USDA moves toward broader use of satellite data and AI for crop estimates

The US Department of Agriculture (USDA) plans to expand the use of satellite data and artificial intelligence to estimate planted area and crop yields. Under a pilot project, the agency will work with NASA to test advanced satellite monitoring systems and machine-learning algorithms.

The reform is aimed at improving the accuracy of USDA agricultural statistics, which serve as a key benchmark for global grain and oilseed markets. The agency’s estimates for corn, wheat and soybean production regularly influence commodity prices. The push to modernize the system intensified following criticism over significant discrepancies between preliminary and final crop estimates in 2025–2026.

High-resolution satellite imagery will be used to more accurately determine field boundaries, monitor crop development stages and assess the impact of droughts, floods and other weather events. This should allow USDA to track changes in crop conditions more quickly throughout the growing season.

Large volumes of satellite data will be processed using artificial intelligence and machine-learning algorithms and compared with digital crop growth models. Traditional farmer surveys will remain part of the system, but USDA plans to move them largely online and supplement them with existing geospatial data.

USDA expects the new system to allow more frequent forecast updates while improving their accuracy and transparency for agricultural market participants. According to US Under Secretary of Agriculture for Research, Education, and Economics Scott Hutchins, the first calibrated results from satellite and AI-based crop monitoring are expected before the end of the current presidential term.

Tags: soybean, USDA Report, wheat, NASA, crop forecast, AI, corn