Pine wilt disease is a highly contagious forest quarantine ailment that spreads rapidly. In this study, we designed a new Pine-YOLO model for pine wilt disease detection by incorporating Dynamic Snake Convolution (DSConv), the Multidimensional Collaborative Attention Mechanism (MCA), and Wise-IoU v3 (WIoUv3) into a YOLOv8 network. Firstly, we collected UAV images from Beihai Forest and Linhai Park in Weihai City to construct a dataset via a sliding window method. Then, we used this dataset to train and test Pine-YOLO. We found that DSConv adaptively focuses on fragile and curved local features and then enhances the perception of delicate tubular structures in discolored pine branches. MCA strengthens the attention to the specific features of pine trees, helps to enhance the representational capability, and improves the generalization to diseased pine tree recognition in variable natural environments. The bounding box loss function has been optimized to WIoUv3, thereby improving the overall recognition accuracy and robustness of the model. The experimental results reveal that our Pine-YOLO model achieved the following values across various evaluation metrics: MAP@0.5 at 90.69%, mAP@0.5:0.95 at 49.72%, precision at 91.31%, recall at 85.72%, and F1-score at 88.43%. These outcomes underscore the high effectiveness of our model. Therefore, our newly designed Pine-YOLO perfectly addresses the disadvantages of the original YOLO network, which helps to maintain the health and stability of the ecological environment.

Pine wilt disease (PWD) is a highly destructive forest disease. However, when PWD datasets are constructed, and image detection is conducted, Unmanned Aerial Vehicle (UAV)-based remote sensing detection often encounters the challenge of illumination-degraded UAV imagery. To address this problem, we propose a model called PineSight, which integrates three key enhancements into the Real-Time Detection Transformer (RT-DETR) framework, i.e., Contrastive Language–Image Pre-training (CLIP-LIT), Content-Aware Feature Re-Assembly (CARAFE) and Efficient Vision Transformer (EfficientViT) modules. The proposed model achieves optimal performance across multiple evaluation metrics; for example, the mAP@0.5, mAP@0.5:0.95, Precision and F1-score reach high values of 87.19%, 49.57%, 91.08% and 87.06%, respectively. Simultaneously, the parameter count is reduced from 20.08 M to 11.06 M, and GFLOPs decrease from 58.3 to 28.6. Evaluation on two independent datasets containing images captured under both normal and illumination-degraded conditions shows that PineSight exhibits robustness against illumination degradation. Even under such challenging conditions, the model maintains high performance; it shows high Precision and F1-score with values of 88.10% and 82.23%. The PineSight model not only improves the efficiency of PWD detection but also provides new perspectives and approaches for intelligent forest pest detection in illumination-degraded UAV imagery.