Aerial Image Segmentation of Nematode-Affected Pine Trees with U-Net Convolutional Neural Network
文献类型：期刊论文
| 作者 | Shen, Jiankang1; Xu, Qinghua1; Gao, Mingyang1; Ning, Jicai2 ; Jiang, Xiaopeng2; Gao, Meng1
|
| 刊名 | APPLIED SCIENCES-BASEL
 |
| 出版日期 | 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(mgao@ytu.edu.cn) |
| 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. |
| WOS关键词 | WILT DISEASE |
| WOS研究方向 | Chemistry ; Engineering ; Materials Science ; Physics |
| 语种 | 英语 |
| WOS记录号 | WOS:001254655000001 |
| 资助机构 | Key Program of Shandong Natural Science Foundation |
| 源URL | [http://ir.yic.ac.cn/handle/133337/35902]  |
| 专题 | 烟台海岸带研究所_中科院海岸带环境过程与生态修复重点实验室 烟台海岸带研究所_海岸带信息集成与综合管理实验室 |
| 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. |
入库方式： OAI收割
来源：烟台海岸带研究所
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