Algorithm for Detecting Trees Affected by Pine Wilt Disease in Complex Scenes Based on CNN-Transformer

Pine wilt disease, a highly destructive forest disease with rapid spread, currently has no effective treatments. Infected pine trees usually die within a few months, causing severe damage to forest ecosystems. A rapid and accurate detection algorithm for diseased trees is crucial for curbing the spread of this disease. In recent years, the combination of drone remote sensing and deep learning has become the main methods of detecting and locating diseased trees. Previous studies have shown that increasing network depth cannot improve accuracy in this task. Therefore, a lightweight semantic segmentation model based on a CNN-Transformer hybrid architecture was designed in this study, named EVitNet. This segmentation model reduces network parameters while improving recognition accuracy, outperforming mainstream models. The segmentation IoU for discolored trees reached 0.713, with only 1.195 M parameters. Furthermore, considering the diverse and complex terrain where diseased trees are distributed, a fine-tuning model approach was adopted. After a small amount of training, the IoU on new samples increased from 0.321 to 0.735, greatly enhancing the practicality of the algorithm. The model’s segmentation speed in the task of discolored trees identification meets the requirements of real-time performance, and its accuracy exceeds that of mainstream semantic segmentation models. In the future, it is expected to be deployed on drones for real-time recognition, accelerating the entire process of discovering and locating infected trees.