Tea Verification Using Triplet Loss Convolutional Network


  • Kun-Yi Chen Department of Computer Science and Information Engineering, Asia University, Taichung, Taiwan
  • Chi-Yu Chang Department of Computer Science and Information Engineering, Asia University, Taichung, Taiwan
  • Zhi-Ren Tsai Department of Computer Science and Information Engineering, Asia University, Taichung, Taiwan
  • Chun-Ting Lee Department of Psychology, Asia University, Taichung, Taiwan
  • Zon-Yin Shae Department of Computer Science and Information Engineering, Asia University, Taichung, Taiwan




convolutional neural network, tea image classification, tea image verification, triplet loss


To solve tea image classification problems, this study focuses on triplet loss convolutional neural network to classify six high-mountain oolong tea classes. In the experiment, instead of using traditional deep learning training approach for local feature of tea images, an innovative image verification approach is proposed to learn the global feature of tea images by integrating the distributed tea leaves’ features of all tea sub-images and using a majority voting mechanism to do classification. The results show that the proposed approach can work for small sample size dataset and have higher accuracy than normal transfer learning approach. The average accuracy of the proposed approach achieves 99.54%.


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How to Cite

K.-Y. Chen, C.-Y. . Chang, Z.-R. . Tsai, C.-T. . Lee, and Z.-Y. . Shae, “Tea Verification Using Triplet Loss Convolutional Network”, Adv. technol. innov., vol. 6, no. 4, pp. 199–212, Sep. 2021.