A Recognition Method for Marigold Picking Points Based on the Lightweight SCS-YOLO-Seg Model
Accurate identification of picking points remains a critical challenge for automated marigold harvesting, primarily due to complex backgrounds and significant pose variations of the flowers. To overcome this challenge, this study proposes SCS-YOLO-Seg, a novel method based on a lightweight segmentat...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-08-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/25/15/4820 |
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| author | Baojian Ma Zhenghao Wu Yun Ge Bangbang Chen He Zhang Hao Xia Dongyun Wang |
| author_facet | Baojian Ma Zhenghao Wu Yun Ge Bangbang Chen He Zhang Hao Xia Dongyun Wang |
| author_sort | Baojian Ma |
| collection | DOAJ |
| description | Accurate identification of picking points remains a critical challenge for automated marigold harvesting, primarily due to complex backgrounds and significant pose variations of the flowers. To overcome this challenge, this study proposes SCS-YOLO-Seg, a novel method based on a lightweight segmentation model. The approach enhances the baseline YOLOv8n-seg architecture by replacing its backbone with StarNet and introducing C2f-Star, a novel lightweight feature extraction module. These modifications achieve substantial model compression, significantly reducing the model size, parameter count, and computational complexity (GFLOPs). Segmentation efficiency is further optimized through a dual-path collaborative architecture (Seg-Marigold head). Following mask extraction, picking points are determined by intersecting the optimized elliptical mask fitting results with the stem skeleton. Experimental results demonstrate that SCS-YOLO-Seg effectively balances model compression with segmentation performance. Compared to YOLOv8n-seg, it maintains high accuracy while significantly reducing resource requirements, achieving a picking point identification accuracy of 93.36% with an average inference time of 28.66 ms per image. This work provides a robust and efficient solution for vision systems in automated marigold harvesting. |
| format | Article |
| id | doaj-art-1dd5d282005a49509ce32ea8cd1fe6f9 |
| institution | Kabale University |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-1dd5d282005a49509ce32ea8cd1fe6f92025-08-20T04:00:51ZengMDPI AGSensors1424-82202025-08-012515482010.3390/s25154820A Recognition Method for Marigold Picking Points Based on the Lightweight SCS-YOLO-Seg ModelBaojian Ma0Zhenghao Wu1Yun Ge2Bangbang Chen3He Zhang4Hao Xia5Dongyun Wang6Department of Mechanical and Electrical Engineering, Xinjiang Institute of Technology, Aksu 843100, ChinaCollege of Mechanical and Electrical Engineering, Shihezi University, Shihezi 832003, ChinaCollege of Mechanical and Electrical Engineering, Shihezi University, Shihezi 832003, ChinaDepartment of Mechanical and Electrical Engineering, Xinjiang Institute of Technology, Aksu 843100, ChinaCollege of Mechanical and Electrical Engineering, Shihezi University, Shihezi 832003, ChinaCollege of Mechanical and Electrical Engineering, Shihezi University, Shihezi 832003, ChinaCollege of Engineering, Zhejiang Normal University, Jinhua 321004, ChinaAccurate identification of picking points remains a critical challenge for automated marigold harvesting, primarily due to complex backgrounds and significant pose variations of the flowers. To overcome this challenge, this study proposes SCS-YOLO-Seg, a novel method based on a lightweight segmentation model. The approach enhances the baseline YOLOv8n-seg architecture by replacing its backbone with StarNet and introducing C2f-Star, a novel lightweight feature extraction module. These modifications achieve substantial model compression, significantly reducing the model size, parameter count, and computational complexity (GFLOPs). Segmentation efficiency is further optimized through a dual-path collaborative architecture (Seg-Marigold head). Following mask extraction, picking points are determined by intersecting the optimized elliptical mask fitting results with the stem skeleton. Experimental results demonstrate that SCS-YOLO-Seg effectively balances model compression with segmentation performance. Compared to YOLOv8n-seg, it maintains high accuracy while significantly reducing resource requirements, achieving a picking point identification accuracy of 93.36% with an average inference time of 28.66 ms per image. This work provides a robust and efficient solution for vision systems in automated marigold harvesting.https://www.mdpi.com/1424-8220/25/15/4820marigoldsegmentationlightweight modelpicking point recognitionautomated harvesting |
| spellingShingle | Baojian Ma Zhenghao Wu Yun Ge Bangbang Chen He Zhang Hao Xia Dongyun Wang A Recognition Method for Marigold Picking Points Based on the Lightweight SCS-YOLO-Seg Model Sensors marigold segmentation lightweight model picking point recognition automated harvesting |
| title | A Recognition Method for Marigold Picking Points Based on the Lightweight SCS-YOLO-Seg Model |
| title_full | A Recognition Method for Marigold Picking Points Based on the Lightweight SCS-YOLO-Seg Model |
| title_fullStr | A Recognition Method for Marigold Picking Points Based on the Lightweight SCS-YOLO-Seg Model |
| title_full_unstemmed | A Recognition Method for Marigold Picking Points Based on the Lightweight SCS-YOLO-Seg Model |
| title_short | A Recognition Method for Marigold Picking Points Based on the Lightweight SCS-YOLO-Seg Model |
| title_sort | recognition method for marigold picking points based on the lightweight scs yolo seg model |
| topic | marigold segmentation lightweight model picking point recognition automated harvesting |
| url | https://www.mdpi.com/1424-8220/25/15/4820 |
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