Detection of Pine-Wilt-Disease-Affected Trees Based on Improved YOLO v7
Author:
Zhu Xianhao12ORCID, Wang Ruirui12, Shi Wei3, Liu Xuan12, Ren Yanfang12, Xu Shicheng12, Wang Xiaoyan12
Affiliation:
1. College of Forestry, Beijing Forestry University, Beijing 100083, China 2. Beijing Key Laboratory of Precision Forestry, Beijing Forestry University, Beijing 100083, China 3. Beijing Ocean Forestry Technology Co., Ltd., Beijing 100083, China
Abstract
Pine wilt disease (PWD) poses a significant threat to global pine resources because of its rapid spread and management challenges. This study uses high-resolution helicopter imagery and the deep learning model You Only Look Once version 7 (YOLO v7) to detect symptomatic trees in forests. Attention mechanism technology from artificial intelligence is integrated into the model to enhance accuracy. Comparative analysis indicates that the YOLO v7-SE model exhibited the best performance, with a precision rate of 0.9281, a recall rate of 0.8958, and an F1 score of 0.9117. This study demonstrates efficient and precise automatic detection of symptomatic trees in forest areas, providing reliable support for prevention and control efforts, and emphasizes the importance of attention mechanisms in improving detection performance.
Funder
National Natural Science Foundation of China
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