Journal Article

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

Liu, Junyao, Wang, Yanyan, Zhang, Teng, Xu, Yuqing, Guo, Jingli, Chai, Haojie, Li, Bingqian

2026

Overview

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: 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. 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. 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.

• Publisher: Frontiers Media SA,Frontiers Media S.A
• Subject: Accuracy / attention mechanism / Channels / Computation / Data augmentation / Datasets / Decision making / Deep learning / Disease / Disease prevention / Forestry / Forests / Lesions / lightweight model / Object recognition / Original Research / Pine / pine wilt disease / Real time / Remote sensing / Semantics / Trees / UAV remote sensing / Unmanned aerial vehicles / Wavelet transforms / Wilt / Woodlands / YOLOv8