Forecasting crop yields is important for agricultural making plans, aid allocation, and choice-making. With the usage of precise and timely projections, farmers may also improve their strategies, decrease risks, and increase general manufacturing. Machine studying algorithms have recently shown to be helpful gear for predicting agricultural productiveness due to their ability to deal with massive volumes of facts and uncover complicated linkages. To assess numerous agricultural yield prediction system gaining knowledge of structures' overall performance and determine the maximum efficient approaches. An considerable series of historic agricultural data, including weather patterns, soil properties, and management strategies, turned into used to perform an in-depth examination. Several machine learning algorithms, including Random Forest, Support Vector Machines, Artificial Neural Networks, and Gradient Boosting, were developed and assessed based on their predictive accuracy, computational efficiency, and robustness in processing various types of agricultural data. Several metrics, including mean absolute error, root mean square error, and coefficient of determination, were used to assess how well the algorithms performed. Using feature importance analysis, the main factors influencing agricultural productivity were also identified. This paper goes into considerable detail about the choice and use of machine learning techniques for agricultural production prediction. The findings can assist agricultural stakeholders, researchers, and policymakers in making well-informed decisions on agricultural planning, resource allocation, and risk management strategies.

Crop yield prediction, machine learning algorithms, Random Forest, Support Vector Machines, Artificial Neural Networks, Gradient Boosting, agricultural data analysis.