},
    videourl = {https://www.youtube.com/watch?v=6K79Ih6KXTs},
    }

  • F. Magistri, N. Chebrolu, and C. Stachniss, "Segmentation-Based 4D Registration of Plants Point Clouds for Phenotyping," in iros , 2020.
    [BibTeX] [PDF] [Video]
    @InProceedings{magistri2020iros,
    author = {F. Magistri and N. Chebrolu and C. Stachniss},
    title = {{Segmentation-Based 4D Registration of Plants Point Clouds for Phenotyping}},
    booktitle = iros,
    year = {2020},
    url = {https://www.ipb.uni-bonn.de/pdfs/magistri2020iros.pdf},
    videourl = {https://youtu.be/OV39kb5Nqg8},
    }

  • X. Chen, T. Läbe, L. Nardi, J. Behley, and C. Stachniss, "Learning an Overlap-based Observation Model for 3D LiDAR Localization," in iros , 2020.
    [BibTeX] [PDF] [Video]
    @InProceedings{chen2020iros,
    author = {X. Chen and T. L\"abe and L. Nardi and J. Behley and C. Stachniss},
    title = {{Learning an Overlap-based Observation Model for 3D LiDAR Localization}},
    booktitle = iros,
    year = {2020},
    url = {http://www.ipb.uni-bonn.de/pdfs/chen2020iros.pdf},
    videourl = {https://www.youtube.com/watch?v=BozPqy_6YcE},
    }

  • F. Langer, A. Milioto, A. Haag, J. Behley, and C. Stachniss, "Domain Transfer for Semantic Segmentation of LiDAR Data using Deep Neural Networks," in iros , 2020.
    [BibTeX] [PDF] [Video]
    @InProceedings{langer2020iros,
    author = {F. Langer and A. Milioto and A. Haag and J. Behley and C. Stachniss},
    title = {{Domain Transfer for Semantic Segmentation of LiDAR Data using Deep Neural Networks}},
    booktitle = iros,
    year = {2020},
    url = {http://www.ipb.uni-bonn.de/pdfs/langer2020iros.pdf},
    videourl = {https://youtu.be/6FNGF4hKBD0},
    }

  • R. Sheikh, A. Milioto, P. Lottes, C. Stachniss, M. Bennewitz, and T. Schultz, "Gradient and Log-based Active Learning for Semantic Segmentation of Crop and Weed for Agricultural Robots." 2020.
    [BibTeX] [PDF] [Video]
    @InProceedings{sheikh2020icra,
    title = {Gradient and Log-based Active Learning for Semantic Segmentation of Crop and Weed for Agricultural Robots},
    author = {R. Sheikh and A. Milioto and P. Lottes and C. Stachniss and M. Bennewitz and T. Schultz},
    booktitle = icra,
    year = {2020},
    url = {http://www.ipb.uni-bonn.de/pdfs/sheikh2020icra.pdf},
    videourl = {https://www.youtube.com/watch?v=NySa59gxFAg},
    }

  • B. Heeren, S. Paulus, H. Goldbach, H. Kuhlmann, A. Mahlein, M. Rumpf, and B. Wirth, "Statistical shape analysis of tap roots: a methodological case study on laser scanned sugar beets," BMC Bioinformatics, vol. 21, iss. 1, 2020. doi:10.1186/s12859-020-03654-8
    [BibTeX] [PDF]

    {The efficient and robust statistical analysis of the shape of plant organs of different cultivars is an important investigation issue in plant breeding and enables a robust cultivar description within the breeding progress. Laserscanning is a highly accurate and high resolution technique to acquire the 3D shape of plant surfaces. The computation of a shape based principal component analysis (PCA) built on concepts from continuum mechanics has proven to be an effective tool for a qualitative and quantitative shape examination.}

    @Article{heeren2020,
    author = {Heeren, Behrend and Paulus, Stefan and Goldbach, Heiner and Kuhlmann, Heiner and Mahlein, Anne-Katrin and Rumpf, Martin and Wirth, Benedikt},
    title = "{Statistical shape analysis of tap roots: a methodological case study on laser scanned sugar beets}",
    journal = {BMC Bioinformatics},
    volume = {21},
    number = {1},
    year = {2020},
    month = {07},
    abstract = "{The efficient and robust statistical analysis of the shape of plant organs of different cultivars is an important investigation issue in plant breeding and enables a robust cultivar description within the breeding progress. Laserscanning is a highly accurate and high resolution technique to acquire the 3D shape of plant surfaces. The computation of a shape based principal component analysis (PCA) built on concepts from continuum mechanics has proven to be an effective tool for a qualitative and quantitative shape examination.}",
    issn = {1471-2105},
    doi = {10.1186/s12859-020-03654-8},
    url = {https://doi.org/10.1186/s12859-020-03654-8},
    }

  • S. Paulus and A. Mahlein, "Technical workflows for hyperspectral plant image assessment and processing on the greenhouse and laboratory scale," GigaScience, vol. 9, iss. 8, 2020. doi:10.1093/gigascience/giaa090
    [BibTeX] [PDF]

    {The use of hyperspectral cameras is well established in the field of plant phenotyping, especially as a part of high-throughput routines in greenhouses. Nevertheless, the workflows used differ depending on the applied camera, the plants being imaged, the experience of the users, and the measurement set-up.This review describes a general workflow for the assessment and processing of hyperspectral plant data at greenhouse and laboratory scale. Aiming at a detailed description of possible error sources, a comprehensive literature review of possibilities to overcome these errors and influences is provided. The processing of hyperspectral data of plants starting from the hardware sensor calibration, the software processing steps to overcome sensor inaccuracies, and the preparation for machine learning is shown and described in detail. Furthermore, plant traits extracted from spectral hypercubes are categorized to standardize the terms used when describing hyperspectral traits in plant phenotyping. A scientific data perspective is introduced covering information for canopy, single organs, plant development, and also combined traits coming from spectral and 3D measuring devices.This publication provides a structured overview on implementing hyperspectral imaging into biological studies at greenhouse and laboratory scale. Workflows have been categorized to define a trait-level scale according to their metrological level and the processing complexity. A general workflow is shown to outline procedures and requirements to provide fully calibrated data of the highest quality. This is essential for differentiation of the smallest changes from hyperspectral reflectance of plants, to track and trace hyperspectral development as an answer to biotic or abiotic stresses.}

    @Article{10.1093/gigascience/giaa090,
    author = {Paulus, Stefan and Mahlein, Anne-Katrin},
    title = "{Technical workflows for hyperspectral plant image assessment and processing on the greenhouse and laboratory scale}",
    journal = {GigaScience},
    volume = {9},
    number = {8},
    year = {2020},
    month = {08},
    abstract = "{The use of hyperspectral cameras is well established in the field of plant phenotyping, especially as a part of high-throughput routines in greenhouses. Nevertheless, the workflows used differ depending on the applied camera, the plants being imaged, the experience of the users, and the measurement set-up.This review describes a general workflow for the assessment and processing of hyperspectral plant data at greenhouse and laboratory scale. Aiming at a detailed description of possible error sources, a comprehensive literature review of possibilities to overcome these errors and influences is provided. The processing of hyperspectral data of plants starting from the hardware sensor calibration, the software processing steps to overcome sensor inaccuracies, and the preparation for machine learning is shown and described in detail. Furthermore, plant traits extracted from spectral hypercubes are categorized to standardize the terms used when describing hyperspectral traits in plant phenotyping. A scientific data perspective is introduced covering information for canopy, single organs, plant development, and also combined traits coming from spectral and 3D measuring devices.This publication provides a structured overview on implementing hyperspectral imaging into biological studies at greenhouse and laboratory scale. Workflows have been categorized to define a trait-level scale according to their metrological level and the processing complexity. A general workflow is shown to outline procedures and requirements to provide fully calibrated data of the highest quality. This is essential for differentiation of the smallest changes from hyperspectral reflectance of plants, to track and trace hyperspectral development as an answer to biotic or abiotic stresses.}",
    issn = {2047-217X},
    doi = {10.1093/gigascience/giaa090},
    url = {https://doi.org/10.1093/gigascience/giaa090},
    note = {giaa090},
    eprint = {https://academic.oup.com/gigascience/article-pdf/9/8/giaa090/33667602/giaa090.pdf},
    }

  • P. Schramowski, W. Stammer, S. Teso, A. Brugger, F. Herbert, X. Shao, H. Luigs, A. Mahlein, and K. Kersting, "Making deep neural networks right for the right scientific reasons by interacting with their explanations," Nature Machine Intelligence, vol. 2, 2020. doi:10.1038/s42256-020-0212-3
    [BibTeX] [PDF]

    Deep neural networks have demonstrated excellent performances in many real-world applications. Unfortunately, they may show Clever Hans-like behaviour (making use of confounding factors within datasets) to achieve high performance. In this work we introduce the novel learning setting of explanatory interactive learning and illustrate its benefits on a plant phenotyping research task. Explanatory interactive learning adds the scientist into the training loop, who interactively revises the original model by providing feedback on its explanations. Our experimental results demonstrate that explanatory interactive learning can help to avoid Clever Hans moments in machine learning and encourages (or discourages, if appropriate) trust in the underlying model.

    @Article{schramowski2020,
    title = "Making deep neural networks right for the right scientific reasons by interacting with their explanations",
    journal = "Nature Machine Intelligence",
    volume = "2",
    year = "2020",
    issn = "2522-5839",
    doi = "10.1038/s42256-020-0212-3",
    url = "https://doi.org/10.1038/s42256-020-0212-3",
    author = "Schramowski, Patrick and Stammer, Wolfgang and Teso, Stefano and Brugger, Anna and Herbert, Franziska and Shao, Xiaoting and Luigs, Hans-Georg and Mahlein, Anne-Katrin and Kersting, Kristian",
    abstract = "Deep neural networks have demonstrated excellent performances in many real-world applications. Unfortunately, they may show Clever Hans-like behaviour (making use of confounding factors within datasets) to achieve high performance. In this work we introduce the novel learning setting of explanatory interactive learning and illustrate its benefits on a plant phenotyping research task. Explanatory interactive learning adds the scientist into the training loop, who interactively revises the original model by providing feedback on its explanations. Our experimental results demonstrate that explanatory interactive learning can help to avoid Clever Hans moments in machine learning and encourages (or discourages, if appropriate) trust in the underlying model.",
    }

  • M. Langensiepen, M. A. K. Jansen, A. Wingler, B. Demmig-Adams, W. W. Adams, I. C. Dodd, V. Fotopoulos, R. Snowdon, E. Fenollosa, M. C. De Tullio, G. Buck-Sorlin, and S. Munné-Bosch, "Linking integrative plant physiology with agronomy to sustain future plant production," Environmental and Experimental Botany, vol. 178, p. 104125, 2020. doi:https://doi.org/10.1016/j.envexpbot.2020.104125
    [BibTeX] [PDF]

    Sustainable production of high-quality food is one of today’s major challenges of agriculture. To achieve this goal, a better understanding of plant physiological processes and a more integrated approach with respect to current agronomical practices are needed. In this review, various examples of cooperation between integrative plant physiology and agronomy are discussed, and this demonstrates the complexity of these interrelations. The examples are meant to stimulate discussions on how both research areas can deliver solutions to avoid looming food crises due to population growth and climate change. In the last decades, unprecedented progress has been made in the understanding of how plants grow and develop in a variety of environments and in response to biotic stresses, but appropriate management and interpretation of the resulting complex datasets remains challenging. After providing an historical overview of integrative plant physiology, we discuss possible avenues of integration, involving advances in integrative plant physiology, to sustain plant production in the current post-omics era. Finally, recommendations are provided on how to practice the transdisciplinary mindset required, emphasising a broader approach to sustainable production of high-quality food in the future, whereby all those who are involved are made partners in knowledge generation processes through transdisciplinary cooperation.

    @Article{langensiepen2020104125,
    title = "Linking integrative plant physiology with agronomy to sustain future plant production",
    journal = "Environmental and Experimental Botany",
    volume = "178",
    pages = "104125",
    year = "2020",
    issn = "0098-8472",
    doi = "https://doi.org/10.1016/j.envexpbot.2020.104125",
    url = "http://www.sciencedirect.com/science/article/pii/S0098847220301519",
    author = "Matthias Langensiepen and Marcel A.K. Jansen and Astrid Wingler and Barbara Demmig-Adams and William W. Adams and Ian C. Dodd and Vasileios Fotopoulos and Rod Snowdon and Erola Fenollosa and Mario C. {De Tullio} and Gerhard Buck-Sorlin and Sergi Munné-Bosch",
    keywords = "Food production, Molecular plant biology, Plant physiology, Agronomy, Sustainability, Transdisciplinarity",
    abstract = "Sustainable production of high-quality food is one of today’s major challenges of agriculture. To achieve this goal, a better understanding of plant physiological processes and a more integrated approach with respect to current agronomical practices are needed. In this review, various examples of cooperation between integrative plant physiology and agronomy are discussed, and this demonstrates the complexity of these interrelations. The examples are meant to stimulate discussions on how both research areas can deliver solutions to avoid looming food crises due to population growth and climate change. In the last decades, unprecedented progress has been made in the understanding of how plants grow and develop in a variety of environments and in response to biotic stresses, but appropriate management and interpretation of the resulting complex datasets remains challenging. After providing an historical overview of integrative plant physiology, we discuss possible avenues of integration, involving advances in integrative plant physiology, to sustain plant production in the current post-omics era. Finally, recommendations are provided on how to practice the transdisciplinary mindset required, emphasising a broader approach to sustainable production of high-quality food in the future, whereby all those who are involved are made partners in knowledge generation processes through transdisciplinary cooperation.",
    }

  • J. Bömer, L. Zabawa, P. Sieren, A. Kicherer, L. Klingbeil, U. Rascher, O. Muller, H. Kuhlmann, and R. Roscher, Automatic Differentiation of Damaged and Unharmed Grapes Using RGB Images and Convolutional Neural Networks, 2020.
    [BibTeX]
    @Misc{bömer2929,
    title = {Automatic Differentiation of Damaged and Unharmed Grapes Using RGB Images and Convolutional Neural Networks},
    author = {Bömer, J and Zabawa, L and Sieren, P and Kicherer, A and Klingbeil, L and Rascher, U and Muller, O and Kuhlmann, H and Roscher, R},
    year = {2020},
    booktitle = {Conference on Computer Vision (ECCV)},
    }

  • J. Quenzel, R. A. Rosu, T. Läbe, C. Stachniss, and S. Behnke, "Beyond Photometric Consistency: Gradient-based Dissimilarity for Improving Visual Odometry and Stereo Matching," in 2020 IEEE International Conference on Robotics and Automation (ICRA) , 2020, pp. 272-278. doi:10.1109/ICRA40945.2020.9197483
    [BibTeX]
    @INPROCEEDINGS{9197483,
    author={Quenzel, Jan and Rosu, Radu Alexandru and Läbe, Thomas and Stachniss, Cyrill and Behnke, Sven},
    booktitle={2020 IEEE International Conference on Robotics and Automation (ICRA)},
    title={Beyond Photometric Consistency: Gradient-based Dissimilarity for Improving Visual Odometry and Stereo Matching},
    year={2020},
    pages={272-278},
    doi={10.1109/ICRA40945.2020.9197483},
    }

  • A. Barreto, S. Paulus, M. Varrelmann, and A. Mahlein, "Hyperspectral imaging of symptoms induced by Rhizoctonia solani in sugar beet: comparison of input data and different machine learning algorithms," Journal of Plant Diseases and Protection, vol. 127, iss. 4, 2020. doi:10.1007/s41348-020-00344-8
    [BibTeX] [PDF]

    The fungal pathogen Rhizoctonia solani is one of the most important soil-borne diseases in sugar beet production worldwide. Root and crown rot caused by this fungus are traditionally recognized later in the cropping season by rating the above-ground symptoms like wilting and chlorosis on foliage, and dark brown lesions at the base of petioles. The present study was designed to evaluate noninvasive sensors and machine learning for measuring disease incidence and early detection. Eight-weeks-old plants were inoculated with the pathogen in two different concentrations and under controlled conditions. Hyperspectral images in the visible and near-infrared range from leaf were obtained in time-series. One hundred thirty and fifteen spectral features were selected in two levels by using the recursive feature elimination method (RFE) and a clustering approach. Subsequently, five types of machine-learning methods were employed to train four types of spectral data containing reflectance values, vegetation indices, selected variables of the RFE process and selected variables of an RFE-clustering process. The best classifier was obtained from a partial least squares modeling process and required a number of 15 spectral features, which include first and second derivatives of the wavelength spectrum as well as the Ctr3, EVI and PSSRa vegetation index. This investigation proves that under controlled conditions early detection of indirect symptoms caused by Rhizoctonia root rot in sugar-beet plants is possible. Scoring of disease incidence of Rhizoctonia root rot at 10 dai was 3 to 5 times higher with a machine-learning classifier in comparison with the human visual rating.

    @Article{barreto2020,
    author = {Barreto, Abel and Paulus, Stefan and Varrelmann, Mark and Mahlein, Anne-Katrin},
    title = {Hyperspectral imaging of symptoms induced by Rhizoctonia solani in sugar beet: comparison of input data and different machine learning algorithms},
    journal = {Journal of Plant Diseases and Protection},
    volume = {127},
    year = {2020},
    number = {4},
    url = {https://doi.org/10.1007/s41348-020-00344-8},
    issn = {1861-3837},
    abstract = {The fungal pathogen Rhizoctonia solani is one of the most important soil-borne diseases in sugar beet production worldwide. Root and crown rot caused by this fungus are traditionally recognized later in the cropping season by rating the above-ground symptoms like wilting and chlorosis on foliage, and dark brown lesions at the base of petioles. The present study was designed to evaluate noninvasive sensors and machine learning for measuring disease incidence and early detection. Eight-weeks-old plants were inoculated with the pathogen in two different concentrations and under controlled conditions. Hyperspectral images in the visible and near-infrared range from leaf were obtained in time-series. One hundred thirty and fifteen spectral features were selected in two levels by using the recursive feature elimination method (RFE) and a clustering approach. Subsequently, five types of machine-learning methods were employed to train four types of spectral data containing reflectance values, vegetation indices, selected variables of the RFE process and selected variables of an RFE-clustering process. The best classifier was obtained from a partial least squares modeling process and required a number of 15 spectral features, which include first and second derivatives of the wavelength spectrum as well as the Ctr3, EVI and PSSRa vegetation index. This investigation proves that under controlled conditions early detection of indirect symptoms caused by Rhizoctonia root rot in sugar-beet plants is possible. Scoring of disease incidence of Rhizoctonia root rot at 10 dai was 3 to 5 times higher with a machine-learning classifier in comparison with the human visual rating.},
    doi = {10.1007/s41348-020-00344-8},
    }

  • F. Savian, M. Martini, P. Ermacora, S. Paulus, and A. Mahlein, "Prediction of the Kiwifruit Decline Syndrome in Diseased Orchards by Remote Sensing," Remote Sensing, vol. 12, iss. 14, 2020. doi:10.3390/rs12142194
    [BibTeX] [PDF]

    Eight years after the first record in Italy, Kiwifruit Decline (KD), a destructive disease causing root rot, has already affected more than 25% of the area under kiwifruit cultivation in Italy. Diseased plants are characterised by severe decay of the fine roots and sudden wilting of the canopy, which is only visible after the season’s first period of heat (July–August). The swiftness of symptom appearance prevents correct timing and positioning for sampling of the disease, and is therefore a barrier to aetiological studies. The aim of this study is to test the feasibility of thermal and multispectral imaging for the detection of KD using an unsupervised classifier. Thus, RGB, multispectral and thermal data from a kiwifruit orchard, with healthy and diseased plants, were acquired simultaneously during two consecutive growing seasons (2017–2018) using an Unmanned Aerial Vehicle (UAV) platform. Data reduction was applied to the clipped areas of the multispectral and thermal data from the 2017 survey. Reduced data were then classified with two unsupervised algorithms, a K-means and a hierarchical method. The plant vigour (canopy size and presence/absence of wilted leaves) and the health shifts exhibited by asymptomatic plants between 2017 and 2018 were evaluated from RGB data via expert assessment and used as the ground truth for cluster interpretation. Multispectral data showed a high correlation with plant vigour, while temperature data demonstrated a good potential use in predicting health shifts, especially in highly vigorous plants that were asymptomatic in 2017 and became symptomatic in 2018. The accuracy of plant vigour assessment was above 73% when using multispectral data, while clustering of the temperature data allowed the prediction of disease outbreak one year in advance, with an accuracy of 71%. Based on our results, the unsupervised clustering of remote sensing data could be a reliable tool for the identification of sampling areas, and can greatly improve aetiological studies of this new disease in kiwifruit.

    @Article{rs12142194,
    author = {Savian, Francesco and Martini, Marta and Ermacora, Paolo and Paulus, Stefan and Mahlein, Anne-Katrin},
    title = {Prediction of the Kiwifruit Decline Syndrome in Diseased Orchards by Remote Sensing},
    journal = {Remote Sensing},
    volume = {12},
    year = {2020},
    number = {14},
    article-number= {2194},
    url = {https://www.mdpi.com/2072-4292/12/14/2194},
    issn = {2072-4292},
    abstract = {Eight years after the first record in Italy, Kiwifruit Decline (KD), a destructive disease causing root rot, has already affected more than 25% of the area under kiwifruit cultivation in Italy. Diseased plants are characterised by severe decay of the fine roots and sudden wilting of the canopy, which is only visible after the season’s first period of heat (July–August). The swiftness of symptom appearance prevents correct timing and positioning for sampling of the disease, and is therefore a barrier to aetiological studies. The aim of this study is to test the feasibility of thermal and multispectral imaging for the detection of KD using an unsupervised classifier. Thus, RGB, multispectral and thermal data from a kiwifruit orchard, with healthy and diseased plants, were acquired simultaneously during two consecutive growing seasons (2017–2018) using an Unmanned Aerial Vehicle (UAV) platform. Data reduction was applied to the clipped areas of the multispectral and thermal data from the 2017 survey. Reduced data were then classified with two unsupervised algorithms, a K-means and a hierarchical method. The plant vigour (canopy size and presence/absence of wilted leaves) and the health shifts exhibited by asymptomatic plants between 2017 and 2018 were evaluated from RGB data via expert assessment and used as the ground truth for cluster interpretation. Multispectral data showed a high correlation with plant vigour, while temperature data demonstrated a good potential use in predicting health shifts, especially in highly vigorous plants that were asymptomatic in 2017 and became symptomatic in 2018. The accuracy of plant vigour assessment was above 73% when using multispectral data, while clustering of the temperature data allowed the prediction of disease outbreak one year in advance, with an accuracy of 71%. Based on our results, the unsupervised clustering of remote sensing data could be a reliable tool for the identification of sampling areas, and can greatly improve aetiological studies of this new disease in kiwifruit.},
    doi = {10.3390/rs12142194},
    }

  • F. Jonard, D. S. Cannière, N. Brüggemann, P. Gentine, S. D. J. Gianotti, G. Lobet, D. G. Miralles, C. Montzka, B. R. Pagán, U. Rascher, and H. Vereecken, "Value of sun-induced chlorophyll fluorescence for quantifying hydrological states and fluxes: Current status and challenges," Agricultural and Forest Meteorology, vol. 291, p. 108088, 2020. doi:https://doi.org/10.1016/j.agrformet.2020.108088
    [BibTeX] [PDF]

    Predictions of hydrological states and fluxes, especially transpiration, are poorly constrained in hydrological models due to large uncertainties in parameterization and process description. Novel technologies like remote sensing of sun-induced chlorophyll fluorescence (SIF)—which provides information from the photosynthetic apparatus—may help in constraining water cycle components. This paper discusses the nature of the plant physiological basis of the fluorescence signal and analyses the current literature linking hydrological states and fluxes to SIF. Given the connection between photosynthesis and transpiration, through the water use efficiency, SIF may serve as a pertinent constraint for hydrological models. The FLuorescence EXplorer (FLEX) satellite, planned to be launched in 2023, is expected to provide spatially high-resolution measurements of red and far-red SIF complementing the products from existing satellite missions and the high-temporal resolution products from upcoming geostationary missions. This new data stream may allow us to better constrain plant transpiration, assess the impacts of water stress on plants, and infer processes occurring in the root zone through the soil-plant water column. To make optimal use of this data, progress needs to be made in 1) our process representation of spatially aggregated fluorescence signals from spaceborne SIF instruments, 2) integration of fluorescence processes in hydrological models—particularly when paired with other satellite data, 3) quantifying the impact of soil moisture on SIF across scales, and 4) assessment of the accuracy of SIF measurements—especially from space.

    @Article{jonard2020108088,
    title = "Value of sun-induced chlorophyll fluorescence for quantifying hydrological states and fluxes: Current status and challenges",
    journal = "Agricultural and Forest Meteorology",
    volume = "291",
    pages = "108088",
    year = "2020",
    issn = "0168-1923",
    doi = "https://doi.org/10.1016/j.agrformet.2020.108088",
    url = "http://www.sciencedirect.com/science/article/pii/S0168192320301908",
    author = "F. Jonard and S. De Cannière and N. Brüggemann and P. Gentine and D.J. Short Gianotti and G. Lobet and D.G. Miralles and C. Montzka and B.R. Pagán and U. Rascher and H. Vereecken",
    keywords = "Solar-induced chlorophyll fluorescence, Soil water availability, Drought stress, Transpiration, Hydrological processes, Radiative transfer model",
    abstract = "Predictions of hydrological states and fluxes, especially transpiration, are poorly constrained in hydrological models due to large uncertainties in parameterization and process description. Novel technologies like remote sensing of sun-induced chlorophyll fluorescence (SIF)—which provides information from the photosynthetic apparatus—may help in constraining water cycle components. This paper discusses the nature of the plant physiological basis of the fluorescence signal and analyses the current literature linking hydrological states and fluxes to SIF. Given the connection between photosynthesis and transpiration, through the water use efficiency, SIF may serve as a pertinent constraint for hydrological models. The FLuorescence EXplorer (FLEX) satellite, planned to be launched in 2023, is expected to provide spatially high-resolution measurements of red and far-red SIF complementing the products from existing satellite missions and the high-temporal resolution products from upcoming geostationary missions. This new data stream may allow us to better constrain plant transpiration, assess the impacts of water stress on plants, and infer processes occurring in the root zone through the soil-plant water column. To make optimal use of this data, progress needs to be made in 1) our process representation of spatially aggregated fluorescence signals from spaceborne SIF instruments, 2) integration of fluorescence processes in hydrological models—particularly when paired with other satellite data, 3) quantifying the impact of soil moisture on SIF across scales, and 4) assessment of the accuracy of SIF measurements—especially from space.",
    }

  • P. Gaugler, V. Gaugler, M. Kamleitner, and G. Schaaf, "Extraction and Quantification of Soluble, Radiolabeled Inositol Polyphosphates from Different Plant Species using SAX-HPLC," Journal of Visualized Experiments, iss. 160, 2020. doi:10.3791/61495
    [BibTeX] [PDF]
    @Article{gaugler_gaugler_kamleitner_schaaf_2020,
    title = {Extraction and Quantification of Soluble, Radiolabeled Inositol Polyphosphates from Different Plant Species using SAX-HPLC},
    url = {https://www.jove.com/video/61495/extraction-quantification-soluble-radiolabeled-inositol},
    doi = {10.3791/61495},
    number = {160},
    journal = {Journal of Visualized Experiments},
    author = {Philipp Gaugler and Verena Gaugler and Marília Kamleitner and Gabriel Schaaf},
    year = {2020},
    month = {Jun},
    }

  • P. Schramowski, W. Stammer, S. Teso, A. Brugger, X. Shao, H. Luigs, A. Mahlein, and K. Kersting, Right for the Wrong Scientific Reasons: Revising Deep Networks by Interacting with their Explanations, 2020.
    [BibTeX]
    @Misc{schramowski2020right,
    title = {Right for the Wrong Scientific Reasons: Revising Deep Networks by Interacting with their Explanations},
    author = {Patrick Schramowski and Wolfgang Stammer and Stefano Teso and Anna Brugger and Xiaoting Shao and Hans-Georg Luigs and Anne-Katrin Mahlein and Kristian Kersting},
    year = {2020},
    eprint = {2001.05371},
    archiveprefix = {arXiv},
    primaryclass = {cs.LG},
    }

  • C. H. Bock, J. G. A. Barbedo, E. D. M. Ponte, D. Bohnenkamp, and A. Mahlein, "From visual estimates to fully automated sensor-based measurements of plant disease severity: status and challenges for improving accuracy," Phytopathology Research 2, 2020. doi:https://doi.org/10.1186/s42483-020-00049-8
    [BibTeX] [PDF]

    The severity of plant diseases, traditionally the proportion of the plant tissue exhibiting symptoms, is a key quantitative variable to know for many diseases and is prone to error. Good quality disease severity data should be accurate (close to the true value). Earliest quantification of disease severity was by visual estimates. Sensor-based image analysis including visible spectrum and hyperspectral and multispectral sensors are established technologies that promise to substitute, or complement visual ratings. Indeed, these technologies have measured disease severity accurately under controlled conditions but are yet to demonstrate their full potential for accurate measurement under field conditions. Sensor technology is advancing rapidly, and artificial intelligence may help overcome issues for automating severity measurement under hyper-variable field conditions. The adoption of appropriate scales, training, instruction and aids (standard area diagrams) has contributed to improved accuracy of visual estimates. The apogee of accuracy for visual estimation is likely being approached, and any remaining increases in accuracy are likely to be small. Due to automation and rapidity, sensor-based measurement offers potential advantages compared with visual estimates, but the latter will remain important for years to come. Mobile, automated sensor-based systems will become increasingly common in controlled conditions and, eventually, in the field for measuring plant disease severity for the purpose of research and decision making.

    @Article{bock2020,
    title = "From visual estimates to fully automated sensor-based measurements of plant disease severity: status and challenges for improving accuracy",
    journal = "Phytopathology Research 2",
    year = "2020",
    issn = "2524-4167",
    doi = "https://doi.org/10.1186/s42483-020-00049-8",
    url = "https://phytopatholres.biomedcentral.com/articles/10.1186/s42483-020-00049-8",
    author = "Clive H. Bock and Jayme G. A. Barbedo and Emerson M. Del Ponte and David Bohnenkamp and Anne-Katrin Mahlein",
    abstract = "The severity of plant diseases, traditionally the proportion of the plant tissue exhibiting symptoms, is a key quantitative variable to know for many diseases and is prone to error. Good quality disease severity data should be accurate (close to the true value). Earliest quantification of disease severity was by visual estimates. Sensor-based image analysis including visible spectrum and hyperspectral and multispectral sensors are established technologies that promise to substitute, or complement visual ratings. Indeed, these technologies have measured disease severity accurately under controlled conditions but are yet to demonstrate their full potential for accurate measurement under field conditions. Sensor technology is advancing rapidly, and artificial intelligence may help overcome issues for automating severity measurement under hyper-variable field conditions. The adoption of appropriate scales, training, instruction and aids (standard area diagrams) has contributed to improved accuracy of visual estimates. The apogee of accuracy for visual estimation is likely being approached, and any remaining increases in accuracy are likely to be small. Due to automation and rapidity, sensor-based measurement offers potential advantages compared with visual estimates, but the latter will remain important for years to come. Mobile, automated sensor-based systems will become increasingly common in controlled conditions and, eventually, in the field for measuring plant disease severity for the purpose of research and decision making.",
    }

  • B. Müller, F. Hoffmann, T. Heckelei, C. Müller, T. W. Hertel, G. J. Polhill, M. [. Wijk], T. Achterbosch, P. Alexander, C. Brown, D. Kreuer, F. Ewert, J. Ge, J. D. A. Millington, R. Seppelt, P. H. Verburg, and H. Webber, "Modelling food security: Bridging the gap between the micro and the macro scale," Global Environmental Change, vol. 63, p. 102085, 2020. doi:https://doi.org/10.1016/j.gloenvcha.2020.102085
    [BibTeX] [PDF]

    Achieving food and nutrition security for all in a changing and globalized world remains a critical challenge of utmost importance. The development of solutions benefits from insights derived from modelling and simulating the complex interactions of the agri-food system, which range from global to household scales and transcend disciplinary boundaries. A wide range of models based on various methodologies (from food trade equilibrium to agent-based) seek to integrate direct and indirect drivers of change in land use, environment and socio-economic conditions at different scales. However, modelling such interaction poses fundamental challenges, especially for representing non-linear dynamics and adaptive behaviours. We identify key pieces of the fragmented landscape of food security modelling, and organize achievements and gaps into different contextual domains of food security (production, trade, and consumption) at different spatial scales. Building on in-depth reflection on three core issues of food security – volatility, technology, and transformation – we identify methodological challenges and promising strategies for advancement. We emphasize particular requirements related to the multifaceted and multiscale nature of food security. They include the explicit representation of transient dynamics to allow for path dependency and irreversible consequences, and of household heterogeneity to incorporate inequality issues. To illustrate ways forward we provide good practice examples using meta-modelling techniques, non-equilibrium approaches and behavioural-based modelling endeavours. We argue that further integration of different model types is required to better account for both multi-level agency and cross-scale feedbacks within the food system.

    @Article{muller2020102085,
    title = "Modelling food security: Bridging the gap between the micro and the macro scale",
    journal = "Global Environmental Change",
    volume = "63",
    pages = "102085",
    year = "2020",
    issn = "0959-3780",
    doi = "https://doi.org/10.1016/j.gloenvcha.2020.102085",
    url = "http://www.sciencedirect.com/science/article/pii/S0959378019307277",
    author = "Birgit Müller and Falk Hoffmann and Thomas Heckelei and Christoph Müller and Thomas W. Hertel and J. Gareth Polhill and Mark [van Wijk] and Thom Achterbosch and Peter Alexander and Calum Brown and David Kreuer and Frank Ewert and Jiaqi Ge and James D.A. Millington and Ralf Seppelt and Peter H. Verburg and Heidi Webber",
    keywords = "Food security, Multi-scale interactions, Model integration, Agent-based models, Economic equilibrium models, Crop models, Social-ecological feedbacks, Land use",
    abstract = "Achieving food and nutrition security for all in a changing and globalized world remains a critical challenge of utmost importance. The development of solutions benefits from insights derived from modelling and simulating the complex interactions of the agri-food system, which range from global to household scales and transcend disciplinary boundaries. A wide range of models based on various methodologies (from food trade equilibrium to agent-based) seek to integrate direct and indirect drivers of change in land use, environment and socio-economic conditions at different scales. However, modelling such interaction poses fundamental challenges, especially for representing non-linear dynamics and adaptive behaviours. We identify key pieces of the fragmented landscape of food security modelling, and organize achievements and gaps into different contextual domains of food security (production, trade, and consumption) at different spatial scales. Building on in-depth reflection on three core issues of food security – volatility, technology, and transformation – we identify methodological challenges and promising strategies for advancement. We emphasize particular requirements related to the multifaceted and multiscale nature of food security. They include the explicit representation of transient dynamics to allow for path dependency and irreversible consequences, and of household heterogeneity to incorporate inequality issues. To illustrate ways forward we provide good practice examples using meta-modelling techniques, non-equilibrium approaches and behavioural-based modelling endeavours. We argue that further integration of different model types is required to better account for both multi-level agency and cross-scale feedbacks within the food system.",
    }

  • L. Zabawa, A. Kicherer, L. Klingbeil, R. Töpfer, H. Kuhlmann, and R. Roscher, "Counting of grapevine berries in images via semantic segmentation using convolutional neural networks," ISPRS Journal of Photogrammetry and Remote Sensing, vol. 164, pp. 73-83, 2020. doi:https://doi.org/10.1016/j.isprsjprs.2020.04.002
    [BibTeX] [PDF]

    The extraction of phenotypic traits is often very time and labour intensive. Especially the investigation in viticulture is restricted to an on-site analysis due to the perennial nature of grapevine. Traditionally skilled experts examine small samples and extrapolate the results to a whole plot. Thereby different grapevine varieties and training systems, e.g. vertical shoot positioning (VSP) and semi minimal pruned hedges (SMPH) pose different challenges. In this paper we present an objective framework based on automatic image analysis which works on two different training systems. The images are collected semi automatic by a camera system which is installed in a modified grape harvester. The system produces overlapping images from the sides of the plants. Our framework uses a convolutional neural network to detect single berries in images by performing a semantic segmentation. Each berry is then counted with a connected component algorithm. We compare our results with the Mask-RCNN, a state-of-the-art network for instance segmentation and with a regression approach for counting. The experiments presented in this paper show that we are able to detect green berries in images despite of different training systems. We achieve an accuracy for the berry detection of 94.0% in the VSP and 85.6% in the SMPH.

    @Article{zabawa202073,
    title = "Counting of grapevine berries in images via semantic segmentation using convolutional neural networks",
    journal = "ISPRS Journal of Photogrammetry and Remote Sensing",
    volume = "164",
    pages = "73 - 83",
    year = "2020",
    issn = "0924-2716",
    doi = "https://doi.org/10.1016/j.isprsjprs.2020.04.002",
    url = "http://www.sciencedirect.com/science/article/pii/S0924271620300939",
    author = "Laura Zabawa and Anna Kicherer and Lasse Klingbeil and Reinhard Töpfer and Heiner Kuhlmann and Ribana Roscher",
    keywords = "Deep learning, Semantic segmentation, Geoinformation, High-throughput analysis, Plant phenotyping, Vitis",
    abstract = "The extraction of phenotypic traits is often very time and labour intensive. Especially the investigation in viticulture is restricted to an on-site analysis due to the perennial nature of grapevine. Traditionally skilled experts examine small samples and extrapolate the results to a whole plot. Thereby different grapevine varieties and training systems, e.g. vertical shoot positioning (VSP) and semi minimal pruned hedges (SMPH) pose different challenges. In this paper we present an objective framework based on automatic image analysis which works on two different training systems. The images are collected semi automatic by a camera system which is installed in a modified grape harvester. The system produces overlapping images from the sides of the plants. Our framework uses a convolutional neural network to detect single berries in images by performing a semantic segmentation. Each berry is then counted with a connected component algorithm. We compare our results with the Mask-RCNN, a state-of-the-art network for instance segmentation and with a regression approach for counting. The experiments presented in this paper show that we are able to detect green berries in images despite of different training systems. We achieve an accuracy for the berry detection of 94.0% in the VSP and 85.6% in the SMPH.",
    }

  • A. Bonerath, J. -H. Haunert, and B. Niedermann, "Tight Rectilinear Hulls of Simple Polygons," in Proceedings of the 36th European Workshop on Computational Geometry (EuroCG'20) , 2020.
    [BibTeX] [PDF]

    A polygon is called C-oriented if the orientations of all its edges stem from a pre-defined set C. The schematization of a polygon is then a C-oriented polygon that describes and simplifies the shape of the input polygon with respect to given hard and soft constraints. We study the case that the C-oriented polygon needs to contain the input polygon such that it is tight in the sense that it cannot be shrunk without starting to overlap with the input polygon; we call this a tight C-hull of the polygon. We restrict the tight C-hull to be a simple polygon. We aim at a tight C-hull that optimally balances the number of bends, the total edge length and the enclosed area. For the case that both polygons are rectilinear, we present a dynamic-programming approach that yields such a tight hull in polynomial time. For arbitrary simple polygons we can use the same approach to obtain approximate tight rectilinear hulls.

    @InProceedings{bhn-trhsp-20,
    abstract = {A polygon is called C-oriented if the orientations of all its edges stem from a pre-defined set C. The schematization of a polygon is then a C-oriented polygon that describes and simplifies the shape of the input polygon with respect to given hard and soft constraints. We study the case that the C-oriented polygon needs to contain the input polygon such that it is tight in the sense that it cannot be shrunk without starting to overlap with the input polygon; we call this a tight C-hull of the polygon. We restrict the tight C-hull to be a simple polygon. We aim at a tight C-hull that optimally balances the number of bends, the total edge length and the enclosed area. For the case that both polygons are rectilinear, we present a dynamic-programming approach that yields such a tight hull in polynomial time. For arbitrary simple polygons we can use the same approach to obtain approximate tight rectilinear hulls.},
    author = {Bonerath, A. and Haunert, J.-H. and Niedermann, B.},
    booktitle = {Proceedings of the 36th European Workshop on Computational Geometry (EuroCG'20)},
    note = {Preprint.},
    title = {{Tight Rectilinear Hulls of Simple Polygons}},
    url = {http://www1.pub.informatik.uni-wuerzburg.de/eurocg2020/data/uploads/eurocg20_proceedings.pdf},
    year = {2020},
    }

  • E. Heinz, C. Holst, H. Kuhlmann, and L. Klingbeil, "Design and Evaluation of a Permanently Installed Plane-Based Calibration Field for Mobile Laser Scanning Systems," Remote Sensing, vol. 12, iss. 3, 2020. doi:10.3390/rs12030555
    [BibTeX] [PDF]

    Mobile laser scanning has become an established measuring technique that is used for many applications in the fields of mapping, inventory, and monitoring. Due to the increasing operationality of such systems, quality control w.r.t. calibration and evaluation of the systems becomes more and more important and is subject to on-going research. This paper contributes to this topic by using tools from geodetic configuration analysis in order to design and evaluate a plane-based calibration field for determining the lever arm and boresight angles of a 2D laser scanner w.r.t. a GNSS/IMU unit (Global Navigation Satellite System, Inertial Measurement Unit). In this regard, the impact of random, systematic, and gross observation errors on the calibration is analyzed leading to a plane setup that provides accurate and controlled calibration parameters. The designed plane setup is realized in the form of a permanently installed calibration field. The applicability of the calibration field is tested with a real mobile laser scanning system by frequently repeating the calibration. Empirical standard deviations of <1 ... 1.5 mm for the lever arm and <0.005 ∘ for the boresight angles are obtained, which was priorly defined to be the goal of the calibration. In order to independently evaluate the mobile laser scanning system after calibration, an evaluation environment is realized consisting of a network of control points as well as TLS (Terrestrial Laser Scanning) reference point clouds. Based on the control points, both the horizontal and vertical accuracy of the system is found to be < 10 mm (root mean square error). This is confirmed by comparisons to the TLS reference point clouds indicating a well calibrated system. Both the calibration field and the evaluation environment are permanently installed and can be used for arbitrary mobile laser scanning systems.

    @Article{rs12030555,
    author = {Heinz, Erik and Holst, Christoph and Kuhlmann, Heiner and Klingbeil, Lasse},
    title = {Design and Evaluation of a Permanently Installed Plane-Based Calibration Field for Mobile Laser Scanning Systems},
    journal = {Remote Sensing},
    volume = {12},
    year = {2020},
    number = {3},
    article-number= {555},
    url = {https://www.mdpi.com/2072-4292/12/3/555},
    issn = {2072-4292},
    abstract = {Mobile laser scanning has become an established measuring technique that is used for many applications in the fields of mapping, inventory, and monitoring. Due to the increasing operationality of such systems, quality control w.r.t. calibration and evaluation of the systems becomes more and more important and is subject to on-going research. This paper contributes to this topic by using tools from geodetic configuration analysis in order to design and evaluate a plane-based calibration field for determining the lever arm and boresight angles of a 2D laser scanner w.r.t. a GNSS/IMU unit (Global Navigation Satellite System, Inertial Measurement Unit). In this regard, the impact of random, systematic, and gross observation errors on the calibration is analyzed leading to a plane setup that provides accurate and controlled calibration parameters. The designed plane setup is realized in the form of a permanently installed calibration field. The applicability of the calibration field is tested with a real mobile laser scanning system by frequently repeating the calibration. Empirical standard deviations of <1 ... 1.5 mm for the lever arm and <0.005 ∘ for the boresight angles are obtained, which was priorly defined to be the goal of the calibration. In order to independently evaluate the mobile laser scanning system after calibration, an evaluation environment is realized consisting of a network of control points as well as TLS (Terrestrial Laser Scanning) reference point clouds. Based on the control points, both the horizontal and vertical accuracy of the system is found to be < 10 mm (root mean square error). This is confirmed by comparisons to the TLS reference point clouds indicating a well calibrated system. Both the calibration field and the evaluation environment are permanently installed and can be used for arbitrary mobile laser scanning systems.},
    doi = {10.3390/rs12030555},
    }

2019

  • F. Seiffarth, T. Horvath, and S. Wrobel, "Maximal Closed Set and Half-Space Separations in Finite Closure Systems," in ECML/PKDD 2019 and has appeared in the Lecture Notes in Computer Science, Machine Learning and Knowledge Discovery in Databases - European Conference , 2019.
    [BibTeX]
    @InProceedings{seiffarth2019maximal,
    title = {Maximal Closed Set and Half-Space Separations in Finite Closure Systems},
    author = {Florian Seiffarth and Tamas Horvath and Stefan Wrobel},
    year = {2019},
    booktitle = {ECML/PKDD 2019 and has appeared in the Lecture Notes in Computer Science, Machine Learning and Knowledge Discovery in Databases - European Conference},
    }

  • J. Behley, M. Garbade, A. Milioto, J. Quenzel, S. Behnke, C. Stachniss, and J. Gall, "SemanticKITTI: A Dataset for Semantic Scene Understanding of LiDAR Sequences." 2019.
    [BibTeX] [PDF] [Video]
    @InProceedings{behley2019iccv,
    author = {J. Behley and M. Garbade and A. Milioto and J. Quenzel and S. Behnke and C. Stachniss and J. Gall},
    title = {{SemanticKITTI: A Dataset for Semantic Scene Understanding of LiDAR Sequences}},
    booktitle = iccv,
    year = {2019},
    url = {https://www.ipb.uni-bonn.de/wp-content/papercite-data/pdf/behley2019iccv.pdf},
    videourl = {http://www.ipb.uni-bonn.de/html/projects/semantic_kitti/videos/teaser.mp4},
    }

  • E. Palazzolo, J. Behley, P. Lottes, P. Giguère, and C. Stachniss, "ReFusion: 3D Reconstruction in Dynamic Environments for RGB-D Cameras Exploiting Residuals," in Proceedings of the IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS) , 2019.
    [BibTeX] [PDF] [Code] [Video]
    @InProceedings{palazzolo2019iros,
    author = {E. Palazzolo and J. Behley and P. Lottes and P. Gigu\`ere and C. Stachniss},
    title = {{ReFusion: 3D Reconstruction in Dynamic Environments for RGB-D Cameras Exploiting Residuals}},
    booktitle = {Proceedings of the IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS)},
    year = {2019},
    url = {http://www.ipb.uni-bonn.de/pdfs/palazzolo2019iros.pdf},
    codeurl = {https://github.com/PRBonn/refusion},
    videourl = {https://youtu.be/1P9ZfIS5-p4},
    }

  • X. Chen, A. Milioto, E. Palazzolo, P. Giguère, J. Behley, and C. Stachniss, "SuMa++: Efficient LiDAR-based Semantic SLAM," in Proceedings of the IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS) , 2019.
    [BibTeX] [PDF] [Code] [Video]
    @InProceedings{chen2019iros,
    author = {X. Chen and A. Milioto and E. Palazzolo and P. Giguère and J. Behley and C. Stachniss},
    title = {{SuMa++: Efficient LiDAR-based Semantic SLAM}},
    booktitle = {Proceedings of the IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS)},
    year = 2019,
    codeurl = {https://github.com/PRBonn/semantic_suma/},
    videourl = {https://youtu.be/uo3ZuLuFAzk},
    url = {https://www.ipb.uni-bonn.de/wp-content/papercite-data/pdf/chen2019iros.pdf},
    }

  • A. Milioto, I. Vizzo, J. Behley, and C. Stachniss, "RangeNet++: Fast and Accurate LiDAR Semantic Segmentation," in Proceedings of the IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS) , 2019.
    [BibTeX] [PDF] [Code] [Video]
    @InProceedings{milioto2019iros,
    author = {A. Milioto and I. Vizzo and J. Behley and C. Stachniss},
    title = {{RangeNet++: Fast and Accurate LiDAR Semantic Segmentation}},
    booktitle = {Proceedings of the IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS)},
    year = 2019,
    codeurl = {https://github.com/PRBonn/lidar-bonnetal},
    videourl = {https://youtu.be/wuokg7MFZyU},
    url = {https://www.ipb.uni-bonn.de/wp-content/papercite-data/pdf/milioto2019iros.pdf},
    }

  • F. Yan, O. Vysotska, and C. Stachniss, " Global Localization on OpenStreetMap Using 4-bit Semantic Descriptors," in Proceedings of the European Conference on Mobile Robots (ECMR) , 2019.
    [BibTeX] [PDF]
    @InProceedings{yan2019ecmr,
    author = {F. Yan and O. Vysotska and C. Stachniss},
    title = {{ Global Localization on OpenStreetMap Using 4-bit Semantic Descriptors}},
    booktitle = {Proceedings of the European Conference on Mobile Robots (ECMR)},
    year = {2019},
    url = {https://www.ipb.uni-bonn.de/wp-content/papercite-data/pdf/yan2019ecmr.pdf},
    }

  • R. Schirmer, P. Bieber, and C. Stachniss, "Coverage Path Planning in Belief Space ," in Proc. of the IEEE Intl. Conf. on Robotics & Automation (ICRA) , 2019.
    [BibTeX] [PDF]
    @InProceedings{schirmer2019icra,
    author = {R. Schirmer and P. Bieber and C. Stachniss},
    title = {{Coverage Path Planning in Belief Space }},
    booktitle = {Proc. of the IEEE Intl. Conf. on Robotics \& Automation (ICRA)},
    year = 2019,
    url = {https://www.ipb.uni-bonn.de/wp-content/papercite-data/pdf/schirmer2019icra.pdf},
    }

  • K. Huang and C. Stachniss, "Accurate Direct Visual-Laser Odometry with Explicit Occlusion Handling and Plane Detection," in Proc. of the IEEE Intl. Conf. on Robotics & Automation (ICRA) , 2019.
    [BibTeX]
    @InProceedings{huang2019icra,
    author = {K. Huang and C. Stachniss},
    title = {{Accurate Direct Visual-Laser Odometry with Explicit Occlusion Handling and Plane Detection}},
    booktitle = {Proc. of the IEEE Intl. Conf. on Robotics \& Automation (ICRA)},
    year = 2019,
    }

  • A. Bonerath, B. Niedermann, and J. -H. Haunert, "Retrieving alpha-Shapes and Schematic Polygonal Approximations for Sets of Points within Queried Temporal Ranges," in Proceedings of the 27th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems , New York, NY, USA, 2019, p. 249–258. doi:10.1145/3347146.3359087
    [BibTeX] [PDF]

    The interactive exploration of data requires data structures that can be repeatedly queried to obtain simple visualizations of parts of the data. We consider the scenario that the data is a set of points each associated with a time stamp and that the result of each query is visualized by an α-shape, which generalizes the concept of convex hulls. Instead of computing each shape independently, we suggest and analyze a simple data structure that aggregates the α-shapes of all possible queries. Once the data structure is built, it particularly allows us to query single α-shapes without retrieving the actual (possibly large) point set and thus to rapidly produce small previews of the queried data. We discuss the data structure for the original α-shapes as well as for a schematized version of α-shapes, which further simplifies the visualization. We evaluate the data structure on real-world data. The experiments indicate linear memory consumption with respect to the number of points, which makes the data structure applicable in practice, although the size is quadratic for a theoretic worst case example.

    @InProceedings{bhn-rasspa-19,
    author = {Bonerath, A. and Niedermann, B. and Haunert, J.-H.},
    title = {{Retrieving alpha-Shapes and Schematic Polygonal Approximations for Sets of Points within Queried Temporal Ranges}},
    abstract = {The interactive exploration of data requires data structures that can be repeatedly queried to obtain simple visualizations of parts of the data. We consider the scenario that the data is a set of points each associated with a time stamp and that the result of each query is visualized by an α-shape, which generalizes the concept of convex hulls. Instead of computing each shape independently, we suggest and analyze a simple data structure that aggregates the α-shapes of all possible queries. Once the data structure is built, it particularly allows us to query single α-shapes without retrieving the actual (possibly large) point set and thus to rapidly produce small previews of the queried data. We discuss the data structure for the original α-shapes as well as for a schematized version of α-shapes, which further simplifies the visualization. We evaluate the data structure on real-world data. The experiments indicate linear memory consumption with respect to the number of points, which makes the data structure applicable in practice, although the size is quadratic for a theoretic worst case example.},
    booktitle = {Proceedings of the 27th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems},
    series = {SIGSPATIAL '19},
    year = {2019},
    isbn = {978-1-4503-6909-1},
    location = {Chicago, IL, USA},
    pages = {249--258},
    numpages = {10},
    url = {http://doi.acm.org/10.1145/3347146.3359087},
    doi = {10.1145/3347146.3359087},
    acmid = {3359087},
    publisher = {ACM},
    address = {New York, NY, USA},
    keywords = {alpha-shape, data structure, point set, temporal range queries},
    }

  • D. Laha, N. Parvin, A. Hofer, R. F. H. Giehl, N. Fernandez-Rebollo, N. von Wirén, A. Saiardi, H. J. Jessen, and G. Schaaf, "Arabidopsis ITPK1 and ITPK2 Have an Evolutionarily Conserved Phytic Acid Kinase Activity," ACS Chemical Biology, vol. 14, iss. 10, pp. 2127-2133, 2019. doi:10.1021/acschembio.9b00423
    [BibTeX] [PDF]
    @Article{doi:10.1021/acschembio.9b00423,
    author = {Laha, Debabrata and Parvin, Nargis and Hofer, Alexandre and Giehl, Ricardo F. H. and Fernandez-Rebollo, Nicolas and von Wirén, Nicolaus and Saiardi, Adolfo and Jessen, Henning J. and Schaaf, Gabriel},
    title = {Arabidopsis ITPK1 and ITPK2 Have an Evolutionarily Conserved Phytic Acid Kinase Activity},
    journal = {ACS Chemical Biology},
    volume = {14},
    number = {10},
    pages = {2127-2133},
    year = {2019},
    doi = {10.1021/acschembio.9b00423},
    note = {PMID: 31525024},
    url = {https://doi.org/10.1021/acschembio.9b00423},
    eprint = {https://doi.org/10.1021/acschembio.9b00423},
    }

  • H. Storm, K. Baylis, and T. Heckelei, "Machine learning in agricultural and applied economics," European Review of Agricultural Economics, 2019. doi:10.1093/erae/jbz033
    [BibTeX] [PDF]

    {This review presents machine learning (ML) approaches from an applied economist’s perspective. We first introduce the key ML methods drawing connections to econometric practice. We then identify current limitations of the econometric and simulation model toolbox in applied economics and explore potential solutions afforded by ML. We dive into cases such as inflexible functional forms, unstructured data sources and large numbers of explanatory variables in both prediction and causal analysis, and highlight the challenges of complex simulation models. Finally, we argue that economists have a vital role in addressing the shortcomings of ML when used for quantitative economic analysis.}

    @Article{10.1093/erae/jbz033,
    author = {Storm, Hugo and Baylis, Kathy and Heckelei, Thomas},
    title = "{Machine learning in agricultural and applied economics}",
    journal = {European Review of Agricultural Economics},
    year = {2019},
    month = {08},
    abstract = "{This review presents machine learning (ML) approaches from an applied economist’s perspective. We first introduce the key ML methods drawing connections to econometric practice. We then identify current limitations of the econometric and simulation model toolbox in applied economics and explore potential solutions afforded by ML. We dive into cases such as inflexible functional forms, unstructured data sources and large numbers of explanatory variables in both prediction and causal analysis, and highlight the challenges of complex simulation models. Finally, we argue that economists have a vital role in addressing the shortcomings of ML when used for quantitative economic analysis.}",
    issn = {0165-1587},
    doi = {10.1093/erae/jbz033},
    url = {https://doi.org/10.1093/erae/jbz033},
    note = {jbz033},
    eprint = {http://oup.prod.sis.lan/erae/advance-article-pdf/doi/10.1093/erae/jbz033/29194185/jbz033.pdf},
    }

  • A. Mahlein, M. T. Kuska, S. Thomas, M. Wahabzada, J. Behmann, U. Rascher, and K. Kersting, "Quantitative and qualitative phenotyping of disease resistance of crops by hyperspectral sensors: seamless interlocking of phytopathology, sensors, and machine learning is needed," Current Opinion in Plant Biology, vol. 50, 2019. doi:10.1016/j.pbi.2019.06.007
    [BibTeX] [PDF]
    @Article{mahlein2020,
    author = {Mahlein, Anne-Katrin and Kuska, Matheus Thomas and Thomas, Stefan and Wahabzada, Mirwaes and Behmann, Jan and Rascher, Uwe and Kersting, Kristian},
    title = "{Quantitative and qualitative phenotyping of disease resistance of crops by hyperspectral sensors: seamless interlocking of phytopathology, sensors, and machine learning is needed}",
    journal = {Current Opinion in Plant Biology},
    volume = {50},
    year = {2019},
    month = {08},
    doi = {10.1016/j.pbi.2019.06.007},
    url = {https://doi.org/10.1016/j.pbi.2019.06.007},
    }

  • O. Vysotska and C. Stachniss, "Effective Visual Place Recognition Using Multi-Sequence Maps," IEEE Robotics and Automation Letters (RA-L), 2019.
    [BibTeX] [PDF] [Video]
    @Article{vysotska2019ral,
    author = {O. Vysotska and C. Stachniss},
    title = {{Effective Visual Place Recognition Using Multi-Sequence Maps}},
    journal = {IEEE Robotics and Automation Letters (RA-L)},
    year = 2019,
    url = {http://www.ipb.uni-bonn.de/pdfs/vysotska2019ral.pdf},
    videourl = {https://youtu.be/wFU0JoXTH3c},
    }

  • N. Chebrolu, P. Lottes, T. Laebe, and C. Stachniss, "Robot Localization Based on Aerial Images for Precision Agriculture Tasks in Crop Fields," in Proc. of the IEEE Intl. Conf. on Robotics & Automation (ICRA) , 2019.
    [BibTeX] [PDF] [Video]
    @InProceedings{chebrolu2019icra,
    author = {N. Chebrolu and P. Lottes and T. Laebe and C. Stachniss},
    title = {{Robot Localization Based on Aerial Images for Precision Agriculture Tasks in Crop Fields}},
    booktitle = {Proc. of the IEEE Intl. Conf. on Robotics \& Automation (ICRA)},
    year = 2019,
    url = {http://www.ipb.uni-bonn.de/pdfs/chebrolu2019icra.pdf},
    videourl = {https://youtu.be/TlijLgoRLbc},
    }

  • A. Milioto and C. Stachniss, "Bonnet: An Open-Source Training and Deployment Framework for Semantic Segmentation in Robotics using CNNs," in Proc. of the IEEE Intl. Conf. on Robotics & Automation (ICRA) , 2019.
    [BibTeX] [PDF] [Code] [Video]
    @InProceedings{milioto2019icra,
    author = {A. Milioto and C. Stachniss},
    title = {{Bonnet: An Open-Source Training and Deployment Framework for Semantic Segmentation in Robotics using CNNs}},
    booktitle = {Proc. of the IEEE Intl. Conf. on Robotics \& Automation (ICRA)},
    year = 2019,
    url = {https://arxiv.org/abs/1802.08960},
    codeurl = {https://github.com/Photogrammetry-Robotics-Bonn/bonnet},
    videourl = {https://www.youtube.com/watch?v=tfeFHCq6YJs},
    }

  • A. Milioto, L. Mandtler, and C. Stachniss, "Fast Instance and Semantic Segmentation Exploiting Local Connectivity, Metric Learning, and One-Shot Detection for Robotics," in Proc. of the IEEE Intl. Conf. on Robotics & Automation (ICRA) , 2019.
    [BibTeX]
    @InProceedings{milioto2019icra-fiass,
    author = {A. Milioto and L. Mandtler and C. Stachniss},
    title = {{Fast Instance and Semantic Segmentation Exploiting Local Connectivity, Metric Learning, and One-Shot Detection for Robotics}},
    booktitle = {Proc. of the IEEE Intl. Conf. on Robotics \& Automation (ICRA)},
    year = 2019,
    }

  • L. Nardi and C. Stachniss, "Uncertainty-Aware Path Planning for Navigation on Road Networks Using Augmented MDPs," in Proc. of the IEEE Intl. Conf. on Robotics & Automation (ICRA) , 2019.
    [BibTeX] [PDF] [Video]
    @InProceedings{nardi2019icra-uapp,
    author = {L. Nardi and C. Stachniss},
    title = {{Uncertainty-Aware Path Planning for Navigation on Road Networks Using Augmented MDPs}},
    booktitle = {Proc. of the IEEE Intl. Conf. on Robotics \& Automation (ICRA)},
    year = {2019},
    url = {http://www.ipb.uni-bonn.de/pdfs/nardi2019icra-uapp.pdf},
    videourl = {https://youtu.be/3PMSamgYzi4},
    }

  • L. Nardi and C. Stachniss, "Actively Improving Robot Navigation On Different Terrains Using Gaussian Process Mixture Models," in Proc. of the IEEE Intl. Conf. on Robotics and Automation (ICRA) , 2019.
    [BibTeX] [PDF] [Video]
    @InProceedings{nardi2019icra-airn,
    author = {L. Nardi and C. Stachniss},
    title = {{Actively Improving Robot Navigation On Different Terrains Using Gaussian Process Mixture Models}},
    booktitle = {Proc. of the IEEE Intl. Conf. on Robotics and Automation (ICRA)},
    year = 2019,
    url = {http://www.ipb.uni-bonn.de/pdfs/nardi2019icra-airn.pdf},
    videourl = {https://youtu.be/DlMbP3u1g2Y},
    }