Early-Stage Pine Wilt Disease Detection via Multi-Feature Fusion in UAV Imagery

Abstract

Pine wilt disease (PWD) is a highly contagious and devastating forest disease. The timely detection of pine trees infected with PWD in the early stage is of great significance to effectively control the spread of PWD and protect forest resources. However, in the spatial domain, the features of early-stage PWD are not distinctly evident, leading to numerous missed detections and false positives when directly using spatial-domain images. However, we found that frequency domain information can more clearly express the characteristics of early-stage PWD. In this paper, we propose a detection method based on deep learning for early-stage PWD by comprehensively utilizing the features in the frequency domain and the spatial domain. An attention mechanism is introduced to further enhance the frequency domain features. Employing two deformable convolutions to fuse the features in both domains, we aim to fully capture semantic and spatial information. To substantiate the proposed method, this study employs UAVs to capture images of early-stage pine trees infected with PWD at Dahuofang Experimental Forest in Fushun, Liaoning Province. A dataset of early infected pine trees affected by PWD is curated to facilitate future research on the detection of early-stage infestations in pine trees. The results on the early-stage PWD dataset indicate that, compared to Faster R-CNN, DETR and YOLOv5, the best-performing method improves the average precision (AP) by 17.7%, 6.2% and 6.0%, and the F1 scores by 14.6%, 3.9% and 5.0%, respectively. The study provides technical support for early-stage PWD tree counting and localization in the field in forest areas and lays the foundation for the early control of pine wood nematode disease.

Keywords

• pine wood nematode
• drone imagery
• object detection
• frequency domain
• feature fusion