Pest and Disease Prediction and Management for Sugarcane Using a Hybrid Autoregressive Integrated Moving Average—A Long Short-Term Memory Model
Minghui Wang, Tong Li
Abstract
This study introduces a hybrid AutoRegressive Integrated Moving Average (ARIMA)—Long Short-Term Memory (LSTM) model for predicting and managing sugarcane pests and diseases, leveraging big data for enhanced accuracy. The ARIMA component efficiently captures linear patterns in time-series data, while the LSTM model identifies complex nonlinear dependencies. By integrating these two approaches, the hybrid model effectively handles both linear trends and nonlinear fluctuations, improving predictive performance over conventional models. The model was trained on 33 years of meteorological and pest occurrence data, and its effectiveness was evaluated using mean square error (MSE), root mean square error (RMSE) and mean absolute error (MAE). The results show that the ARIMA-LSTM model achieves an MSE of 2.66, RMSE of 1.63, and MAE of 1.34, outperforming both the standalone ARIMA model (MSE = 4.97, RMSE = 2.29, MAE = 1.79) and LSTM model (MSE = 3.77, RMSE = 1.86, MAE = 1.45). This superior performance highlights its ability to effectively capture seasonal variations and complex nonlinear patterns in pest outbreaks. Beyond accurate forecasting, this model provides valuable decision-making support for agricultural management, aiding in early intervention strategies. Future enhancements, including the integration of additional variables and climate change factors, could further expand its applicability across diverse agricultural sectors, improving crop yield stability and pest control strategies in an increasingly unpredictable climate.