A non-destructive methodology for determination of cantaloupe sugar content using machine vision and deep learning.

Journal: Journal of the science of food and agriculture
PMID: 35596652

Abstract

BACKGROUND: To determine the maturity of cantaloupe, measuring the soluble solid content (SSC) as the indicator of sugar content based on the refractometric index is commonly practised. This method, however, is destructive and limited to a small number of samples. In this study, the coupling of a convolutional neural network (CNN) with machine vision was proposed in detecting the SSC of cantaloupe. The cantaloupe images were first acquired under controlled and uncontrolled conditions and subsequently fed to the CNN to predict the class to which each cantaloupe belonged. Four hand-crafted classical machine-learning classifiers were used to compare against the performance of the CNN.

Authors

  • Pauline Ong
    Faculty of Mechanical and Manufacturing Engineering, Universiti Tun Hussein Onn Malaysia, Johor, Malaysia.
  • Suming Chen
    Department of Biomechatronics Engineering, National Taiwan University, Taipei, Taiwan.
  • Chao-Yin Tsai
    Department of Biomechatronics Engineering, National Taiwan University, Taipei, Taiwan.
  • Yi-Jing Wu
    Department of Biomechatronics Engineering, National Taiwan University, Taipei, Taiwan.
  • Yi-Tzu Shen
    Department of Biomechatronics Engineering, National Taiwan University, Taipei, Taiwan.

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