Detection of Pine Wilt Disease Using Drone Remote Sensing Imagery and Improved YOLOv8 Algorithm: A Case Study in Weihai, China

• Published in: Forests 2023-10, Vol.14 (10), p.2052
• Main Authors: Wang, Shikuan, Cao, Xingwen, Wu, Mengquan, Yi, Changbo, Zhang, Zheng, Fei, Hang, Zheng, Hongwei, Jiang, Haoran, Jiang, Yanchun, Zhao, Xianfeng, Zhao, Xiaojing, Yang, Pengsen
• Format: Article
• Language: English
• Subjects: Accuracy; Algorithms; attention mechanism; Classification; Computer networks; Deep learning; Disease; Diseases and pests; Ecological effects; Economic impact; Epidemics; Forest protection; Identification and classification; Image enhancement; Inserts; intelligent identifying; Localization; Machine learning; Machine vision; Modules; Nematode diseases of plants; Neural networks; Object recognition; Pine; Pine trees; pine wilt disease (PWD); Remote sensing; Satellites; small object detection layer; Technical services; Trees; Unmanned aerial vehicles; Water conservation; Wilt
• ISSN: 1999-4907; 1999-4907
• DOI: 10.3390/f14102052

Pine Wilt Disease (PWD) is a devastating global forest disease that spreads rapidly and causes severe ecological and economic losses. Drone remote sensing imaging technology is an effective way to detect PWD and control its spread. However, the existing algorithms for detecting PWD using drone images have low recognition accuracy, difficult image calibration, and slow detection speed. We propose a fast detection algorithm for PWD based on an improved YOLOv8 model. The model first adds a small object detection layer to the Neck module in the YOLOv8 base framework to improve the detection performance of small diseased pine trees and then inserts three attention mechanism modules on the backbone network to extend the sensory field of the network to enhance the extraction of image features of deep diseased pine trees. To evaluate the proposed algorithm framework, we collected and created a dataset in Weihai City, China, containing PWD middle-stage and late-stage infected tree samples. The experimental results show that the improved YOLOv8s-GAM model achieves 81%, 67.2%, and 76.4% optimal detection performance on mAP50, mAP50-95, and Mean evaluation metrics, which is 4.5%, 4.5%, and 2.7% higher than the original YOLOv8s model. Our proposed improved YOLOv8 model basically meets the needs of large-scale PWD epidemic detection and can provide strong technical support for forest protection personnel.