Based on aboveground data collected in the field that display crop development, Core Project 2 identifies unknown correlations towards realizing new expressive features for crop science. Here, the focus is on the link between different growth phases and stress influences such as plant disease, nutrient deficiencies, or drought stress on yield development. We identify key features using machine learning techniques that are validated through experimental design approaches. This leads to new insights into the interpretation of sensor data, as well as support for decision making in practical agriculture or plant breeding.
Research Videos
Machine Learning and Knowledge Integration with Large-scale Multi-dimensional Data
PhenoRob PhD Student Maurice Günder talks about his research within Core Project 2: Relevance Detection of Crop Features.
Detection of Nutrient Imbalances
PhenoRob PhD Student Marion Deichmann talks about her research within Core Project 2: Relevance Detection of Crop Features.
Cercospora Leaf Spot Disease Modelling in Sugar Beet by Optical Sensors and Environmental Data
PhenoRob PhD Student Facundo Ispizua talks about his research within Core Project 2: Relevance Detection of Crop Features.
Plant Modelling and AI
PhenoRob PhD Student Thomas Feron talks about his research within Core Project 2: Relevance Detection of Crop Features.
Deep Learning for Non-invasive Diagnosis of Nutrient Deficiencies Using RGB Images
PhenoRob PhD Student Jinhui Yi talks about his research within Core Project 2: Relevance Detection of Crop Features.
Estimation of Occluded Grapevine Berries with Conditional Generative Adversarial Networks
PhenoRob PhD Student Jana Kierdorf talks about her reserach within Core Project 2: Relevance Detection of Crop Features.
A.-K. Mahlein: Making Deep Neural Networks Right for the Right Scientific Reason…
Prof. Dr. Anne-Katrin Mahlein is Principal Investigator at PhenoRob and Director of the Institute of Sugarbeet Research (IfZ) at the University of Göttingen. Making deep neural networks right for the right scientific reasons by interacting with their explanations. Schramowski, P., Stammer, W., Teso, S. et al. Nat Mach Intell 2, 476–486 (2020). https://doi.org/10.1038/s42256-020-0212-3
G. Schaaf: ITPK1 is an InsP6/ADP phosphotransferase that controls phosphate signaling in Arabidopsis
Prof. Dr. Gabriel Schaaf is Principal Investigator at PhenoRob and Professor and head of the Ecophysiology of Plant Nutrition Group, Institute of Crop Science and Resource Conservation (INRES), University of Bonn. ITPK1 is an InsP6/ADP phosphotransferase that controls phosphate signaling in Arabidopsis EstherRiemer, Danye Qiu, Debabrata Laha, Robert K.Harmel, Philipp Gaugler, Verena Gaugler, Michael Frei, Mohammad-Reza Hajirezaei, Nargis Parvin Laha, Lukas Krusenbaum, Robin Schneider, Adolfo Saiardi, Dorothea Fiedler, Henning J. Jessen, Gabriel Schaaf, Ricardo F. H. Giehl Molecular Plant Volume 14, Issue 11, 1 November 2021, Pages 1864-1880 https://doi.org/10.1016/j.molp.2021.07.011
Cercospora leaf spot modeling in sugar beet by Ispizua, Barreto, Günder, Bauckhage & Mahlein
This short paper trailer video is based on the following publication: F. R. Ispizua Yamati, A. Barreto, A. Günder, C. Bauckhage, and A. -K. Mahlein, “Sensing the occurrence and dynamics of Cercospora leaf spot disease using UAV-supported image data and deep learning,” Sugar Industry, vol. 147, iss. 2, pp. 79-86, 2022. Find more info here: https://sugarindustry.info/paper/28345/ https://www.researchgate.net/publication/358243320_Sensing_the_occurrence_and_dynamics_of_Cercospora_leaf_spot_disease_using_UAV-supported_image_data_and_deep_learning
Deep Learning for Non-Invasive Diagnosis of Nutrient Deficiencies in Sugar Beet Using RGB Images
Yi, J.; Krusenbaum, L.; Unger, P.; Hüging, H.; Seidel, S.J.; Schaaf, G.; Gall, J. Deep Learning for Non-Invasive Diagnosis of Nutrient Deficiencies in Sugar Beet Using RGB Images. Sensors 2020, 20, 5893. https://www.mdpi.com/1424-8220/20/20/5893
An Introduction to Temporal Visual Data Analysis
Jürgen Gall, professor and head of the Computer Vision Group, Institute of Computer Science at the University of Bonn gives a PhenoRob Interdisciplinary Lecture [PILS] on the topic of temporal visual data analysis.
Djamei: Many ways to TOPLESS– manipulation of plant auxin signaling by a cluster of fungal effectors
Prof. Dr. Armin Djamei is Professor of Plant Pathology, Institute of Crop Science and Resource Conservation (INRES) at the University of Bonn. Bindics, J., Khan, M., Uhse, S., Kogelmann, B., Baggely, L., Reumann, D., Ingole, K.D., Stirnberg, A., Rybecky, A., Darino, M., Navarrete, F., Doehlemann, G. and Djamei, A. (2022), Many ways to TOPLESS – manipulation of plant auxin signalling by a cluster of fungal effectors. New Phytologist. https://doi.org/10.1111/nph.18315
What Is Plant Breeding?
Annaliese Mason, professor of Plant Breeding, Institute of Crop Science and Resource Conservation (INRES) at the University of Bonn gives a PhenoRob Interdisciplinary Lecture [PILS] on the topic of plant breeding for non-experts.
Genome Editing and Its Impacts in Crop Physiology Studies and Modern Breeding
Gabriel Schaaf, Professor and head of the Ecophysiology of Plant Nutrition Group at the Institute of Crop Science and Resource Conservation (INRES), University of Bonn gives a PhenoRob Interdisciplinary Lecture [PILS] on genome editing and its impacts in crop physiology studies and modern breeding
Plant Pathology – an Overview
Armin Djamei, Professor of Plant Pathology, Institute of Crop Science and Resource Conservation (INRES) at the University of Bonn gives a PhenoRob Interdisciplinary Lecture [PILS] on “Plant Pathology – an Overview”.