The following speakers are confirmed for CVPPA 2021.
Deep Generative Models for Smart Agriculture
Deep generative models are neural networks that are capable of learning complex data distributions and have therefore achieved tremendous success in recent years. In general, they can be used for a variety of applications, such as anomaly detection, current state estimation, or forecasting. In this talk, several deep generative models will be presented and how they can be used in agriculture. A particular focus will be on the interpretability of the results, i.e., a human-understandable representation of the results with high information content. One prominent example covered in this talk is an image-based plant growth model utilizing generative adversarial networks to generate the future appearance of plants.
Ribana Roscher received the Dipl.-Ing. and Ph.D. degrees in Geodesy from the University of Bonn, Germany, in 2008 and 2012, respectively. She is currently an Assistant Professor of Remote Sensing at the Institute of Geodesy and Geoinformation at the University of Bonn and a Visiting Professor in the Future Lab ‘AI for Earth Observation’ at the Technical University Munich. Her research includes pattern recognition and machine learning specifically for agricultural and environmental science applications.