Plucking Point and Posture Determination of Tea Buds Based on Deep Learning
Tea is a significant cash crop grown widely around the world. Currently, tea plucking predominantly relies on manual work. However, due to the aging population and increasing labor costs, machine plucking has become an important trend in the tea industry. The determination of the plucking position a...
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| Format: | Article |
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MDPI AG
2025-01-01
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| Series: | Agriculture |
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| Online Access: | https://www.mdpi.com/2077-0472/15/2/144 |
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| author | Chengju Dong Weibin Wu Chongyang Han Zhiheng Zeng Ting Tang Wenwei Liu |
| author_facet | Chengju Dong Weibin Wu Chongyang Han Zhiheng Zeng Ting Tang Wenwei Liu |
| author_sort | Chengju Dong |
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| description | Tea is a significant cash crop grown widely around the world. Currently, tea plucking predominantly relies on manual work. However, due to the aging population and increasing labor costs, machine plucking has become an important trend in the tea industry. The determination of the plucking position and plucking posture is a critical prerequisite for machine plucking tea leaves. In order to improve the accuracy and efficiency of machine plucking tea leaves, a method is presented in this paper to determine the plucking point and plucking posture based on the instance segmentation deep learning network. In this study, tea images in the dataset were first labeled using the Labelme software (version 4.5.13), and then the LDS-YOLOv8-seg model was proposed to identify the tea bud region and plucking area. The plucking points and the central points of the tea bud’s bounding box were calculated and matched as pairs using the nearest point method (NPM) and the point in range method (PIRM) proposed in this study. Finally, the plucking posture was obtained according to the results of the feature points matching. The matching results on the test dataset show that the PIRM has superior performance, with a matching accuracy of 99.229% and an average matching time of 2.363 milliseconds. In addition, failure cases of feature points matching in the plucking posture determination process were also analyzed in this study. The test results show that the plucking position and posture determination method proposed in this paper is feasible for machine plucking tea. |
| format | Article |
| id | doaj-art-b2d035b419ce4f498490962f0a2f45e3 |
| institution | Kabale University |
| issn | 2077-0472 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
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| series | Agriculture |
| spelling | doaj-art-b2d035b419ce4f498490962f0a2f45e32025-01-24T13:15:53ZengMDPI AGAgriculture2077-04722025-01-0115214410.3390/agriculture15020144Plucking Point and Posture Determination of Tea Buds Based on Deep LearningChengju Dong0Weibin Wu1Chongyang Han2Zhiheng Zeng3Ting Tang4Wenwei Liu5College of Engineering, South China Agricultural University, Guangzhou 510642, ChinaCollege of Engineering, South China Agricultural University, Guangzhou 510642, ChinaCollege of Engineering, South China Agricultural University, Guangzhou 510642, ChinaCollege of Engineering, South China Agricultural University, Guangzhou 510642, ChinaCollege of Engineering, South China Agricultural University, Guangzhou 510642, ChinaThe Fifth Electronics Research Institute of Ministry of Industry and Information Technology, Guangzhou 511370, ChinaTea is a significant cash crop grown widely around the world. Currently, tea plucking predominantly relies on manual work. However, due to the aging population and increasing labor costs, machine plucking has become an important trend in the tea industry. The determination of the plucking position and plucking posture is a critical prerequisite for machine plucking tea leaves. In order to improve the accuracy and efficiency of machine plucking tea leaves, a method is presented in this paper to determine the plucking point and plucking posture based on the instance segmentation deep learning network. In this study, tea images in the dataset were first labeled using the Labelme software (version 4.5.13), and then the LDS-YOLOv8-seg model was proposed to identify the tea bud region and plucking area. The plucking points and the central points of the tea bud’s bounding box were calculated and matched as pairs using the nearest point method (NPM) and the point in range method (PIRM) proposed in this study. Finally, the plucking posture was obtained according to the results of the feature points matching. The matching results on the test dataset show that the PIRM has superior performance, with a matching accuracy of 99.229% and an average matching time of 2.363 milliseconds. In addition, failure cases of feature points matching in the plucking posture determination process were also analyzed in this study. The test results show that the plucking position and posture determination method proposed in this paper is feasible for machine plucking tea.https://www.mdpi.com/2077-0472/15/2/144plucking pointplucking posturetea budsdeep learningfeature points matching |
| spellingShingle | Chengju Dong Weibin Wu Chongyang Han Zhiheng Zeng Ting Tang Wenwei Liu Plucking Point and Posture Determination of Tea Buds Based on Deep Learning Agriculture plucking point plucking posture tea buds deep learning feature points matching |
| title | Plucking Point and Posture Determination of Tea Buds Based on Deep Learning |
| title_full | Plucking Point and Posture Determination of Tea Buds Based on Deep Learning |
| title_fullStr | Plucking Point and Posture Determination of Tea Buds Based on Deep Learning |
| title_full_unstemmed | Plucking Point and Posture Determination of Tea Buds Based on Deep Learning |
| title_short | Plucking Point and Posture Determination of Tea Buds Based on Deep Learning |
| title_sort | plucking point and posture determination of tea buds based on deep learning |
| topic | plucking point plucking posture tea buds deep learning feature points matching |
| url | https://www.mdpi.com/2077-0472/15/2/144 |
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