Development of a Smart Agricultural Marketplace with Machine Learning-Based Price Forecasting

This paper presents a Smart Agricultural Marketplace integrated with machine learning–based price forecasting to assist farmers in making informed selling decisions. The system predicts commodity prices using historical agricultural market data and compares multiple regression models to identify the most effective predictor.

Linear Regression and Random Forest algorithms were trained and evaluated using realworld agricultural market datasets. Experimental evaluation shows that the Random Forest model achieves superior performance, obtaining an R² score of 0.9576 with significantly lower MAE and RMSE values compared to Linear Regression. The results demonstrate that machine learning–driven price forecasting can provide reliable decision support and reduce farmers’ dependence on intermediaries.

Development, Smart Agricultural, Marketplace, Forecasting

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