Analysis of Changes in Rice Harvest Timing Using Deep Learning- Based Climate Prediction

Abstract This study predicts future harvest dates for Samkwang rice across 12 major cultivation regions in South Korea using a deep learning-based time-series approach. A Long Short-Term Memory (LSTM) model was trained on daily average temperature data spanning 1995 to 2024 and subsequently used to forecast regional temperatures for the period 2025–2040. Harvest dates were estimated based on the day cumulative temperature after heading reached 1,150°C, in accordance with official agronomic guidelines. Prediction accuracy was evaluated using the Mean Absolute Error (MAE) for each region. The results indicate a general advancement in harvest dates, attributed to accelerated heat accumulation under ongoing climate warming. However, regional variations were observed: northern regions exhibited more delayed and variable harvest patterns, whereas southern regions demonstrated greater temporal stability. Unlike previous studies confined to specific areas, this research incorporates a broad latitudinal dataset, offering a generalized predictive model to support adaptive agricultural strategies in the face of climate change.