  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.
