Lightweight YOLOv8-based real-time detection of pine wilt disease from drone imagery.

Introduction: A core bottleneck of forestry remote sensing lies in accurate, real-time pine wilt disease monitoring on UAV-borne edge hardware, which suffers from constrained computing capacity and complicated field forest backgrounds. To fill this technical gap, we developed an ultra-lightweight real-time detection architecture named Edge-Forest YOLO in this work. Methods: Methods: Built upon the baseline YOLOv8n network, three targeted optimizations were embedded into the proposed model: (1) a domain-adaptive data augmentation workflow to mitigate poor generalization induced by variable illumination and uneven lesion sizes in complex woodland; (2) scale-aware asymmetric channel redistribution, cutting 37.5% shallow channels and expanding 75% deep channels to remove redundant spatial features and strengthen high-level pathological feature extraction; (3) Cross-layer ECA attention adopting 1D convolution to capture inter-channel correlation and concentrate on diseased regions with minimal computation overhead. All model validation was performed on the public high-resolution UAV pine wilt PDT dataset. Results: Edge-Forest YOLO only occupies 2.31 M storage with mAP@0.5 up to 92.7%. Its single-image inference costs 4.2 ms on regular computing equipment and runs at around 26 FPS on the Jetson Nano edge platform. Compared with YOLOv8s and customized YOLO-DP, our model cuts over half parameter quantity while retaining competitive detection precision. Discussion: The proposed lightweight detector supplies a low-power, practically deployable solution for on-board UAV real-time forest disease monitoring, supporting rapid in-field pine wilt diagnosis and facilitating scientific decision-making for forest health management and disease prevention.