TBD-Y: Automatic tea bud detection with synergistic object-spatial attention and global-local attention guided feature fusion
Automatic Tea Bud Detection (TBD) is a critical technology in intelligent tea-picking systems. Nevertheless, challenges, such as complex environments and the high visual similarity between tea buds and backgrounds, frequently result in false detection and missed detection, especially for small tea b...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-12-01
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| Series: | Smart Agricultural Technology |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2772375525002990 |
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| author | Zhongyuan Liu Li Zhuo Chunwang Dong Jiafeng Li Yang Li |
| author_facet | Zhongyuan Liu Li Zhuo Chunwang Dong Jiafeng Li Yang Li |
| author_sort | Zhongyuan Liu |
| collection | DOAJ |
| description | Automatic Tea Bud Detection (TBD) is a critical technology in intelligent tea-picking systems. Nevertheless, challenges, such as complex environments and the high visual similarity between tea buds and backgrounds, frequently result in false detection and missed detection, especially for small tea buds. To address these issues, in this paper, an automatic TBD method is proposed, which is built upon the YOLOv11 object detection framework, named TBD-Y. Firstly, a Synergistic Object-Spatial Attention (SOSA) mechanism is proposed, which incorporates the proposed Local Context Attention (LCA) mechanism to enhance the features in both spatial and regional dimensions. It enables the network to focus more on the tea bud regions, and suppress the interference from background noise. Secondly, a Global-local Attention Guided Feature Fusion (GAGFF) strategy is designed. It consists of two branches: one branch enhances low-resolution, high-level features containing rich global semantic information, while the other branch strengthens high-resolution features that preserve low-level visual details. The fusion of these two branches improves the representation capability of the features. The SOSA and GAGFF are integrated into the YOLOv11 framework, constructing three variants of the TBD model with different parameter scales, named TBD-Y-L, TBD-Y-M, and TBD-Y-S. Experimental results on the self-built TBD dataset and the publicly available Global Wheat Head Dataset 2021 (GWHD_2021) demonstrate that the proposed TBD-Y-L outperforms the existing methods, achieving superior detection accuracy. Furthermore, the TBD-Y-S model exhibits improved detection accuracy compared to YOLOv11-L, while maintaining lower model parameters and computational complexity. |
| format | Article |
| id | doaj-art-5d7b5f7a4cd14e2bada610d3bc579450 |
| institution | OA Journals |
| issn | 2772-3755 |
| language | English |
| publishDate | 2025-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Smart Agricultural Technology |
| spelling | doaj-art-5d7b5f7a4cd14e2bada610d3bc5794502025-08-20T02:33:12ZengElsevierSmart Agricultural Technology2772-37552025-12-011210106610.1016/j.atech.2025.101066TBD-Y: Automatic tea bud detection with synergistic object-spatial attention and global-local attention guided feature fusionZhongyuan Liu0Li Zhuo1Chunwang Dong2Jiafeng Li3Yang Li4Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing University of Technology, Beijing 100124, China; Faculty of Information Technology, Beijing University of Technology, Beijing 100124, ChinaBeijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing University of Technology, Beijing 100124, China; Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China; Corresponding authors.Tea Research Institute, Shandong Academy of Agricultural Sciences, Jinan 250100, China; Corresponding authors.Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing University of Technology, Beijing 100124, China; Faculty of Information Technology, Beijing University of Technology, Beijing 100124, ChinaKey Laboratory of Tea Quality and Safety Control, Ministry of Agriculture and Rural Affairs, Tea Research Institute, Chinese Academy of Agricultural Sciences, Hangzhou, ChinaAutomatic Tea Bud Detection (TBD) is a critical technology in intelligent tea-picking systems. Nevertheless, challenges, such as complex environments and the high visual similarity between tea buds and backgrounds, frequently result in false detection and missed detection, especially for small tea buds. To address these issues, in this paper, an automatic TBD method is proposed, which is built upon the YOLOv11 object detection framework, named TBD-Y. Firstly, a Synergistic Object-Spatial Attention (SOSA) mechanism is proposed, which incorporates the proposed Local Context Attention (LCA) mechanism to enhance the features in both spatial and regional dimensions. It enables the network to focus more on the tea bud regions, and suppress the interference from background noise. Secondly, a Global-local Attention Guided Feature Fusion (GAGFF) strategy is designed. It consists of two branches: one branch enhances low-resolution, high-level features containing rich global semantic information, while the other branch strengthens high-resolution features that preserve low-level visual details. The fusion of these two branches improves the representation capability of the features. The SOSA and GAGFF are integrated into the YOLOv11 framework, constructing three variants of the TBD model with different parameter scales, named TBD-Y-L, TBD-Y-M, and TBD-Y-S. Experimental results on the self-built TBD dataset and the publicly available Global Wheat Head Dataset 2021 (GWHD_2021) demonstrate that the proposed TBD-Y-L outperforms the existing methods, achieving superior detection accuracy. Furthermore, the TBD-Y-S model exhibits improved detection accuracy compared to YOLOv11-L, while maintaining lower model parameters and computational complexity.http://www.sciencedirect.com/science/article/pii/S2772375525002990Tea bud detectionYOLOv11Synergistic object-spatial attentionLocal context attentionGlobal-local attention guided feature fusion |
| spellingShingle | Zhongyuan Liu Li Zhuo Chunwang Dong Jiafeng Li Yang Li TBD-Y: Automatic tea bud detection with synergistic object-spatial attention and global-local attention guided feature fusion Smart Agricultural Technology Tea bud detection YOLOv11 Synergistic object-spatial attention Local context attention Global-local attention guided feature fusion |
| title | TBD-Y: Automatic tea bud detection with synergistic object-spatial attention and global-local attention guided feature fusion |
| title_full | TBD-Y: Automatic tea bud detection with synergistic object-spatial attention and global-local attention guided feature fusion |
| title_fullStr | TBD-Y: Automatic tea bud detection with synergistic object-spatial attention and global-local attention guided feature fusion |
| title_full_unstemmed | TBD-Y: Automatic tea bud detection with synergistic object-spatial attention and global-local attention guided feature fusion |
| title_short | TBD-Y: Automatic tea bud detection with synergistic object-spatial attention and global-local attention guided feature fusion |
| title_sort | tbd y automatic tea bud detection with synergistic object spatial attention and global local attention guided feature fusion |
| topic | Tea bud detection YOLOv11 Synergistic object-spatial attention Local context attention Global-local attention guided feature fusion |
| url | http://www.sciencedirect.com/science/article/pii/S2772375525002990 |
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