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
Detection of Nutrient Imbalances
Cercospora Leaf Spot Disease Modelling in Sugar Beet by Optical Sensors and Environmental Data
Plant Modelling and AI
Deep Learning for Non-invasive Diagnosis of Nutrient Deficiencies Using RGB Images
Estimation of Occluded Grapevine Berries with Conditional Generative Adversarial Networks
A.-K. Mahlein: Making Deep Neural Networks Right for the Right Scientific Reason…
G. Schaaf: ITPK1 is an InsP6/ADP phosphotransferase that controls phosphate signaling in Arabidopsis