Utilizing Ensemble Learning Techniques to Enhance Corn Price Prediction: A Case Study on South Dakota

Predicting crop prices is a complex challenge that farmers must navigate each year, but machine learning algorithms can provide valuable insights to support more informed decision making. In recent years, agricultural price prediction models have made significant advances, with architectures achieving varying degrees of success. However, ensuring the accuracy and reliability of these models remains an ongoing challenge. This research explores the use of stacking, an ensemble learning technique, to enhance the performance of base models in predicting corn prices in many regions of the state of South Dakota in the USA. We propose a hybrid architecture that combines Long Short-Term Memory Networks and Transformer Neural Networks, allowing us to leverage the strengths of both models. Using real corn price data from the last 11 years, our findings show that this stacked architecture not only outperforms its individual base models but also industry standard approaches. The ensemble model can predict within a one-month window, reaching a maximum mean average error of 6%. More research is needed to use multiple features, such as weather, crude oil price, and market demand, to provide an efficient decision support system.