https://doi.org/10.4081/jae.2024.1560

Distinguishing tea stalks of Wuyuan green tea using hyperspectral imaging analysis and convolutional neural network

Vol. 55 No. 2 (2024)
Published: 16 February 2024
Convolutional neural network, green tea, hyperspectral image, principal component analysis, tea stalks
Abstract Views: 192
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Authors

Xin Yu
College of Mechanical and Automotive Engineering, Liaocheng University, China.
Ling Zhao
zhaoling@lcu.edu.cn
College of Mechanical and Automotive Engineering, Liaocheng University, China.
Zongbin Liu
College of Mechanical and Automotive Engineering, Liaocheng University, China.
Yiqing Zhang
College of Mechanical and Automotive Engineering, Liaocheng University, China.

A well-known agricultural product in China, Wuyuan green tea is safeguarded by national geographical indications. In addition, the processed green tea must be free of contaminants like stones and tea stalks. Nevertheless, due to their similar colors, photoelectric sorting and 2D image recognition technologies are unable to distinguish tea stalks from Wuyuan green tea. In order to address the issue of incorrect sorting brought on by similar color matching, this paper uses hyperspectral imaging technology. Using a 400–1000 nm visible and near-infrared camera, green tea with tea stalks was photographed. Furthermore, the hyperspectral image that was gathered was reduced in dimension using principal component analysis. Additionally, tea stalks were successfully identified in hyperspectral images using the convolutional neural network, which can automatically learn the corresponding features and do away with the laborious feature extraction procedure. According to the experiment’s findings, tea stalks can be recognized with an accuracy of 98.53%. The technique can meet the real production requirements and has a high recognition rate. The selection rate is as high as 97.05% following field testing.

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How to Cite

Yu, X., Zhao, L., Liu, Z. and Zhang, Y. (2024) “Distinguishing tea stalks of Wuyuan green tea using hyperspectral imaging analysis and convolutional neural network”, Journal of Agricultural Engineering, 55(2). doi: 10.4081/jae.2024.1560.
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