Crop Price Prediction Using Machine Learning

Abstract

Crop price prediction is a pivotal aspect of agricultural economics, impacting stakeholders across the industry, from farmers to policymakers to consumers. Traditional methodologies often struggle to provide accurate and efficient predictions, primarily due to the intricate and ever-changing nature of agricultural markets. However, in recent years, the emergence of machine learning techniques has offered promising solutions to enhance crop price prediction. This paper conducts an extensive review of various machine learning approaches utilized for this purpose, covering regression-based methods, time series forecasting techniques, ensemble methods, deep learning strategies, and hybrid models. We delve into the unique strengths, limitations, and practical applications of each technique. Moreover, we address the prevalent challenges associated with employing machine learning in crop price prediction, such as data accessibility, feature selection, model interpretability, scalability, and generalization. Additionally, we look ahead to future research avenues and opportunities aimed at refining the accuracy and utility of machine learning models in predicting crop prices. Through this comprehensive review, we aim to provide valuable insights for researchers, practitioners, and policymakers, facilitating informed decision-making in agricultural contexts through the utilization of machine learning methodologies.