FARMER E-MARKET FOR BETTER PRODUCT DEMAND PREDICTION

Abstract: Farmers often have to decide what to grow and how much to bring to the market without having a clear idea of future demand. When too much of a crop reaches the market, some of it may remain unsold and prices may fall. When the available quantity is too low, buyers may struggle to find the product they need. This paper proposes a Farmer E-Market system that brings farmers and market information together and uses past demand records to estimate future demand for selected crops. The proposed system allows a farmer to enter the crop name, quantity and expected harvest date. It then compares the farmer’s planned supply with current supply, expected future supply and predicted demand for the selected region and period. The first version uses a Random Forest regression model trained on historical crop-demand records containing year, month, region, crop and market demand. The project uses a time-based train-test split so that earlier records are used for training and later records are used for testing. The system is intended to provide a simple, understandable view of demand and supply rather than guarantee a sale or a particular price. Its usefulness will depend on the quality, coverage and freshness of the data available.

Keywords: Farmer E-Market, Crop Demand Prediction, Random Forest, Supply-Demand Comparison, Digital Agriculture.