Patch-Wise Prediction and Interpretable Analysis of Pine Wilt Disease Occurrence

The pine wood nematode, a microscopic worm-like organism, is the primary cause of Pine Wilt Disease (PWD), a serious threat to pine forests, as infected trees can die within a few months. In this study, we aim to predict the occurrence of PWD by leveraging geographical and meteorological features, with a particular focus on incorporating interpretability through explainable AI (XAI). Unlike conventional models that utilize features from a single point of location, our approach considers surrounding environmental factors (patches) and employs a channel grouping mechanism to aggregate features effectively, enhancing prediction accuracy. Experimental results demonstrate that the proposed model based on convolutional neural network (CNN) outperforms traditional point-wise models, achieving a 9.7% higher F1-score. Experimental results show that data augmentation further improved performance, while interpretability analysis identified precipitation and temperature-related features as the most significant contributors. The developed CNN model provides a robust and interpretable framework, offering valuable insights into the spatial and environmental dynamics of PWD occurrence.