Development of a Premium Tea-Picking Robot Incorporating Deep Learning and Computer Vision for Leaf Detection
文献类型: 外文期刊
作者: Wu, Luofa 1 ; Liu, Helai 1 ; Ye, Chun 1 ; Wu, Yanqi 2 ;
作者机构: 1.Jiangxi Acad Agr Sci, Inst Agr Engn, Nanchang 330200, Peoples R China
2.Eindhoven Univ Technol, Dept Elect Engn, NL-5600 MB Eindhoven, Netherlands
关键词: premium tea; picking robot; deep learning; visual recognition
期刊名称:APPLIED SCIENCES-BASEL ( 影响因子:2.5; 五年影响因子:2.7 )
ISSN:
年卷期: 2024 年 14 卷 13 期
页码:
收录情况: SCI
摘要: Premium tea holds a significant place in Chinese tea culture, enjoying immense popularity among domestic consumers and an esteemed reputation in the international market, thereby significantly impacting the Chinese economy. To tackle challenges associated with the labor-intensive and inefficient manual picking process of premium tea, and to elevate the competitiveness of the premium tea sector, our research team has developed and rigorously tested a premium tea-picking robot that harnesses deep learning and computer vision for precise leaf recognition. This innovative technology has been patented by the China National Intellectual Property Administration (ZL202111236676.7). In our study, we constructed a deep-learning model that, through comprehensive data training, enabled the robot to accurately recognize tea buds. By integrating computer vision techniques, we achieved exact positioning of the tea buds. From a hardware perspective, we employed a high-performance robotic arm to ensure stable and efficient picking operations even in complex environments. During the experimental phase, we conducted detailed validations on the practical application of the YOLOv8 algorithm in tea bud identification. When compared to the YOLOv5 algorithm, YOLOv8 exhibited superior accuracy and reliability. Furthermore, we performed comprehensive testing on the path planning for the picking robotic arm, evaluating various algorithms to determine the most effective path planning approach for the picking process. Ultimately, we conducted field tests to assess the robot's performance. The results indicated a 62.02% success rate for the entire picking process of the premium tea-picking robot, with an average picking time of approximately 1.86 s per qualified tea bud. This study provides a solid foundation for further research, development, and deployment of premium tea-picking robots, serving as a valuable reference for the design of other crop-picking robots as well.
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