Accessible Crop Yield Prediction Using Decision Trees: A Farmer-Oriented Web-Based AI Application

Abstract

Traditional prediction methods are imprecise frequently due to their reliance on environmental & manual agricultural measurements. Accurate estimation of crop yield acts an essential role in modern agriculture by allowing optimized resource use, productive planning and improved decision-making. This study offers a machine learning-based crop yield prediction system using a Decision Tree Regressor trained on key agricultural and environmental parameters containing soil pH, temperature, rainfall and past yield data. The decision tree regressor model shows high performance of prediction with a mean absolute error (13.65), an accuracy (99.62%) & a root mean square error (1.15) suggesting a near-zero deviation & high consistency between actual values and predicted values. The system allows/shows real-time yield forecasting and provides an effortless interface for experts of agriculture, farmers & also policymakers. The proposed system highlights the potential of integrating machine learning and AI with lightweight web technologies to deliver highly accurate yield predictions, supporting sustainable agricultural planning and enhancing productivity through data-driven insights. To enable application the model is incorporated into a convenient web application (Streamlit) employing secure access control, modifiable input options and system-controlled data storage of estimation results.