AN OPERATIONAL APPROACH TO LARGE-SCALE CROP YIELD PREDICTION WITH SPATIO-TEMPORAL MACHINE LEARNING MODELS

Precise and reliable crop yield prediction serves as a valuable tool empowering farmers to make informed and sustainable decisions. However, yield prediction is intricately challenging due to the various factors that play a role in the complex landscape of crop growth. In this paper, we propose an operational yield forecasting approach based on spatiotemporal Machine Learning and a many-to-many network structure. We demonstrate that the simultaneous consideration of spatial and temporal dependencies of crop yield substantially improves the yield prediction performance on field and subfield level across all regions of our large-scale dataset. We further show how our many-to-many network structure leads to outstanding operational results.