Development of a Premium Tea-Picking Robot Incorporating Deep Learning and Computer Vision for Leaf Detection
Source abstract
Abstract
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.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Smart Agriculture and AI · Vehicle License Plate Recognition · Robotic Path Planning Algorithms
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Institute of Agricultural Engineering, Jiangxi Academy of Agricultural Sciences, Nanchang 330000, China
Department of Electronic Engineering, Eindhoven University of Technology, 5600 MB Eindhoven, The Netherlands
Funding
Ministry of Agriculture and rural areas · Jiangxi province linkage project
References (31)
Show source-provided references
1. Analysis on the Technical Gap and Key Problems of Tea Picker in China
2. Liu, J., Zhang, C., Hu, R., Zhu, X., and Cai, J. (2019). Aging of agricultural labor force and technical efficiency in tea production: Evidence from Meitan County, China. Sustainability, 11.10.3390/su11226246 ↗
3. Tea picking point detection and location based on Mask-RCNN
4. Yan, L., Wu, K., Lin, J., Xu, X., Zhang, J., Zhao, X., Tayor, J., and Chen, D. (2022). Identification and picking point positioning of tender tea shoots based on MR3P-TS model. Front. Plant Sci., 13.10.3389/fpls.2022.962391 ↗
5. Tender tea shoots recognition and positioning for picking robot using improved YOLO-V3 model10.1109/ACCESS.2019.2958614 ↗
6. Path planning of picking robot for famous tea
7. Research on the application of machine vision in tea autonomous picking
8. Computer vision-based high-quality tea automatic plucking robot using Delta parallel manipulator10.1016/j.compag.2020.105946 ↗
9. Selsiya, M.S., Manusa, K.R., Gopika, S., Kanimozhi, J., Varsheni, K.C., and Mohan, M.S. (2021, January 8–9). Robotic Arm Enabled Automatic Tea Harvester. Proceedings of the 2021 International Conference on Advancements in Electrical, Electronics, Communication, Computing and Automation (ICAECA), Coimbatore, India.
10. A lightweight tea bud detection model based on Yolov510.1016/j.compag.2023.107636 ↗
11. Zhu, Y., Wu, C., Tong, J., Chen, J., He, L., Wang, R., and Jia, J. (2021). Deviation Tolerance Performance Evaluation and Experiment of Picking End Effector for Famous Tea. Agriculture, 11.10.3390/agriculture11020128 ↗
12. System design and experiment of famous and high-quality tea picking robot10.3901/JME.2022.19.012 ↗
13. Efficient detection and picking sequence planning of tea buds in a high-density canopy10.1016/j.compag.2023.108213 ↗
14. Cai, Z., and Vasconcelos, N. (2018, January 18–23). Cascade r-cnn: Delving into high quality object detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.10.1109/CVPR.2018.00644 ↗
15. ERF-YOLO: A YOLO algorithm compatible with fewer parameters and higher accuracy10.1016/j.imavis.2021.104317 ↗
16. LaValle, S. (1998). Rapidly-exploring random trees: A new tool for path planning. Res. Rep. 9811.
17. Path planning of a 6-DOF measuring robot with a direction guidance RRT method10.1016/j.eswa.2023.122057 ↗
18. Kuffner, J.J., and LaValle, S.M. (2000, January 24–28). RRT-connect: An efficient approach to single-query path planning. Proceedings of the 2000 ICRA. Millennium Conference. IEEE International Conference on Robotics and Automation. Symposia Proceedings (Cat. No. 00CH37065), San Francisco, CA, USA.
19. Trajectory planning and control for SCARA manipulator of tea picking robot
20. Sampling-based algorithms for optimal motion planning10.1177/0278364911406761 ↗
21. Experimental Study on High-quality Tea Plucking by Robot
22. A detection method for apple fruits based on color and shape features10.1109/ACCESS.2019.2918313 ↗
23. Comprehensive visual information acquisition for tomato picking robot based on multitask convolutional neural network10.1016/j.biosystemseng.2023.12.017 ↗
24. Recognition and localization of strawberries from 3D binocular cameras for a strawberry picking robot using coupled YOLO/Mask R-CNN
25. A novel end-to-end vision-based architecture for agricultural human-robot collaboration in fruit picking operations10.1016/j.robot.2023.104567 ↗
26. Workspace decomposition based path planning for fruit-picking robot in complex greenhouse environment10.1016/j.compag.2023.108353 ↗
27. Dual-arm cooperation and implementing for robotic harvesting tomato using binocular vision10.1016/j.robot.2019.01.019 ↗
28. Tan, C., Sun, F., Kong, T., Zhang, W., Yang, C., and Liu, C. (2018). A survey on deep transfer learning. Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4–7, 2018, Springer International Publishing. Proceedings Part III 27.10.1007/978-3-030-01424-7_27 ↗
29. Development and field evaluation of a robotic harvesting system for plucking high-quality tea10.1016/j.compag.2023.107659 ↗
30. Research State and Trend of Fruit Picking Robot Manipulator Structure
31. Robotic arm and gripper to pick fallen peaches in orchards10.17660/ActaHortic.2022.1352.77 ↗