Large field-of-view pine wilt disease tree detection based on improved YOLO v4 model with UAV images.

Zhang, Zhenbang; Han, Chongyang; Wang, Xinrong; Li, Haoxin; Li, Jie; Zeng, Jinbin; Sun, Si; Wu, Weibin

Zhang, Zhenbang; Han, Chongyang; Wang, Xinrong; Li, Haoxin; Li, Jie; Zeng, Jinbin; Sun, Si; Wu, Weibin.

Afiliación

Zhang Z; College of Engineering, South China Agricultural University, Guangzhou, China.
Han C; Guangdong Provincial Key Laboratory of Utilization and Conservation of Food and Medicinal Resources in Northern Region, Shaoguan University, Shaoguan, China.
Wang X; College of Intelligent Engineering, Shaoguan University, Shaoguan, China.
Li H; College of Engineering, South China Agricultural University, Guangzhou, China.
Li J; College of Plant Protection, South China Agricultural University, Guangzhou, China.
Zeng J; College of Engineering, South China Agricultural University, Guangzhou, China.
Sun S; College of Artificial Intelligence, Nankai University, Tianjin, China.
Wu W; College of Engineering, South China Agricultural University, Guangzhou, China.

Front Plant Sci ; 15: 1381367, 2024.

Article en En | MEDLINE | ID: mdl-38966144

ABSTRACT

ABSTRACT

Introduction:

Pine wilt disease spreads rapidly, leading to the death of a large number of pine trees. Exploring the corresponding prevention and control measures for different stages of pine wilt disease is of great significance for its prevention and control.

Methods:

To address the issue of rapid detection of pine wilt in a large field of view, we used a drone to collect multiple sets of diseased tree samples at different times of the year, which made the model trained by deep learning more generalizable. This research improved the YOLO v4(You Only Look Once version 4) network for detecting pine wilt disease, and the channel attention mechanism module was used to improve the learning ability of the neural network.

Results:

The ablation experiment found that adding the attention mechanism SENet module combined with the self-designed feature enhancement module based on the feature pyramid had the best improvement effect, and the mAP of the improved model was 79.91%.

Discussion:

Comparing the improved YOLO v4 model with SSD, Faster RCNN, YOLO v3, and YOLO v5, it was found that the mAP of the improved YOLO v4 model was significantly higher than the other four models, which provided an efficient solution for intelligent diagnosis of pine wood nematode disease. The improved YOLO v4 model enables precise location and identification of pine wilt trees under changing light conditions. Deployment of the model on a UAV enables large-scale detection of pine wilt disease and helps to solve the challenges of rapid detection and prevention of pine wilt disease.

Palabras clave

UAV images; deep learning; large field-of-view; pine wilt disease; target detection

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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: Front Plant Sci Año: 2024 Tipo del documento: Article País de afiliación: China Pais de publicación: Suiza

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