A Review of Machine Learning Applications in Predicting Agricultural Commodity Prices

Agricultural price prediction is crucial for the economic well-being of farmers, policymakers, businesses, and other stakeholders in the agricultural sector. However, this task is challenging due to the complex and dynamic nature of agricultural markets. Machine learning algorithms have the potential to revolutionize agricultural price prediction by enhancing accuracy, enabling real-time forecasting, and offering customizable and integrative solutions. This paper reviews recent state-of-the-art research on machine learning algorithms for agricultural price prediction. Firstly, we discuss the importance of agriculture in developing countries, identify the challenges of predicting agricultural prices, and highlight how machine learning algorithms can provide better predictive capabilities. Furthermore, a comprehensive analysis of recent studies is presented, discussing the strengths and weaknesses of various machine learning techniques. Our findings suggest that while machine learning holds significant promise for improving agricultural price prediction, further research is essential to address the current limitations and challenges associated with these approaches. This offers valuable insights into the current state of research and practical implications for the application of machine learning in agricultural price forecasting.