Pine wilt disease (PWD), also known as pine wilt nematode disease, is a severe forest disease caused by the pine wood nematode ( Bursaphelenchus xylophilus ), which spreads rapidly and causes severe ecological damage in China. However, the existing detection methods have low accuracy in identifying early‐stage infected trees and are prone to missing detections. To address these issues, this study proposes YOLOv11‐OC, a detection method of pine wilt disease based on improved YOLOv11 with UAV remote sensing images. The omni‐dimensional dynamic convolution (ODConv) is employed to optimize the C3K2 module, thereby enhancing the feature extraction ability for small targets and improving the accuracy of identifying early‐stage disease areas. Meanwhile, the context anchor attention (CAA) mechanism is introduced to improve the C2PSA module, enhancing the model's context awareness, effectively reducing missed detections, and improving detection performance in complex backgrounds. Experimental results demonstrate that the proposed YOLOv11‐OC algorithm outperformed the original YOLOv11, achieving a precision of 94.2%, a recall of 83.1%, a F1‐score of 88.3%, and a mean average precision (mAP) of 88.9%. Compared to the original algorithm, the improved version shows increases of 2.8% in precision, 1.0% in F1‐score, and 1.4% in mAP. Furthermore, the algorithm also demonstrates good generalization ability on the public PlantDoc dataset.

Pine Wilt Disease (PWD) is a highly infectious and lethal disease that severely threatens global pine forest ecosystems and forestry economies. Early and accurate detection of infected trees is crucial to prevent large-scale outbreaks and support timely forest management. However, existing remote sensing-based detection models often struggle with performance degradation in complex environments, as well as a trade-off between detection accuracy and real-time efficiency. To address these challenges, we propose an improved object detection model, YOLOv8-MFD, designed for accurate and efficient detection of PWD-infected trees from UAV imagery. The model incorporates a MobileViT-based backbone that fuses convolutional neural networks with Transformer-based global modeling to enhance feature representation under complex forest backgrounds. To further improve robustness and precision, we integrate a Focal Modulation mechanism to suppress environmental interference and adopt a Dynamic Head to strengthen multi-scale object perception and adaptive feature fusion. Experimental results on a UAV-based forest dataset demonstrate that YOLOv8-MFD achieves a precision of 92.5%, a recall of 84.7%, an F1-score of 88.4%, and a mAP@0.5 of 88.2%. Compared to baseline models such as YOLOv8 and YOLOv10, our method achieves higher accuracy while maintaining acceptable computational cost (11.8 GFLOPs) and a compact model size (10.2 MB). Its inference speed is moderate and still suitable for real-time deployment. Overall, the proposed method offers a reliable solution for early-stage PWD monitoring across large forested areas, enabling more timely disease intervention and resource protection. Furthermore, its generalizable architecture holds promise for broader applications in forest health monitoring and agricultural disease detection.