The sustainability and reliability of modern food systems are defining problems of our time. Their urgency and the increasing availability of data make agriculture a perfect application for modern AI methods. In my research, I apply supervised learning techniques to model the complex GxExM (genetics, environment, management) interactions that determine the nutritional outputs of agricultural systems as well as their environmental impact. Moreover, I use reinforcement learning and Bayesian optimization algorithms to make data-driven decisions to optimize the productivity while minimizing the impact of such systems.
