Tea grading, blending, and matching based on computer vision and deep learning.

Journal: Journal of the science of food and agriculture
PMID: 39711109

Abstract

BACKGROUND: Accurate tea blending assessment and sample matching are critical in the tea production process. Traditional methods face efficiency and accuracy challenges, which can be addressed by advances in computer vision and deep learning. This study developed an efficient and non-destructive method for fast tea grading classification, blending ratio evaluation, and sample matching. The method trained a Residual Network (ResNet) model on an enhanced dataset of tea images and used Convolutional Block Attention Module (CBAM) to improve the model's feature-extraction ability.

Authors

  • Jilong Guo
    School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, PR China.
  • Kexin Zhang
    Centre for Automation and Robotics (CAR) UPM-CSIC, Universidad Politécnica de Madrid (UPM), 28006 Madrid, Spain.
  • Selorm Yao-Say Solomon Adade
    College of Ocean Food and Biological Engineering, Jimei University, Xiamen 361021, PR China.
  • Jinsu Lin
    Bama Tea Co., Ltd, Shenzhen, People's Republic of China.
  • Hao Lin
    Yangtze Delta Region Institute (Huzhou), University of Electronic Science and Technology of China, Huzhou, Zhejiang, China.
  • Quansheng Chen
    School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, PR China.

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