Built for farmers who want a clear answer and for engineers who want to understand exactly how that answer was produced.
Crop selection in India is still largely driven by tradition and word-of-mouth. A farmer who picks the wrong crop for a given district and season can lose an entire season's income. We set out to build a system that turns decades of historical agricultural records into a single, actionable recommendation.
Three public datasets were collected, cleaned, and merged to build the training set:
After merging on state, district, crop, and season keys, records with missing production or price data were dropped. Average yield (production / area) and expected revenue per hectare were derived as target-adjacent features.
What it does: Given a district, state, season, area, rainfall, and historical yield, it predicts the single most profitable crop to grow.
Architecture: A two-layer stacking classifier. The base layer consists of three independently trained models — Random Forest, Gradient Boosting Classifier, and AdaBoost — each producing probability estimates across all crop classes. These out-of-fold predictions are fed as features to a Logistic Regression meta-learner, which produces the final class output. Stacking was chosen over a single model because each base learner captures different non-linear patterns, and the meta-learner learns how to weight them optimally.
Inputs: State, district (label-encoded), season (label-encoded), land area, average rainfall, average historical yield.
What it does: Predicts expected revenue per hectare for the recommended crop in a given district and season.
Architecture: A Gradient Boosting Regressor trained on historical revenue per hectare, calculated from production records and market prices. The model captures non-linear interactions between district, season, crop type, and yield history. The app applies a ±20% band around the point estimate to reflect realistic market price volatility.
The interactive map uses Leaflet.js with a district-level GeoJSON of India. Each polygon is pre-annotated with its nearest matching model district via string similarity. Districts not in the training vocabulary fall back to the nearest supported district — the location strip shows exactly which district's data was used.
Enter a district, growing season, and farm area. Get a crop recommendation and projected revenue estimate from a trained machine learning pipeline.
Open Advisor →Stacking Ensemble (RF · GBM · AdaBoost) • Gradient Boosting Regressor