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How Deep Learning Affect Price Forecasting of Agricultural Supply Chain?
Abstract
Due to the many factors that affect commodity prices, price forecasting has become
a problematic research point. With the development of machine learning and artificial intelligence, some advanced ensemble algorithms and deep learning prediction methods based on time series have high accuracy and robustness. These algorithms have gradually become the inevitable choice for solving price prediction problems. Based on the National Bureau of Statistics of China data from January 2012 to December 2021, this study proposes deep learning combined forecasting model based on neurall networks to predict wheat prices and fill the research gap in agricultural product price forecasting. Researchers utilize P>lhon and Selenium to realize the automatic data acquisition of web pages to achieve the purpose of data collection and calculation. The final price result curve predicted by the price prediction model based on LSTM deep learning agrees with the actual
price curve. and the mean square error MSE is only 0.00026. It shows that this prediction model based on time series influenced by multiple factors has an excellent application prospect in price prediction.
According to the FAO, wheat, corn, and rice are staple crops that support global food security, providing 50% of the world’s dietary energy. The ability to predict accurately these key food crop agricultural commodity prices is important in stabilizing markets, supporting policymaking, and informing stakeholders’ decisions. To this aim, machine learning (ML), ensemble learning (EL), deep learning (DL), and time series methods (TS) have been increasingly used for forecasting due to the rapid development of computational power and data availability. This study presents a systematic literature review (SLR) of peer-reviewed original research articles focused on forecasting the prices of wheat, corn, and rice using machine learning (ML), deep learning (DL), ensemble learning (EL), and time series techniques. The results of the study help uncover suitable forecasting methods, such as hybrid deep learning models that consistently outperform traditional methods, and they identify important limitations in model interpretability and the use of region-specific datasets, highlighting the need for explainable and generalizable forecasting solutions. This systematic review adheres to the PRISMA 2020 reporting guidelines.
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