Aerial Image Segmentation of Nematode-Affected Pine Trees with U-Net Convolutional Neural Network

doi:10.3390/app14125087

	Aerial Image Segmentation of Nematode-Affected Pine Trees with U-Net Convolutional Neural Network
	Shen, Jiankang 1; Xu, Qinghua 1; Gao, Mingyang 1; Ning, Jicai2 ; Jiang, Xiaopeng 2; Gao, Meng1
发表期刊	APPLIED SCIENCES-BASEL
	2024-06-01
卷号	14 期号:12 页码:14
关键词	UAV remote sensing pine wood nematode machine learning U-Net
DOI	10.3390/app14125087
通讯作者	Gao, Meng([email protected])
英文摘要	Pine wood nematode disease, commonly referred to as pine wilt, poses a grave threat to forest health, leading to profound ecological and economic impacts. Originating from the pine wood nematode, this disease not only causes the demise of pine trees but also casts a long shadow over the entire forest ecosystem. The accurate identification of infected trees stands as a pivotal initial step in developing effective prevention and control measures for pine wilt. Nevertheless, existing identification methods face challenges in precisely determining the disease status of individual pine trees, impeding early detection and efficient intervention. In this study, we leverage the capabilities of unmanned aerial vehicle (UAV) remote sensing technology and integrate the VGG classical small convolutional kernel network with U-Net to detect diseased pine trees. This cutting-edge approach captures the spatial and characteristic intricacies of infected trees, converting them into high-dimensional features through multiple convolutions within the VGG network. This method significantly reduces the parameter count while enhancing the sensing range. The results obtained from our validation set are remarkably promising, achieving a Mean Intersection over Union (MIoU) of 81.62%, a Mean Pixel Accuracy (MPA) of 85.13%, an Accuracy of 99.13%, and an F1 Score of 88.50%. These figures surpass those obtained using other methods such as ResNet50 and DeepLab v3+. The methodology presented in this research facilitates rapid and accurate monitoring of pine trees infected with nematodes, offering invaluable technical assistance in the prevention and management of pine wilt disease.
资助机构	Key Program of Shandong Natural Science Foundation
收录类别	SCI
语种	英语
关键词[WOS]	WILT DISEASE
研究领域[WOS]	Chemistry ; Engineering ; Materials Science ; Physics
WOS记录号	WOS:001254655000001
引用统计	被引频次：1[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.yic.ac.cn/handle/133337/35903
专题	中国科学院海岸带环境过程与生态修复重点实验室中国科学院海岸带环境过程与生态修复重点实验室_海岸带信息集成与战略规划研究中心
通讯作者	Gao, Meng
作者单位	1.Yantai Univ, Sch Math & Informat Sci, Yantai 264005, Peoples R China 2.Chinese Acad Sci, Yantai Inst Coastal Zone Res, Yantai 264003, Peoples R China
推荐引用方式 GB/T 7714	Shen, Jiankang,Xu, Qinghua,Gao, Mingyang,et al. Aerial Image Segmentation of Nematode-Affected Pine Trees with U-Net Convolutional Neural Network[J]. APPLIED SCIENCES-BASEL,2024,14(12):14.
APA	Shen, Jiankang,Xu, Qinghua,Gao, Mingyang,Ning, Jicai,Jiang, Xiaopeng,&Gao, Meng.(2024).Aerial Image Segmentation of Nematode-Affected Pine Trees with U-Net Convolutional Neural Network.APPLIED SCIENCES-BASEL,14(12),14.
MLA	Shen, Jiankang,et al."Aerial Image Segmentation of Nematode-Affected Pine Trees with U-Net Convolutional Neural Network".APPLIED SCIENCES-BASEL 14.12(2024):14.