A DATA-DRIVEN APPROACH TO CROP YIELD PREDICTION USING MACHINE LEARNING
Keywords:
Crop Yield Prediction, Machine Learning, Data-Driven Agriculture, Predictive Analytics, Precision Farming, Soil Analysis, Weather Data, Agricultural Forecasting, Regression Models, Ensemble LearningAbstract
This paper aims to improve agricultural productivity and decision-making by presenting a data-driven approach to crop yield prediction using machine learning. The research utilizes historical data on weather, soil, crops, and farming to develop prediction models that accurately predict crop yields. The optimal approach is determined by employing and evaluating a variety of algorithms, including ensemble methods, decision trees, and regression models. We ensure the model's dependability and robustness by training and validating it on real-world datasets. The findings indicate that machine learning generates predictions that are considerably more precise than conventional statistical methodologies. The proposed system is beneficial to both farmers and lawmakers because it provides current information on sustainable agricultural planning, risk management, and resource allocation.
