AI-Based Predictive Market Pricing Model for Rural Farmers Using Machine Learning

Abstract

Rural farmers in developing countries still struggle with wild price swings and unclear markets. These issues often leave them at the mercy of middlemen and facing serious financial stress. This paper introduces an AI-powered tool that predicts future crop prices using machine learning. By crunching historical market numbers together with local weather data, the tool gives farmers straightforward, useful pricing forecasts on their crops. But it’s not just a bunch of technical jargon, practical access matters. So, the model connects to a lightweight, easy-to-use web app that farmers can actually open and navigate. The backend runs on Node.js, built for speed with an asynchronous design, so price predictions pop up instantly. On the user side, the frontend is smooth and quick, built with React and TypeScript. The main goal? Close the information gap. Now, farmers have real market insights in their hands and can choose the best time and place to sell what they grow. Tests so far look promising, using XGBoost, the model’s price predictions land within 6% of actual prices for staple crops. All in all, this shows you can pair smart web tools with solid algorithms to actually help rural farmers, pushing for fairer markets and more sustainable agriculture.