On-farm research are conducted in real farm scenarios, considering unique properties of each individual field such as soil type, topography, and microclimate. By combining various open-access data sources, such as satellite imagery, LiDAR, SSURGO and weather, with ground-based yield monitoring data, several decisions can be made, including crop yield prediction, management zone delineation for fertilization and seeding rates, the effects of fungicide application and many more. This case study will explore how such data can be integrated into AI/ML models to make these important decisions using freely available open-access data.
