CROP PROFIT PREDICTION USING MACHINE LEARNING

Abstract

This project presents a Flask-based Agricultural Profit Predictor using tree-based regressors (Random Forest; XGBoost supported ) trained on historical crop records to forecast yield and price. The app requires 8 simple farmer inputs and currently uses fixed default soil and weather parameters for prediction . Outputs include predicted yield, price, total revenue, cost, profit and ROI, providing a low-barrier decision-support tool for farmers. The system emphasizes practicality and ease-of-use while enabling accurate, deployable ML-driven recommendations.crop selection.