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 |
| Published: |
PAGEPress Publications
2024-06-01
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| Series: | Journal of Agricultural Engineering |
| Subjects: | |
| Online Access: | https://www.agroengineering.org/jae/article/view/1587 |
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| Summary: | 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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| ISSN: | 1974-7071 2239-6268 |