It is a Machine Learning Weather Forecasting and Crop Recommendation Model for Precision Irrigation Practices.
This project presents an integrated approach to enhance agricultural decision support through machine learning. The study focuses on accurate rainfall prediction using an XGBoost regression model and subsequent crop recommendation employing a Random Forest classifier. The models demonstrate robust performance, with the rainfall prediction model evaluated using Mean Squared Error and the crop recommendation model achieving high accuracy. The innovative forecast generation involves sequential predictions, incorporating forecasted rainfall into crop recommendations. This Model's potential impact on agriculture lies in optimizing water management, increasing crop yields, and mitigating risks for farmers. Furthermore, the report highlights the future directions for development and underscores practical integration of the machine learning model into decision-making processes, emphasizing its significance in real-world agricultural scenarios for sustainable practices, food security, and economic development.