Detection algorithm for pine wilt disease in complex environments

Pine wilt disease (PWD), caused by the pine wood nematode, is a highly destructive forest disease with severe ecological and economic consequences worldwide. Early and accurate detection of PWD is therefore crucial for effective prevention and control. To address the challenge of detecting small, sparsely distributed infected pine trees in complex environments, this study utilized RGB orthophotos captured by DJI drones flying at altitudes of 50–100 m across two representative pine forest regions in China - Jiangning District, Jiangsu Province (southern region) and Xinbin County, Liaoning Province (northern region). We propose an improved detection model, termed YOLO-ESCF, designed to enhance detection performance under challenging conditions. The model integrates a C3ECA fused attention module and a SimCSPSPPF module into the backbone network, which effectively reduces the interference caused by overlapping tree crowns and environmental complexity while improving sensitivity to early-stage symptoms. In the neck structure, an enhanced coordinate convolution is introduced to enable the network to exploit spatial positional information, thereby improving its ability to learn target distribution patterns. Experimental results demonstrate that the proposed YOLO-ESCF model achieves an average detection accuracy of 82.9 % and an F1-score of 0.919 across both early and late PWD stages, outperforming conventional detection models. With a model size of only 18.8 MB and an FPS (frames per second, f/s) of 121.9, YOLO-ESCF offers a strong balance between accuracy and efficiency. These results highlight its potential for real-time monitoring and automated early warning systems, providing valuable support for timely intervention to minimize ecological and economic losses. • A novel drone dataset for pine wood nematode disease. • The proposed YOLO-ESCF model has a higher accuracy compared with the baseline model. • The model can be designed to meet the needs of detecting pine wood nematode diseases in complex environments.