Machine Learning Based Hybrid System for Rainfall Prediction and Crop Recommendation

Accurate rainfall prediction plays an important role in agricultural decision making like irrigation, crop selection, and yield expectations. Powerful machine learning models, such as regression and classification algorithms are used to analyze historical rainfall data and other relevant weather parameters to predict future rainfall patterns. This paper proposes a machine learning-driven system to empower farmers with accurate rainfall predictions and data-driven crop recommendations. A novel system that incorporates Random Forest and Adaboost models is used to predict the rainfall and recommend crops. The algorithm uses various weather parameters like temperature, wind speed, humidity and dew to predict future rainfall. Beyond rainfall, the system also recommends suitable crops based on factors like soil type, climate conditions, and historical crop performance in the region. The machine learning models are trained and validated using an open-source dataset of past rainfall records, weather data, and crop yields. The system's effectiveness is evaluated using rigorous metrics like accuracy, precision, recall and prediction error metrics, ensuring reliable predictions and recommendations. These predictions enable the farmers in optimizing resource allocation, reducing water wastage, and increasing crop productivity.