Spatial-temporal analysis and trend prediction of regional crop disease based on electronic medical records

Abstract

Intelligent diagnosis of individual crop diseases has matured. How to understand the evolution patterns and predict regional disease trends remains a significant challenge. Plant Electronic Medical Records (PEMRs) offer valuable spatial-temporal characteristics about crop diseases, presenting a new opportunity for predicting the occurrence of regional diseases. In this study, we used a large prescription database from Beijing (2018–2021) to reframe regional disease prediction as a time series forecasting task. Firstly, to analyze spatial-temporal evolution patterns, we use ArcGIS to extract key information and identify potential connections between different disease occurrence points. Then, we developed a novel deep learning combined model SV-CBA, which combines Seasonal and Trend decomposition (STL) with Variational Mode Decomposition (VMD) to identify trend, seasonal, and residual components, and re-decomposes the residuals. STL-VMD can capture long-term trends and periodic variations while managing nonlinear and volatile characteristics. The CNN-BiLSTM-Attention model calculates disease trends by linearly integrating predictions of each sub-series. To reduce computational complexity while maintaining predictive performance, we propose an improved simplified attention mechanism. Our model demonstrates superior performance in both comparative and ablation experiments using the PEMRs dataset, outperforming numerous other models. This study provides accurate disease trend predictions, aiding farmers and regional managers in agricultural production management.