Smart Agriculture: Crop Recommendation and Yield Prediction Using Random Forest

Agriculture is a critical sector of India's economy, but environmental changes have made it challenging for farmers to anticipate crop recommendations and yields. Traditional methods based on farmers experience are no longer reliable due to unforeseen climate and environmental changes. The integration of traditional methods with Machine Learning (ML) techniques, can significantly improve agricultural decision-making by recommending optimal crops and predicting their yield. This project proposes a system that utilizes supervised ML algorithm called Random Forest to predict crop yield and recommend suitable crops based on factors like nitrogen, phosphorous, potassium levels in the soil, temperature, humidity, pH, and rainfall. Random Forest models can provide highly accurate predictions for crop recommendation, enabling farmers to optimize their practices, manage risks, and make informed decisions. These recommendations not only enhance agricultural performance but also support sustainable farming practices, fostering food security and economic resilience. The proposed crop recommendation and yield prediction system serves as a valuable tool in agricultural decision-making in India.