DEVELOPMENT OF A MACHINE LEARNING MODEL FOR CROP YIELD PREDICTION IN AGRICULTURE

Abstract

Agricultural productivity remains a crucial element of food security and economic stability in many countries. With climate variability, land degradation, and increasing demand for precision agriculture, accurate crop yield prediction has become essential for informed decision-making. This examines ambitions to broaden and examine a system learning (ML) model for predicting crop yields using environmental, soil, and ancient yield information. The research utilized a dataset combining meteorological variables, soil properties, and crop control practices throughout 3 Indian states over ten years. Several ML algorithms, which includes Linear Regression, Decision Tree Regression, Random Forest, and XGBoost, have been evaluated. The Random Forest model outperformed others with an R² rating of 0.89 and RMSE of 2.Five quintals/ha. The consequences demonstrate the capability of ensemble mastering models in dealing with non-linear agricultural statistics. The take a look at concludes with the consequences of ML-based yield forecasting structures for precision agriculture and coverage-making.