Machine Learning Improves Crop Yield Prediction in Precision Agriculture

Abstract

With the use of machine learning (ML) integrated into precision agriculture, farmers can now make data-driven decisions that improve crop yield forecast and maximize resource efficiency, completely changing the game when it comes to crop management. the influence of ML on precision farming, particularly how ML tools improve agricultural production predictions thru data analysis of various types (e.g., weather, soil, and crop health indicators). Machine learning algorithms like decision trees, regression models, and deep learning approaches allow farmers to better understand their data, which in turn improves their predictions and informs their farming practices. the possibilities for increasing agricultural output thru the integration of remote sensing and IoT technologies, the difficulties encountered during data collecting and model implementation, and the effective uses of ML for predicting crop yields. In the end, the results show how machine learning may revolutionize precision agriculture, providing clues about what is to come in terms of sustainable agricultural methods.