A feature recognition and detection algorithm for pine wilt disease trees based on FLMP-YOLOv8

Abstract

Pine wilt disease, a highly contagious forest disease caused by the pine wood nematode and primarily transmitted via its insect vector, the pine sawyer beetle (Monochamus spp.), poses a significant threat to forest ecosystems. Accurate detection of infected trees is vital for effective prevention and control. This study pioneers the detection of pine wilt disease-infected trees in the China’s Qinba Mountain region, where the complex terrain and uneven forest distribution thinder feature extraction of diseased trees. To address data collection challenge, this paper proposes a novel feature recognition and detection method for pine wilt disease-infected trees based on an FLMP-YOLOv8 algorithm. The enhanced features include: first, integrating FasterBlock module into the backbone and neck networks of YOLOv8 to, boost the model’s feature extraction capability and reduce complexity, thereby achieving a balance between detection efficiency and accuracy. Second, a Large Separable Kernel Attention (LSKA) mechanism is incorporated into the Spatial Pyramid Pooling-Fast (SPPF) module of YOLOv8, improving the model’s ability to perceive fine details of diseased trees and reducing interference from other elements in the forest. Finally, the MPDIoU loss function is adopted for bounding box regression, enhancing the precision of localization. Experimental results on a self-constructed dataset demonstrate the improved model efficacy, achieving 92.0% precision, 80.8% recall, 87.0% mean Average Precision (mAP@0.5), and 81.79 FPS detection speed. Compared to the original YOLOv8 model, the improved algorithm shows increases of 2.2% in precision, 0.6% in recall, and 2.0% in mAP@0.5, with a detection speed improvement of 65.48 FPS. This study provides a more reliable and cost-effective method for detecting trees infected with pine wilt disease.