Detecting Individual Plants Infected with Pine Wilt Disease Using Drones and Satellite Imagery: A Case Study in Xianning, China
In recent years, remote sensing techniques such as satellite and drone-based imaging have been used to monitor Pine Wilt Disease (PWD), a widespread forest disease that causes the death of pine species. Researchers have explored the use of remote sensing imagery and deep learning algorithms to impro...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-05-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/15/10/2671 |
| _version_ | 1797598491216707584 |
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| author | Peihua Cai Guanzhou Chen Haobo Yang Xianwei Li Kun Zhu Tong Wang Puyun Liao Mengdi Han Yuanfu Gong Qing Wang Xiaodong Zhang |
| author_facet | Peihua Cai Guanzhou Chen Haobo Yang Xianwei Li Kun Zhu Tong Wang Puyun Liao Mengdi Han Yuanfu Gong Qing Wang Xiaodong Zhang |
| author_sort | Peihua Cai |
| collection | DOAJ |
| description | In recent years, remote sensing techniques such as satellite and drone-based imaging have been used to monitor Pine Wilt Disease (PWD), a widespread forest disease that causes the death of pine species. Researchers have explored the use of remote sensing imagery and deep learning algorithms to improve the accuracy of PWD detection at the single-tree level. This study introduces a novel framework for PWD detection that combines high-resolution RGB drone imagery with free-access Sentinel-2 satellite multi-spectral imagery. The proposed approach includes an PWD-infected tree detection model named YOLOv5-PWD and an effective data augmentation method. To evaluate the proposed framework, we collected data and created a dataset in Xianning City, China, consisting of object detection samples of infected trees at middle and late stages of PWD. Experimental results indicate that the YOLOv5-PWD detection model achieved 1.2% higher mAP compared to the original YOLOv5 model and a further improvement of 1.9% mAP was observed after applying our dataset augmentation method, which demonstrates the effectiveness and potential of the proposed framework for PWD detection. |
| first_indexed | 2024-03-11T03:21:50Z |
| format | Article |
| id | doaj.art-de2c8c9cb0c34154926e550bf2f91290 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-11T03:21:50Z |
| publishDate | 2023-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-de2c8c9cb0c34154926e550bf2f912902023-11-18T03:08:31ZengMDPI AGRemote Sensing2072-42922023-05-011510267110.3390/rs15102671Detecting Individual Plants Infected with Pine Wilt Disease Using Drones and Satellite Imagery: A Case Study in Xianning, ChinaPeihua Cai0Guanzhou Chen1Haobo Yang2Xianwei Li3Kun Zhu4Tong Wang5Puyun Liao6Mengdi Han7Yuanfu Gong8Qing Wang9Xiaodong Zhang10School of Geosciences, Yangtze University, Wuhan 430010, ChinaState Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, ChinaState Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, ChinaState Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, ChinaInstitute of Geospatial Information, PLA Strategic Support Force Information Engineering University, Zhengzhou 450001, ChinaState Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, ChinaState Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, ChinaSchool of Geosciences, Yangtze University, Wuhan 430010, ChinaState Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, ChinaSchool of Geosciences, Yangtze University, Wuhan 430010, ChinaState Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, ChinaIn recent years, remote sensing techniques such as satellite and drone-based imaging have been used to monitor Pine Wilt Disease (PWD), a widespread forest disease that causes the death of pine species. Researchers have explored the use of remote sensing imagery and deep learning algorithms to improve the accuracy of PWD detection at the single-tree level. This study introduces a novel framework for PWD detection that combines high-resolution RGB drone imagery with free-access Sentinel-2 satellite multi-spectral imagery. The proposed approach includes an PWD-infected tree detection model named YOLOv5-PWD and an effective data augmentation method. To evaluate the proposed framework, we collected data and created a dataset in Xianning City, China, consisting of object detection samples of infected trees at middle and late stages of PWD. Experimental results indicate that the YOLOv5-PWD detection model achieved 1.2% higher mAP compared to the original YOLOv5 model and a further improvement of 1.9% mAP was observed after applying our dataset augmentation method, which demonstrates the effectiveness and potential of the proposed framework for PWD detection.https://www.mdpi.com/2072-4292/15/10/2671pine wilt disease (PWD)YOLOv5-PWDdeep learningremote sensingsingle-tree level detectionobject detection |
| spellingShingle | Peihua Cai Guanzhou Chen Haobo Yang Xianwei Li Kun Zhu Tong Wang Puyun Liao Mengdi Han Yuanfu Gong Qing Wang Xiaodong Zhang Detecting Individual Plants Infected with Pine Wilt Disease Using Drones and Satellite Imagery: A Case Study in Xianning, China Remote Sensing pine wilt disease (PWD) YOLOv5-PWD deep learning remote sensing single-tree level detection object detection |
| title | Detecting Individual Plants Infected with Pine Wilt Disease Using Drones and Satellite Imagery: A Case Study in Xianning, China |
| title_full | Detecting Individual Plants Infected with Pine Wilt Disease Using Drones and Satellite Imagery: A Case Study in Xianning, China |
| title_fullStr | Detecting Individual Plants Infected with Pine Wilt Disease Using Drones and Satellite Imagery: A Case Study in Xianning, China |
| title_full_unstemmed | Detecting Individual Plants Infected with Pine Wilt Disease Using Drones and Satellite Imagery: A Case Study in Xianning, China |
| title_short | Detecting Individual Plants Infected with Pine Wilt Disease Using Drones and Satellite Imagery: A Case Study in Xianning, China |
| title_sort | detecting individual plants infected with pine wilt disease using drones and satellite imagery a case study in xianning china |
| topic | pine wilt disease (PWD) YOLOv5-PWD deep learning remote sensing single-tree level detection object detection |
| url | https://www.mdpi.com/2072-4292/15/10/2671 |
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