Improved YOLO v5s-based detection method for external defects in potato
文献类型：期刊论文
| 作者 | Li, Xilong4; Wang, Feiyun4; Guo, Yalin4; Liu, Yijun3,4; Lv, Huangzhen2,3,4; Zeng, Fankui1 ; Lv, Chengxu4
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| 刊名 | FRONTIERS IN PLANT SCIENCE
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| 出版日期 | 2025-02-18 |
| 卷号 | 16页码:15 |
| 关键词 | potato external defect object detection YOLO v5s deep learning |
| ISSN号 | 1664-462X |
| DOI | 10.3389/fpls.2025.1527508 |
| 通讯作者 | Zeng, Fankui(zengfk@licp.cas.cn) ; Lv, Chengxu(lvchengxu1026@163.com) |
| 英文摘要 | Currently, potato defect sorting primarily relies on manual labor, which is not only inefficient but also prone to bias. Although automated sorting systems offer a potential solution by integrating potato detection models, real-time performance remains challenging due to the need to balance high accuracy and speed under limited resources. This study presents an enhanced version of the YOLO v5s model, named YOLO v5s-ours, specifically designed for real-time detection of potato defects. By integrating Coordinate Attention (CA), Adaptive Spatial Feature Fusion (ASFF), and Atrous Spatial Pyramid Pooling (ASPP) modules, the model significantly improves detection accuracy while maintaining computational efficiency. The model achieved 82.0% precision, 86.6% recall, 84.3% F1-Score and 85.1% mean average precision across six categories - healthy, greening, sprouting, scab, mechanical damage, and rot - marking improvements of 24.6%, 10.5%, 19.4%, and 13.7%, respectively, over the baseline model. Although memory usage increased from 13.7 MB to 23.3 MB and frame rate slightly decreased to 30.7 fps, the accuracy gains ensure the model's suitability for practical applications. The research provides significant support for the development of automated potato sorting systems, advancing agricultural efficiency, particularly in real-time applications, by overcoming the limitations of traditional methods. |
| 资助项目 | National Potato Industry Technical System Project[CARS-10-P23] |
| WOS研究方向 | Plant Sciences |
| 语种 | 英语 |
| 出版者 | FRONTIERS MEDIA SA |
| 资助机构 | National Potato Industry Technical System Project |
| 源URL | [http://ir.licp.cn/handle/362003/31521]  |
| 专题 | 中国科学院兰州化学物理研究所 |
| 通讯作者 | Zeng, Fankui; Lv, Chengxu |
| 作者单位 | 1.Chinese Acad Sci, Lanzhou Inst Chem Phys, Res Ctr Nat Med & Chem Metrol, Lanzhou, Peoples R China 2.China Natl Packaging & Food Machinery Co Ltd, Beijing, Peoples R China 3.Key Lab Agr Prod Proc Equipment Minist Agr & Rural, Beijing, Peoples R China 4.Chinese Acad Agr Mechanizat Sci Croup Co Ltd, Beijing, Peoples R China |
| 推荐引用方式 GB/T 7714 | Li, Xilong,Wang, Feiyun,Guo, Yalin,et al. Improved YOLO v5s-based detection method for external defects in potato[J]. FRONTIERS IN PLANT SCIENCE,2025,16:15. |
| APA | Li, Xilong.,Wang, Feiyun.,Guo, Yalin.,Liu, Yijun.,Lv, Huangzhen.,...&Lv, Chengxu.(2025).Improved YOLO v5s-based detection method for external defects in potato.FRONTIERS IN PLANT SCIENCE,16,15. |
| MLA | Li, Xilong,et al."Improved YOLO v5s-based detection method for external defects in potato".FRONTIERS IN PLANT SCIENCE 16(2025):15. |
入库方式： OAI收割
来源：兰州化学物理研究所
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