This is like a smart weather-and-farming advisor that looks at past data (such as weather, soil, and crop information) and predicts how much farmers in different Indian states are likely to harvest in the future.
Reduces uncertainty in crop planning by forecasting yields across Indian states so governments, agri-businesses, and farmers can better plan procurement, storage, pricing, and risk management.
Access to multi-year, region-specific agricultural and climate datasets and the ability to validate and recalibrate models at scale across many states and crop types.
Classical-ML (Scikit/XGBoost)
Time-Series DB
Medium (Integration logic)
Data quality and coverage across all Indian states and seasons; model performance limited by resolution and reliability of historical yield and weather data.
Early Majority
Focus on state-level agricultural yield prediction in India, likely using localized historical yield, climate, and possibly remote-sensing data, tuned to regional patterns rather than generic global models.