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.