PREDICTION OF AGRICULTURAL PRODUCE USING MACHINE LEARNING ALGORITHM (A CASE STUDY OF COCOA)

This study examines the application of machine learning to agricultural output prediction, with a specific emphasis on cocoa production. Advanced data analysis techniques are becoming more crucial as the need for more accurate forecasting in farming increases. In this study, we examined historical data on cocoa yields, weather, soil types, and market activity using a variety of machine learning approaches, including linear regression, decision trees, and support vector machines. The outcomes demonstrated that these models had significantly higher prediction accuracy than conventional statistical methods. One model in particular stood out for its high degree of accuracy, providing insightful information that can assist farmers and other agricultural stakeholders in making more informed decisions on crop planning and resource utilization. Overall, this study provides a strong foundation for future research in related fields and demonstrates how machine learning may significantly increase productivity and promote sustainability in agriculture. Predictions are a crucial component of contemporary agriculture. They enhance food security and assist farmers in more effectively allocating resources. In this abstract, we describe a study that predicts the price of cocoa using machine learning. To enhance forecasts, we employ past data in addition to other pertinent factors. We incorporate climatic data and assess model performance using important indicators. This research helps inform data driven decisions in agriculture and provides valuable insights for sustainable agricultural practices