Detection of surface defects in soybean seeds based on improved Yolov9
Abstract As one of the important indicators of soybean seed quality identification, the appearance of soybeans has always been of great concern to people, and in traditional detection, it is mainly through the naked eye to check whether there are defects on its surface. The field of machine learning...
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Nature Portfolio
2025-04-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-92429-3 |
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| author | Chuanming Liu Yifan Shen Feng Mu Haixia Long Anas Bilal Xia Yu Qi Dai |
| author_facet | Chuanming Liu Yifan Shen Feng Mu Haixia Long Anas Bilal Xia Yu Qi Dai |
| author_sort | Chuanming Liu |
| collection | DOAJ |
| description | Abstract As one of the important indicators of soybean seed quality identification, the appearance of soybeans has always been of great concern to people, and in traditional detection, it is mainly through the naked eye to check whether there are defects on its surface. The field of machine learning, particularly deep learning technology, has undergone rapid advancements and development, making it possible to detect the defects of soybean seeds using deep learning technology. This method can effectively replace the traditional detection methods in the past and reduce the human resources consumption in this work, leading to decreased expenses associated with agricultural activities. In this paper, we propose a Yolov9-c-ghost-Forward model improved by introducing GhostConv, a lightweight convolutional module in GhostNet, which enhances the recognition of soybean seed images through grayscale conversion, filtering processing, image segmentation, morphological operations, etc. and greatly reduces the noise in them, to separate the soybean seeds from the original images. Based on the Yolov9 network, the soybean seed features are extracted, and the defects of soybean seeds are detected. Based on the experiments’ findings, the recall rate can reach 98.6%, and the mAP0.5 can reach 99.2%. This shows that the model can provide a solid theoretical foundation and technical support for agricultural breeding screening and agricultural development. |
| format | Article |
| id | doaj-art-ffc82fa6fd5b4161b18942f1428ef78c |
| institution | OA Journals |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Nature Portfolio |
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| series | Scientific Reports |
| spelling | doaj-art-ffc82fa6fd5b4161b18942f1428ef78c2025-08-20T02:12:01ZengNature PortfolioScientific Reports2045-23222025-04-0115112110.1038/s41598-025-92429-3Detection of surface defects in soybean seeds based on improved Yolov9Chuanming Liu0Yifan Shen1Feng Mu2Haixia Long3Anas Bilal4Xia Yu5Qi Dai6Key Laboratory of Data Science and Smart Education, Hainan Normal University, Ministry of EducationKey Laboratory of Data Science and Smart Education, Hainan Normal University, Ministry of EducationKey Laboratory of Data Science and Smart Education, Hainan Normal University, Ministry of EducationKey Laboratory of Data Science and Smart Education, Hainan Normal University, Ministry of EducationKey Laboratory of Data Science and Smart Education, Hainan Normal University, Ministry of EducationKey Laboratory of Data Science and Smart Education, Hainan Normal University, Ministry of EducationAffiliation College of Life Science and Medicine, Zhejiang Sci-Tech UniversityAbstract As one of the important indicators of soybean seed quality identification, the appearance of soybeans has always been of great concern to people, and in traditional detection, it is mainly through the naked eye to check whether there are defects on its surface. The field of machine learning, particularly deep learning technology, has undergone rapid advancements and development, making it possible to detect the defects of soybean seeds using deep learning technology. This method can effectively replace the traditional detection methods in the past and reduce the human resources consumption in this work, leading to decreased expenses associated with agricultural activities. In this paper, we propose a Yolov9-c-ghost-Forward model improved by introducing GhostConv, a lightweight convolutional module in GhostNet, which enhances the recognition of soybean seed images through grayscale conversion, filtering processing, image segmentation, morphological operations, etc. and greatly reduces the noise in them, to separate the soybean seeds from the original images. Based on the Yolov9 network, the soybean seed features are extracted, and the defects of soybean seeds are detected. Based on the experiments’ findings, the recall rate can reach 98.6%, and the mAP0.5 can reach 99.2%. This shows that the model can provide a solid theoretical foundation and technical support for agricultural breeding screening and agricultural development.https://doi.org/10.1038/s41598-025-92429-3Yolov9Soybean seedsComputer visionSurface defect detection |
| spellingShingle | Chuanming Liu Yifan Shen Feng Mu Haixia Long Anas Bilal Xia Yu Qi Dai Detection of surface defects in soybean seeds based on improved Yolov9 Scientific Reports Yolov9 Soybean seeds Computer vision Surface defect detection |
| title | Detection of surface defects in soybean seeds based on improved Yolov9 |
| title_full | Detection of surface defects in soybean seeds based on improved Yolov9 |
| title_fullStr | Detection of surface defects in soybean seeds based on improved Yolov9 |
| title_full_unstemmed | Detection of surface defects in soybean seeds based on improved Yolov9 |
| title_short | Detection of surface defects in soybean seeds based on improved Yolov9 |
| title_sort | detection of surface defects in soybean seeds based on improved yolov9 |
| topic | Yolov9 Soybean seeds Computer vision Surface defect detection |
| url | https://doi.org/10.1038/s41598-025-92429-3 |
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