Crop Yield Predication using Random Forest Regression Algorithm

This research presents an automated system for predicting crop yield using the Random Forest Regression algorithm. The model leverages agricultural parameters such as soil composition, rainfall, temperature, and fertilizer usage to provide real-time and accurate yield predictions. A user-friendly web application developed with Python Flask allows for easy interaction, enabling farmers and agricultural professionals to input data and receive yield estimates. The results demonstrate the model’s reliability, achieving over 99% accuracy on test data. The system's modularity supports future expansions, including real-time data integration and mobile deployment for widespread agricultural use. This project presents an AI-driven crop yield prediction system utilizing the Random Forest Regression algorithm. The system is designed to forecast agricultural yield based on various factors such as soil quality, rainfall, temperature, and historical crop data. Implemented using Python and deployed via a user-friendly Flask web interface, the application enables real-time yield prediction, empowering farmers and policymakers to make informed decisions. The model, trained on a diverse dataset, demonstrates high accuracy and robustness, handling non-linear data patterns effectively. By combining machine learning and intuitive design, this solution enhances agricultural planning, resource management, and overall productivity.