A detection method for synchronous recognition of string tomatoes and picking points based on keypoint detection
In the greenhouse environment, factors such as variable lighting conditions, the similarity in color between fruit stems and background, and the complex growth posture of string tomatoes lead to low detection accuracy for picking points. This paper proposes a detection method for the synchronous rec...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2025-07-01
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| Series: | Frontiers in Plant Science |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fpls.2025.1614881/full |
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| _version_ | 1839618356021297152 |
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| author | Linqiang Deng Rongting Ma BaoFan Chen Guozhu Song |
| author_facet | Linqiang Deng Rongting Ma BaoFan Chen Guozhu Song |
| author_sort | Linqiang Deng |
| collection | DOAJ |
| description | In the greenhouse environment, factors such as variable lighting conditions, the similarity in color between fruit stems and background, and the complex growth posture of string tomatoes lead to low detection accuracy for picking points. This paper proposes a detection method for the synchronous recognition of tomatoes and their picking points based on keypoint detection. Using YOLOv8n-pose as the baseline model, we constructed the YOLOv8-TP model. To reduce the computational load of the model, we replaced the C2f module in the backbone network with the C2f-OREPA module. To enhance the model’s accuracy and performance, we introduced a PSA mechanism after the backbone network. Additionally, to strengthen the model’s feature extraction capabilities, we incorporated CGAFusion at the end of the Neck, which adaptively emphasizes important features while suppressing less important ones, thereby enhancing feature expressiveness. Experimental results show that the YOLOv8-TP model achieved an accuracy of 89.8% in synchronously recognizing tomatoes and picking points, with an inference speed of 154.7 FPS. The YOLOv8n-pose model achieves an inference speed of 148.6 FPS. Compared to the baseline model, YOLOv8-TP improved precision, mAP@.5, mAP@.5:.95, and F1-score by 0.6%, 1%, 2%, and 1%, respectively, while reducing model complexity by 8.1%. The Euclidean distance error for detecting picking points was less than 25 pixels, and the depth error was less than 3 millimeters. This method demonstrates excellent detection performance and provides a reference model for detecting string tomatoes and their picking points. |
| format | Article |
| id | doaj-art-bcc99f2ea0a64c6098952263a89c4cbc |
| institution | Matheson Library |
| issn | 1664-462X |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Plant Science |
| spelling | doaj-art-bcc99f2ea0a64c6098952263a89c4cbc2025-07-24T05:33:14ZengFrontiers Media S.A.Frontiers in Plant Science1664-462X2025-07-011610.3389/fpls.2025.16148811614881A detection method for synchronous recognition of string tomatoes and picking points based on keypoint detectionLinqiang DengRongting MaBaoFan ChenGuozhu SongIn the greenhouse environment, factors such as variable lighting conditions, the similarity in color between fruit stems and background, and the complex growth posture of string tomatoes lead to low detection accuracy for picking points. This paper proposes a detection method for the synchronous recognition of tomatoes and their picking points based on keypoint detection. Using YOLOv8n-pose as the baseline model, we constructed the YOLOv8-TP model. To reduce the computational load of the model, we replaced the C2f module in the backbone network with the C2f-OREPA module. To enhance the model’s accuracy and performance, we introduced a PSA mechanism after the backbone network. Additionally, to strengthen the model’s feature extraction capabilities, we incorporated CGAFusion at the end of the Neck, which adaptively emphasizes important features while suppressing less important ones, thereby enhancing feature expressiveness. Experimental results show that the YOLOv8-TP model achieved an accuracy of 89.8% in synchronously recognizing tomatoes and picking points, with an inference speed of 154.7 FPS. The YOLOv8n-pose model achieves an inference speed of 148.6 FPS. Compared to the baseline model, YOLOv8-TP improved precision, mAP@.5, mAP@.5:.95, and F1-score by 0.6%, 1%, 2%, and 1%, respectively, while reducing model complexity by 8.1%. The Euclidean distance error for detecting picking points was less than 25 pixels, and the depth error was less than 3 millimeters. This method demonstrates excellent detection performance and provides a reference model for detecting string tomatoes and their picking points.https://www.frontiersin.org/articles/10.3389/fpls.2025.1614881/fullkeypoint detectionYOLOv8string tomatopicking pointPSA mechanism |
| spellingShingle | Linqiang Deng Rongting Ma BaoFan Chen Guozhu Song A detection method for synchronous recognition of string tomatoes and picking points based on keypoint detection Frontiers in Plant Science keypoint detection YOLOv8 string tomato picking point PSA mechanism |
| title | A detection method for synchronous recognition of string tomatoes and picking points based on keypoint detection |
| title_full | A detection method for synchronous recognition of string tomatoes and picking points based on keypoint detection |
| title_fullStr | A detection method for synchronous recognition of string tomatoes and picking points based on keypoint detection |
| title_full_unstemmed | A detection method for synchronous recognition of string tomatoes and picking points based on keypoint detection |
| title_short | A detection method for synchronous recognition of string tomatoes and picking points based on keypoint detection |
| title_sort | detection method for synchronous recognition of string tomatoes and picking points based on keypoint detection |
| topic | keypoint detection YOLOv8 string tomato picking point PSA mechanism |
| url | https://www.frontiersin.org/articles/10.3389/fpls.2025.1614881/full |
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