Classification of oolong tea varieties based on computer vision and convolutional neural networks.

Journal: Journal of the science of food and agriculture
Published Date: Feb 1, 2024

Abstract

BACKGROUND: In the contemporary food industry, accurate and rapid differentiation of oolong tea varieties holds paramount importance for traceability and quality control. However, achieving this remains a formidable challenge. This study addresses this lacuna by employing machine learning algorithms - namely support vector machines (SVMs) and convolutional neural networks (CNNs) - alongside computer vision techniques for the automated classification of oolong tea leaves based on visual attributes.

Authors

  • Yiwen Zhu
    Department of Computer Science and Engineering, East China University of Science and Technology, Shanghai 200237, PR China.
  • Siyuan Chen
    First author: Department of Computer Science, Columbia University in the City of New York, 10027; second, fourth, and sixth authors: Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY 14853; third author: Department of Mechanical Engineering, Columbia University; fifth author: Uber AI Labs, San Francisco 94103; seventh author: Plant Pathology and Plant-Microbe Biology Section, School of Integrative Plant Science, Cornell University; and eighth author: Department of Mechanical Engineering and Institute of Data Science, Columbia University.
  • Hanzhe Yin
    Department of Food Science & Technology, School of Agriculture & Biology, Shanghai Jiao Tong University, Shanghai, China.
  • Xihao Han
    Department of Food Science & Technology, School of Agriculture & Biology, Shanghai Jiao Tong University, Shanghai, China.
  • Menghan Xu
    Department of Food Science & Technology, School of Agriculture & Biology, Shanghai Jiao Tong University, Shanghai, China.
  • Wenli Wang
    Department of Food Science & Technology, School of Agriculture & Biology, Shanghai Jiao Tong University, Shanghai 200240, China. Electronic address: wenli-wang@sjtu.edu.cn.
  • Yin Zhang
    Department of Radiation Oncology, Rutgers-Cancer Institute of New Jersey, Rutgers-Robert Wood Johnson Medical School, New Brunswick, NJ, United States.
  • Xiaoxiao Feng
    Department of Food Science & Technology, School of Agriculture & Biology, Shanghai Jiao Tong University, Shanghai, China.
  • Yuan Liu
    Department of General Surgery, Wuxi People's Hospital Affiliated to Nanjing Medical University, Wuxi, China.

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