Predicting Agricultural Commodities Prices with Machine Learning: A Review of Current Research
It addresses price prediction challenges for farmers and policymakers in the agricultural sector, but is incremental as it reviews existing work.
This paper reviews recent research on using machine learning algorithms to predict agricultural commodity prices, highlighting their potential to improve accuracy and real-time prediction, but notes that further research is needed to address limitations.
Agricultural price prediction is crucial for farmers, policymakers, and other stakeholders in the agricultural sector. However, it is a challenging task due to the complex and dynamic nature of agricultural markets. Machine learning algorithms have the potential to revolutionize agricultural price prediction by improving accuracy, real-time prediction, customization, and integration. This paper reviews recent research on machine learning algorithms for agricultural price prediction. We discuss the importance of agriculture in developing countries and the problems associated with crop price falls. We then identify the challenges of predicting agricultural prices and highlight how machine learning algorithms can support better prediction. Next, we present a comprehensive analysis of recent research, discussing the strengths and weaknesses of various machine learning techniques. We conclude that machine learning has the potential to revolutionize agricultural price prediction, but further research is essential to address the limitations and challenges associated with this approach.