Education, Science, Technology, Innovation and Life
Open Access
Sign In
  1. Home
  2. Journals
  3. Automation and Machine Learning
  4. Vol 6, Issue 2, 2025

A Review of the Application of Machine Vision in Food Quality Inspection

Download as PDF

DOI: 10.23977/autml.2025.060207 | Downloads: 2 | Views: 136

Author(s)

Peng Yin 1

Affiliation(s)

1 School of Electronic and Information Engineering, University of Science and Technology Liaoning, Anshan, China

Corresponding Author

Peng Yin

ABSTRACT

With the rise in consumer demands for food safety and quality, coupled with the development of automation in the food industry, traditional manual inspection has become insufficient. Machine vision technology, with advantages such as non-contact operation and high precision, has become a key detection method. This paper reviews the application of machine vision in food quality inspection, covering core scenarios such as appearance defect detection of fruits and vegetables, packaging integrity recognition, and impurity detection. It analyzes the technical principles and breakthroughs supported by datasets like Food-101 and Fruits-360, and discusses key challenges including complex shape detection, balancing speed and accuracy, and the cost of data labeling. Research shows that technical solutions integrating deep learning, multimodal fusion, and edge computing significantly enhance detection efficiency. In the future, lightweight models, self-supervised learning, and intelligent quality traceability are expected to become important development directions. This paper provides a technical reference for intelligent inspection in the food industry and promotes the deeper application of machine vision in full-chain quality control.

KEYWORDS

Machine Vision; Food Quality Inspection; Appearance Defects; Packaging Integrity; Impurity Detection

CITE THIS PAPER

Peng Yin, A Review of the Application of Machine Vision in Food Quality Inspection. Automation and Machine Learning (2025) Vol. 6: 50-55. DOI: http://dx.doi.org/10.23977/autml.2025.060207.

REFERENCES

[1] Bossard, Lukas, Matthieu Guillaumin, and Luc Van Gool. "Food-101–mining discriminative components with random forests." European conference on computer vision. Cham: Springer International Publishing, 2014.
[2] Redmon J, Farhadi A. Yolov3: An incremental improvement[J]. arxiv preprint arxiv:1804.02767, 2018.
[3] Ronneberger O, Fischer P, Brox T. U-net: Convolutional networks for biomedical image segmentation[C]// International Conference on Medical image computing and computer-assisted intervention. Cham: Springer international publishing, 2015: 234-241.
[4] Vaswani A, Shazeer N, Parmar N, et al. Attention is all you need[J]. Advances in neural information processing systems, 2017, 30.
[5] Liu W, Anguelov D, Erhan D, et al. Ssd: Single shot multibox detector[C]//European conference on computer vision. Cham: Springer International Publishing, 2016: 21-37.
[6] Garcia-Garcia A, Orts-Escolano S, Oprea S, et al. A review on deep learning techniques applied to semantic segmentation[J]. arxiv preprint arxiv:1704.06857, 2017.

Subscription
E-Mail Alert
Most popular papers
Call for Special Issue
Publication Ethics & OA Statement
Plagiarism Policy
Contact Us
Downloads: 4322
Visits: 200469

Sponsors, Associates, and Links

All published work is licensed under a Creative Commons Attribution 4.0 International License.

Copyright © 2016 - 2031 Clausius Scientific Press Inc. All Rights Reserved.