Millet Crop Yield Variation through Feature Extraction Using XGBoost

Agriculture, being a vital industry, relies significantly on predicting and improving crop yields for sustainable food production. In this context, millet, a staple crop in various regions globally, holds immense agricultural importance due to its nutritional value and resilience to harsh environmental conditions. The approach outlined in this study revolves around the utilization of advanced machine learning techniques, specifically XGBoost, which is a robust gradient boosting algorithm known for its effectiveness in handling structured data and making accurate predictions. This algorithm is employed to create a predictive model for forecasting millet yields.