Prof. Ribana Roscher

We work at the interface of plant sciences and data science to develop data-driven models that efficiently, accurately, and comprehensively analyze plant structure and processes.

Research directions and goals include improving process understanding in plant sciences through the use of advanced machine learning methods. In addition, new methods for plant sciences will be developed considering challenges such as dealing with field conditions due to variable and possibly unmeasurable or unknown environmental factors and the difficult and often complex data basis. Another goal is to explore new fields of application and initiate new developments leading to innovations in plant sciences and bioeconomy. A particular focus is on machine learning-based analysis methods that can be used, for example, to generate comprehensive statistics in breeding or to search for new plant ideotypes and materials.