AI advances agricultural monitoring and crop breeding

Artificial intelligence is being deployed to advance agricultural monitoring and crop development. Researchers at the Indraprastha Institute of Information Technology-Delhi, in collaboration with the MS Swaminathan Research Foundation, have developed SICKLE (Satellite Imagery for Cropping annotated with Key parameter Labels). This multi-sensor dataset combines imagery from Landsat-8, Sentinel-1, and Sentinel-2 with field-level data to help AI models identify crop types, monitor growth stages, and predict yields across large areas.

Simultaneously, companies like Bayer are utilizing AI to accelerate crop breeding processes. By using digital twins to simulate testing environments and correlating genomic information with phenotypic data, researchers can predict optimal breeding combinations. This technology reduces the timeframe for developing new crop varieties from years to months, producing plants with higher yields and increased resistance to drought, pests, and extreme weather, such as the short-stature corn developed in Iowa.