This is like having a smart weather app for your corn fields that doesn’t just tell you tomorrow’s forecast, but predicts how much corn you’ll harvest at the end of the season by combining many different prediction methods and data sources into one best guess.
Growers and agribusinesses struggle to accurately predict corn yield at the field or sub-field level, which hampers decisions on seed selection, fertilizer rates, irrigation, and marketing. The research tackles this by building a more accurate, data-driven yield prediction system tailored to on-farm conditions.
Access to large, high-quality, on-farm yield histories and management data; localized agronomic know-how in model feature design; and potentially proprietary ensemble modeling pipelines tuned to specific geographies and hybrids.
Classical-ML (Scikit/XGBoost)
Time-Series DB
High (Custom Models/Infra)
Aggregating and cleaning heterogeneous on-farm data (weather, soil, management) at scale; model retraining and calibration across many fields and seasons.
Early Majority
Focus on on-farm, field-level corn yield prediction using a multi-model (ensemble) approach rather than a single algorithm or coarse regional statistical model, enabling more precise, operational agronomic decisions.