Development of a Predictive Model for Citrus Shipments and Prices, and Analysis of Influencing Factors

Given the significance of the citrus industry, which accounts for more than half of Jeju Island's agricultural revenue (KRW 950.8 billion, 55.92% of farming income), this study aims to develop prediction models for open-field and greenhouse-grown citrus shipment volumes and prices. While previous research has explored crop production forecasting, there is a notable absence of comprehensive studies integrating deep learning approaches with environmental factors for Jeju citrus prediction, particularly in addressing the complex interplay between weather patterns and market dynamics. To bridge this gap, this study analyzed various domestic and international factors, including weather information, public holidays, and imported fruit data, which were utilized as independent variables in the model design. Deep learning-based models, specifically LSTM for capturing long-term dependencies, Seq2Seq for handling variable-length sequences, and Attention mechanisms for focusing on relevant temporal patterns, were employed to perform the predictions. Their accuracy and stability were thoroughly evaluated against traditional machine learning benchmarks. The findings revealed that citrus shipment volumes and prices are significantly influenced by temporal factors (average temperature, shipment timing) and market dynamics (transaction volume, competing fruit prices), with the Seq2Seq model achieving the highest prediction accuracy. Furthermore, by adjusting the window sizes in various time series models, we were able to simulate different scenarios, providing stakeholders with a robust tool for market planning and decision-making. The findings of this research are expected to contribute to the efficient operation of the citrus market and the maximization of benefits for related stakeholders.