Unified Forecasting Models: Crop Recommendation using Predicted Rainfall with Machine Learning

Accurate crop recommendation based on environmental and climatic factors is crucial for optimizing agricultural yield. This model predicts suitable crops for specific regions in India by integrating rainfall prediction with crop recommendation. The approach begins by collecting historical weather data, particularly rainfall, across various regions. This data is used to predict future rainfall patterns. The predicted rainfall, along with factors like humidity, temperature, and soil nutrients (nitrogen, phosphorus, potassium), is fed into a machine learning model designed to recommend suitable crops. The model employs Support Vector Regression (SVR) for rainfall prediction and AdaBoost Classifier for crop recommendation. The study identifies key factors influencing crop selection, providing valuable insights for farmers to make data-driven decisions and enhance crop productivity.