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April 10, 2026Journal of Food Process Engineering0 citations

Improved CycleGAN Based Quality Level Identification of Tea

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XLXiaohui LuYWYì WángCZCheng Zhang

Key Points

  • The aim is to develop a computer vision method for accurately identifying tea quality levels using deep learning techniques.
  • Utilized CycleGAN deep learning architecture for image processing.
  • Analyzed complex characteristics of various Chinese tea types.
  • Evaluated the system's performance through accurate identification of Longjing green tea grades.
  • The system accurately distinguishes different grades of Longjing green tea.
  • Results indicate significant improvement in quality evaluation compared to traditional methods.

Abstract

ABSTRACT Chinese tea culture has a long history, and tea tasting is a relaxation and a kind of art philosophy. Thus, they have a set of quality evaluation system since ancient times. There are many varieties and brands of Chinese tea. It is relatively difficult to identify tea quality without a professional analyzer. To this end, we propose a quality identification method by computer vision based on deep learning methods. The method can help to analyze the complex characteristics of tea. The results show that the proposed system is capable of distinguishing the Longjing green tea grades accurately. Our findings will assist people to achieve intelligent inspecting and grading of tea by vision.

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Cite This Study

Lu et al. (2026) studied this question.

synapsesocial.com/papers/69d895d86c1944d70ce06fd0https://doi.org/10.1111/jfpe.70482
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