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

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

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

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

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

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

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...

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

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