The application of integrated deep learning models with the Assistance of meteorological factors in forecasting major tobacco diseases

Abstract

Tobacco is a crucial economic crop that is highly susceptible to various diseases during its growth cycle. Developing accurate predictive models is essential for devising effective disease management strategies and reducing pesticide use. This study investigated the occurrence of four major tobacco diseases—tobacco mosaic virus, black shank, bacterial wilt, and brown spot—in seven key tobacco-producing regions of Hunan Province from 2009 to 2015. An ensemble deep learning method, BCNN-LSTM, was developed by integrating Bidirectional Convolutional Neural Networks with Long Short-Term Memory networks. The predictive performance of the BCNN-LSTM model was evaluated across three temporal prediction scales including one-step prediction, short-term prediction, and long-term prediction. In one-step prediction, the average R² of BCNN‐LSTM reached 0.940—a 14.5 % improvement over the next-best model, CNN1D‐LSTM (0.821). For short‐term prediction, the BCNN‐LSTM attained an average R² of 0.822, outperforming CNN1D‐LSTM by 11.7 % (0.736). In long‐term prediction, the BCNN‐LSTM achieved an average R² of 0.838, marking a 34.2 % enhancement compared to the top competing Temporal Convolutional Network (TCN) model (0.625). Furthermore, across all three prediction scales, BCNN‐LSTM consistently delivered lower average Root Mean Squared Error (RMSE) values—3.880, 9.974, and 10.610, respectively. These results demonstrate that, relative to conventional forecasting models, the incorporation of a bidirectional convolution module in BCNN‐LSTM enables effective capture of the personalized temporal effects of meteorological factors and their interactions, thereby bolstering the model’s ability to represent nonlinear and dynamic characteristics. Notably, as a lightweight model (<10 MB), BCNN‐LSTM exhibits excellent scalability and adaptability, making it well-suited for integration into intelligent agricultural systems for large-scale monitoring of tobacco diseases.