Tri-Model Agricultural Decision Support System: Explainable Yield Prediction, Pest Risk Forecasting and Market Price Estimation for Small Farmers

A tri-model agricultural decision support system for small farmers: explainable yield prediction, pest risk forecasting and market price estimation.

A tri-model agricultural decision support system for small farmers: explainable yield prediction, pest risk forecasting and market price estimation. It is offered as a reference project: a complete, worked example of how a system like this is designed, built and evaluated, so that a developer can study it, run it and learn from it.

It suits learners who want hands-on experience of the whole machine learning workflow: data collection and cleaning, feature engineering, model training and tuning, evaluation and presenting results.

The main parts are explanations of each result (feature importance or visual evidence), data preparation and exploration, model training and comparison of algorithms, evaluation with standard metrics, and a simple interface or API to try predictions. The reference build uses SHAP, Python, scikit-learn, Pandas, and Flask, with a clear project structure, setup instructions and sample data so that it can be run and extended.