A lightweight segmentation model toward timely processing for identification of pine wood nematode affected trees with UAV
Abstract Pine wood nematode (PWN), a major international quarantined forest pest, has resulted in significant loss to the pine forest resources, posing a serious threat to global forest ecosystems. Quick and accurate identification of trees infected by PWN can lead to earlier intervention in their s...
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| Format: | Article |
| Language: | English |
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BMC
2025-05-01
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| Series: | Plant Methods |
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| Online Access: | https://doi.org/10.1186/s13007-025-01385-6 |
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| author | Qiangjia Wu Meixiang Chen Hao Shi Tongchuan Yi Gang Xu Weijia Wang Ruirui Zhang |
| author_facet | Qiangjia Wu Meixiang Chen Hao Shi Tongchuan Yi Gang Xu Weijia Wang Ruirui Zhang |
| author_sort | Qiangjia Wu |
| collection | DOAJ |
| description | Abstract Pine wood nematode (PWN), a major international quarantined forest pest, has resulted in significant loss to the pine forest resources, posing a serious threat to global forest ecosystems. Quick and accurate identification of trees infected by PWN can lead to earlier intervention in their spread, thereby significantly reducing losses. However, there is a scarcity of algorithm that are both swift and precise. To achieve more rapid and precise segmentation of trees affected by PWN, we proposed a novel lightweight model termed Refined and Deformable Carafe Attention Net (RCANet). The RCANet excels in both accuracy and real-time performance. It has achieved segmentation accuracy that surpasses mainstream segmentation networks, including DeepLabv3 + , Segformer, PSPNet, HrNet, and UNet. The number of parameters in RCANet is only 5.373 million, the segmentation speed reached 83.14 fps. Compared to the baseline model UNet, the IoU of the affected trees class is improved by 5.6%, and the segmentation speed is accelerated by about 90%. A straightforward yet highly effective lightweight structure was proposed, termed Refined VGG. Additionally, we validate the efficacy of several network modules for this task. RCANet addressed the challenges of low accuracy and inadequate real-time capabilities in the identification of PWN-affected pine trees within intricate forest landscapes. which is expected to be deployed on UAVs in the future for real-time recognition to accelerate the identification and localization of affected trees. This work could facilitate the management of PWN. |
| format | Article |
| id | doaj-art-e66ddbaf325e48a8bc5b8f6f638c2deb |
| institution | OA Journals |
| issn | 1746-4811 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | BMC |
| record_format | Article |
| series | Plant Methods |
| spelling | doaj-art-e66ddbaf325e48a8bc5b8f6f638c2deb2025-08-20T02:34:14ZengBMCPlant Methods1746-48112025-05-0121111610.1186/s13007-025-01385-6A lightweight segmentation model toward timely processing for identification of pine wood nematode affected trees with UAVQiangjia Wu0Meixiang Chen1Hao Shi2Tongchuan Yi3Gang Xu4Weijia Wang5Ruirui Zhang6Intelligent Equipment Research Center, Beijing Academy of Agriculture and Forestry SciencesIntelligent Equipment Research Center, Beijing Academy of Agriculture and Forestry SciencesIntelligent Equipment Research Center, Beijing Academy of Agriculture and Forestry SciencesIntelligent Equipment Research Center, Beijing Academy of Agriculture and Forestry SciencesIntelligent Equipment Research Center, Beijing Academy of Agriculture and Forestry SciencesIntelligent Equipment Research Center, Beijing Academy of Agriculture and Forestry SciencesIntelligent Equipment Research Center, Beijing Academy of Agriculture and Forestry SciencesAbstract Pine wood nematode (PWN), a major international quarantined forest pest, has resulted in significant loss to the pine forest resources, posing a serious threat to global forest ecosystems. Quick and accurate identification of trees infected by PWN can lead to earlier intervention in their spread, thereby significantly reducing losses. However, there is a scarcity of algorithm that are both swift and precise. To achieve more rapid and precise segmentation of trees affected by PWN, we proposed a novel lightweight model termed Refined and Deformable Carafe Attention Net (RCANet). The RCANet excels in both accuracy and real-time performance. It has achieved segmentation accuracy that surpasses mainstream segmentation networks, including DeepLabv3 + , Segformer, PSPNet, HrNet, and UNet. The number of parameters in RCANet is only 5.373 million, the segmentation speed reached 83.14 fps. Compared to the baseline model UNet, the IoU of the affected trees class is improved by 5.6%, and the segmentation speed is accelerated by about 90%. A straightforward yet highly effective lightweight structure was proposed, termed Refined VGG. Additionally, we validate the efficacy of several network modules for this task. RCANet addressed the challenges of low accuracy and inadequate real-time capabilities in the identification of PWN-affected pine trees within intricate forest landscapes. which is expected to be deployed on UAVs in the future for real-time recognition to accelerate the identification and localization of affected trees. This work could facilitate the management of PWN.https://doi.org/10.1186/s13007-025-01385-6Pine wood nematodePine wilt diseaseSemantic segmentationLightweight algorithmUAV remote sensing |
| spellingShingle | Qiangjia Wu Meixiang Chen Hao Shi Tongchuan Yi Gang Xu Weijia Wang Ruirui Zhang A lightweight segmentation model toward timely processing for identification of pine wood nematode affected trees with UAV Plant Methods Pine wood nematode Pine wilt disease Semantic segmentation Lightweight algorithm UAV remote sensing |
| title | A lightweight segmentation model toward timely processing for identification of pine wood nematode affected trees with UAV |
| title_full | A lightweight segmentation model toward timely processing for identification of pine wood nematode affected trees with UAV |
| title_fullStr | A lightweight segmentation model toward timely processing for identification of pine wood nematode affected trees with UAV |
| title_full_unstemmed | A lightweight segmentation model toward timely processing for identification of pine wood nematode affected trees with UAV |
| title_short | A lightweight segmentation model toward timely processing for identification of pine wood nematode affected trees with UAV |
| title_sort | lightweight segmentation model toward timely processing for identification of pine wood nematode affected trees with uav |
| topic | Pine wood nematode Pine wilt disease Semantic segmentation Lightweight algorithm UAV remote sensing |
| url | https://doi.org/10.1186/s13007-025-01385-6 |
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