Pod-pose : an efficient top-down keypoint detection model for fine-grained pod phenotyping in mature soybean
Abstract Background Phenotypic characterization of mature soybean pods is a crucial aspect of breeding programs, yet efficiently obtaining accurate pod phenotypic parameters remains a major challenge. Recent advances in deep learning, particularly in keypoint detection models, have introduced innova...
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| Format: | Article |
| Language: | English |
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BMC
2025-06-01
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| Series: | Plant Methods |
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| Online Access: | https://doi.org/10.1186/s13007-025-01399-0 |
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| _version_ | 1849335787294818304 |
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| author | Fei Liu Hang Liu Qiong Wu Zhongzhi Han Shanchen Pang Shudong Wang Longgang Zhao |
| author_facet | Fei Liu Hang Liu Qiong Wu Zhongzhi Han Shanchen Pang Shudong Wang Longgang Zhao |
| author_sort | Fei Liu |
| collection | DOAJ |
| description | Abstract Background Phenotypic characterization of mature soybean pods is a crucial aspect of breeding programs, yet efficiently obtaining accurate pod phenotypic parameters remains a major challenge. Recent advances in deep learning, particularly in keypoint detection models, have introduced innovative methods for pod phenotype extraction. However, precise identification and analysis of fine-scale phenotypic traits in soybean pods remain challenging in current research. Results We propose Pod-pose, an innovative top-down keypoint detection model for precise soybean pod phenotyping that adapts human pose estimation techniques to plant phenotyping. Specifically, Pod-pose integrates the architectural strengths of various advanced YOLO (You Only Look Once) models through bottleneck structure optimization and positional feature enhancement to achieve superior detection accuracy. Furthermore, we implemented a two-stage detection method augmented with transfer learning, which not only reduces training complexity but also significantly enhances the model's performance. Extensive evaluation of our custom-built dataset demonstrated Pod-Pose's superior performance, with the X variant achieving an Average Precision of 0.912 at an IoU threshold of 0.5 (AP@IoU = 0.5). Notably, four critical pod-related phenotypic traits were successfully quantified: pod length, bending length, curvature, and inflection point width. Conclusions This study establishes Pod-Pose as a viable solution for pod phenotyping, with potential applications in soybean breeding optimization. |
| format | Article |
| id | doaj-art-c2515f2f8a5041008889aa4dc687a561 |
| institution | Kabale University |
| issn | 1746-4811 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | BMC |
| record_format | Article |
| series | Plant Methods |
| spelling | doaj-art-c2515f2f8a5041008889aa4dc687a5612025-08-20T03:45:10ZengBMCPlant Methods1746-48112025-06-0121111710.1186/s13007-025-01399-0Pod-pose : an efficient top-down keypoint detection model for fine-grained pod phenotyping in mature soybeanFei Liu0Hang Liu1Qiong Wu2Zhongzhi Han3Shanchen Pang4Shudong Wang5Longgang Zhao6Qingdao Agricultural UniversityQingdao Agricultural UniversityQingdao Agricultural UniversityQingdao Agricultural UniversityChina University of Petroleum (East China)China University of Petroleum (East China)Qingdao Agricultural UniversityAbstract Background Phenotypic characterization of mature soybean pods is a crucial aspect of breeding programs, yet efficiently obtaining accurate pod phenotypic parameters remains a major challenge. Recent advances in deep learning, particularly in keypoint detection models, have introduced innovative methods for pod phenotype extraction. However, precise identification and analysis of fine-scale phenotypic traits in soybean pods remain challenging in current research. Results We propose Pod-pose, an innovative top-down keypoint detection model for precise soybean pod phenotyping that adapts human pose estimation techniques to plant phenotyping. Specifically, Pod-pose integrates the architectural strengths of various advanced YOLO (You Only Look Once) models through bottleneck structure optimization and positional feature enhancement to achieve superior detection accuracy. Furthermore, we implemented a two-stage detection method augmented with transfer learning, which not only reduces training complexity but also significantly enhances the model's performance. Extensive evaluation of our custom-built dataset demonstrated Pod-Pose's superior performance, with the X variant achieving an Average Precision of 0.912 at an IoU threshold of 0.5 (AP@IoU = 0.5). Notably, four critical pod-related phenotypic traits were successfully quantified: pod length, bending length, curvature, and inflection point width. Conclusions This study establishes Pod-Pose as a viable solution for pod phenotyping, with potential applications in soybean breeding optimization.https://doi.org/10.1186/s13007-025-01399-0Soybean podDeep learningKeypoint detectionDigital breeding |
| spellingShingle | Fei Liu Hang Liu Qiong Wu Zhongzhi Han Shanchen Pang Shudong Wang Longgang Zhao Pod-pose : an efficient top-down keypoint detection model for fine-grained pod phenotyping in mature soybean Plant Methods Soybean pod Deep learning Keypoint detection Digital breeding |
| title | Pod-pose : an efficient top-down keypoint detection model for fine-grained pod phenotyping in mature soybean |
| title_full | Pod-pose : an efficient top-down keypoint detection model for fine-grained pod phenotyping in mature soybean |
| title_fullStr | Pod-pose : an efficient top-down keypoint detection model for fine-grained pod phenotyping in mature soybean |
| title_full_unstemmed | Pod-pose : an efficient top-down keypoint detection model for fine-grained pod phenotyping in mature soybean |
| title_short | Pod-pose : an efficient top-down keypoint detection model for fine-grained pod phenotyping in mature soybean |
| title_sort | pod pose an efficient top down keypoint detection model for fine grained pod phenotyping in mature soybean |
| topic | Soybean pod Deep learning Keypoint detection Digital breeding |
| url | https://doi.org/10.1186/s13007-025-01399-0 |
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