YOLO11m-SCFPose: An Improved Detection Framework for Keypoint Extraction in Cucumber Fruit Phenotyping
To address the issues of low efficiency and large errors in traditional manual cucumber fruit phenotyping methods, this paper proposes the application of keypoint detection technology for cucumber phenotyping and designs an improved lightweight model called YOLO11m-SCFPose. Based on YOLO11m-pose, th...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-07-01
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| Series: | Horticulturae |
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| Online Access: | https://www.mdpi.com/2311-7524/11/7/858 |
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| _version_ | 1849406518793863168 |
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| author | Huijiao Yu Xuehui Zhang Jun Yan Xianyong Meng |
| author_facet | Huijiao Yu Xuehui Zhang Jun Yan Xianyong Meng |
| author_sort | Huijiao Yu |
| collection | DOAJ |
| description | To address the issues of low efficiency and large errors in traditional manual cucumber fruit phenotyping methods, this paper proposes the application of keypoint detection technology for cucumber phenotyping and designs an improved lightweight model called YOLO11m-SCFPose. Based on YOLO11m-pose, the original backbone network is replaced with the lightweight StarNet-S1 backbone, reducing model complexity. Additionally, an improved C3K2_PartialConv neck module is used to enhance information interaction and fusion among multi-scale features while maintaining computational efficiency. The Focaler-IoU loss function is employed to improve keypoint localization accuracy. Results show that the improved model achieves an mAP50-95 of 0.924, with a floating-point operation count (GFLOPs) of 32.1, and reduces the model size to 1.229 × 10<sup>7</sup> parameters. This model demonstrates better computational efficiency and lower resource consumption, providing an effective lightweight solution for crop phenotypic analysis. |
| format | Article |
| id | doaj-art-b62f38c67fa64bd1913999150e81fffa |
| institution | Kabale University |
| issn | 2311-7524 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Horticulturae |
| spelling | doaj-art-b62f38c67fa64bd1913999150e81fffa2025-08-20T03:36:21ZengMDPI AGHorticulturae2311-75242025-07-0111785810.3390/horticulturae11070858YOLO11m-SCFPose: An Improved Detection Framework for Keypoint Extraction in Cucumber Fruit PhenotypingHuijiao Yu0Xuehui Zhang1Jun Yan2Xianyong Meng3College of Information Science and Engineering, Shandong Agricultural University, Tai’an 271000, ChinaCollege of Information Science and Engineering, Shandong Agricultural University, Tai’an 271000, ChinaCollege of Information Science and Engineering, Shandong Agricultural University, Tai’an 271000, ChinaCollege of Information Science and Engineering, Shandong Agricultural University, Tai’an 271000, ChinaTo address the issues of low efficiency and large errors in traditional manual cucumber fruit phenotyping methods, this paper proposes the application of keypoint detection technology for cucumber phenotyping and designs an improved lightweight model called YOLO11m-SCFPose. Based on YOLO11m-pose, the original backbone network is replaced with the lightweight StarNet-S1 backbone, reducing model complexity. Additionally, an improved C3K2_PartialConv neck module is used to enhance information interaction and fusion among multi-scale features while maintaining computational efficiency. The Focaler-IoU loss function is employed to improve keypoint localization accuracy. Results show that the improved model achieves an mAP50-95 of 0.924, with a floating-point operation count (GFLOPs) of 32.1, and reduces the model size to 1.229 × 10<sup>7</sup> parameters. This model demonstrates better computational efficiency and lower resource consumption, providing an effective lightweight solution for crop phenotypic analysis.https://www.mdpi.com/2311-7524/11/7/858cucumber fruit phenotypingkeypoint detectionlightweight modelYOLO11m-SCFPose |
| spellingShingle | Huijiao Yu Xuehui Zhang Jun Yan Xianyong Meng YOLO11m-SCFPose: An Improved Detection Framework for Keypoint Extraction in Cucumber Fruit Phenotyping Horticulturae cucumber fruit phenotyping keypoint detection lightweight model YOLO11m-SCFPose |
| title | YOLO11m-SCFPose: An Improved Detection Framework for Keypoint Extraction in Cucumber Fruit Phenotyping |
| title_full | YOLO11m-SCFPose: An Improved Detection Framework for Keypoint Extraction in Cucumber Fruit Phenotyping |
| title_fullStr | YOLO11m-SCFPose: An Improved Detection Framework for Keypoint Extraction in Cucumber Fruit Phenotyping |
| title_full_unstemmed | YOLO11m-SCFPose: An Improved Detection Framework for Keypoint Extraction in Cucumber Fruit Phenotyping |
| title_short | YOLO11m-SCFPose: An Improved Detection Framework for Keypoint Extraction in Cucumber Fruit Phenotyping |
| title_sort | yolo11m scfpose an improved detection framework for keypoint extraction in cucumber fruit phenotyping |
| topic | cucumber fruit phenotyping keypoint detection lightweight model YOLO11m-SCFPose |
| url | https://www.mdpi.com/2311-7524/11/7/858 |
| work_keys_str_mv | AT huijiaoyu yolo11mscfposeanimproveddetectionframeworkforkeypointextractionincucumberfruitphenotyping AT xuehuizhang yolo11mscfposeanimproveddetectionframeworkforkeypointextractionincucumberfruitphenotyping AT junyan yolo11mscfposeanimproveddetectionframeworkforkeypointextractionincucumberfruitphenotyping AT xianyongmeng yolo11mscfposeanimproveddetectionframeworkforkeypointextractionincucumberfruitphenotyping |