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Locating Famous Tea’s Picking Point Based on Shi-Tomasi Algorithm

Lei Zhang1,2, Lang Zou1,2, Chuanyu Wu1,2,*, Jianneng Chen1,2, Heping Chen3

1 Faculty of Mechanical Engineering & Automation, Zhejiang Sci-Tech University, Hangzhou, 310018, China
2 Key Laboratory of Transplanting Equipment and Technology of Zhejiang Province, Hangzhou, 310018, China
3 Ingram School of Engineering, Texas State University, San Marcos, Texas, USA

* Corresponding Author: Chuanyu Wu. Email: email

Computers, Materials & Continua 2021, 69(1), 1109-1122.


To address the difficulty of locating the picking point of a tea sprout during the intelligent automatic picking of famous tea, this study proposes a method to obtain information on the picking point on the basis of the Shi-Tomasi algorithm. This method can rapidly identify a tea sprout’s picking point and obtain its coordinates. Images of tea sprouts in a tea garden were collected, and the G-B component of tea sprouts was segmented using the Otsu algorithm. The region of interest was set with the lowest point of its contour as the center. The characteristics of tea buds and branches in the area were extracted, and the Otsu algorithm was used for a second segmentation of tea sprout images. The tea buds were segmented using the improved Zhang algorithm. The branch feature binary image was used to refine the skeleton, and the Shi-Tomasi algorithm was used to detect the corners of the skeleton and calculate and mark the picking points of the shoots. Sixty sets of samples were tested. The test identified 1,042 effective shoots for tender buds, and 887 picking points were marked, with a success rate of 85.12%, thereby verifying the effectiveness of the method and providing a theoretical reference for the visual positioning of the automatic picking of famous tea.


Cite This Article

L. Zhang, L. Zou, C. Wu, J. Chen and H. Chen, "Locating famous tea’s picking point based on shi-tomasi algorithm," Computers, Materials & Continua, vol. 69, no.1, pp. 1109–1122, 2021.

cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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