Detection method of potato leaf disease based on YOLOv5s
An improved leaf target detection method based on the YOLOv5s network is proposed to address the issues of low model detection accuracy and slow detection speed in potato leaf image target detection. Firstly, a deformable convolution replaces the standard convolution in YOLOv5s to ensure that the c...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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PAGEPress Publications
2024-06-01
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| Series: | Journal of Agricultural Engineering |
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| Online Access: | https://www.agroengineering.org/jae/article/view/1587 |
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| _version_ | 1850149213432709120 |
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| author | Jingtao Li Hao Chen Guisong Li Yueqi Liu Yanli Yang Xia Liu Chang Yi Wang |
| author_facet | Jingtao Li Hao Chen Guisong Li Yueqi Liu Yanli Yang Xia Liu Chang Yi Wang |
| author_sort | Jingtao Li |
| collection | DOAJ |
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An improved leaf target detection method based on the YOLOv5s network is proposed to address the issues of low model detection accuracy and slow detection speed in potato leaf image target detection. Firstly, a deformable convolution replaces the standard convolution in YOLOv5s to ensure that the convolution region always covers the target region. Secondly, CBAM attention module is introduced into the convolutional module to enhance local feature extraction and fusion capability of the network, while WIoU_Loss serves as Bounding box loss function SRN-DeblurNet deblurnet is combined with YOLOv5s network to convert part of fuzzy images into clear ones before being integrated with multi-scale features for model prediction. To verify its effectiveness, we trained our model using Pytorch deep learning framework and achieved an accuracy rate of 90.3% and recall rate of 88%, which are respectively 8.6% and 8.9% higher than those obtained by YOLOv5s.
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| format | Article |
| id | doaj-art-8967a0f185df41e39ccda8f36dc73ae4 |
| institution | OA Journals |
| issn | 1974-7071 2239-6268 |
| language | English |
| publishDate | 2024-06-01 |
| publisher | PAGEPress Publications |
| record_format | Article |
| series | Journal of Agricultural Engineering |
| spelling | doaj-art-8967a0f185df41e39ccda8f36dc73ae42025-08-20T02:26:59ZengPAGEPress PublicationsJournal of Agricultural Engineering1974-70712239-62682024-06-01553Detection method of potato leaf disease based on YOLOv5sJingtao Li0Hao Chen1Guisong Li2Yueqi Liu3Yanli Yang4Xia Liu5Chang Yi Wang6School of Information Engineering and Automation, Kunming University of Science and TechnologySchool of Information Engineering and Automation, Kunming University of Science and TechnologySchool of Information Engineering and Automation, Kunming University of Science and TechnologySchool of Information Engineering and Automation, Kunming University of Science and TechnologyCollege of Plant Protection, Yunnan Agricultural University, KunmingCollege of Plant Protection, Yunnan Agricultural University, KunmingSchool of Animation and Digital Arts, Communication University of China, Beijing An improved leaf target detection method based on the YOLOv5s network is proposed to address the issues of low model detection accuracy and slow detection speed in potato leaf image target detection. Firstly, a deformable convolution replaces the standard convolution in YOLOv5s to ensure that the convolution region always covers the target region. Secondly, CBAM attention module is introduced into the convolutional module to enhance local feature extraction and fusion capability of the network, while WIoU_Loss serves as Bounding box loss function SRN-DeblurNet deblurnet is combined with YOLOv5s network to convert part of fuzzy images into clear ones before being integrated with multi-scale features for model prediction. To verify its effectiveness, we trained our model using Pytorch deep learning framework and achieved an accuracy rate of 90.3% and recall rate of 88%, which are respectively 8.6% and 8.9% higher than those obtained by YOLOv5s. https://www.agroengineering.org/jae/article/view/1587Potato leaf diseasetargeted detectionYOLOv5sdeformable convolution |
| spellingShingle | Jingtao Li Hao Chen Guisong Li Yueqi Liu Yanli Yang Xia Liu Chang Yi Wang Detection method of potato leaf disease based on YOLOv5s Journal of Agricultural Engineering Potato leaf disease targeted detection YOLOv5s deformable convolution |
| title | Detection method of potato leaf disease based on YOLOv5s |
| title_full | Detection method of potato leaf disease based on YOLOv5s |
| title_fullStr | Detection method of potato leaf disease based on YOLOv5s |
| title_full_unstemmed | Detection method of potato leaf disease based on YOLOv5s |
| title_short | Detection method of potato leaf disease based on YOLOv5s |
| title_sort | detection method of potato leaf disease based on yolov5s |
| topic | Potato leaf disease targeted detection YOLOv5s deformable convolution |
| url | https://www.agroengineering.org/jae/article/view/1587 |
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