Food Safety Monitoring Based on Machine Vision

DOI

10.2991/978-94-6239-648-7_46How to use a DOI?

Keywords

Machine Vision; Food Safety; Quality Inspection; Defect Detection; Multimodal Integration

Abstract

Food safety has become a global challenge due to the inefficiency and subjectivity of traditional inspection methods. These approaches often rely on destructive sampling and manual visual analysis, which lead to high latency, low scalability, and limited accuracy. Machine vision provides a non-destructive, high-precision, and automated solution for food quality and safety monitoring. By utilizing high-resolution CMOS image sensors, multispectral imaging, and advanced algorithms, machine vision converts optical signals into digital data that can be analyzed in real time. This enables accurate detection of surface defects, color differences, and geometric deviations at the sub-pixel level. Furthermore, integrating machine learning and data-driven models improves adaptability and accuracy under complex environmental conditions. The technology’s combination of speed, consistency, and reliability not only reduces human error but also enhances traceability and production efficiency. Future developments will focus on knowledge distillation, self-supervised learning, and digital twin systems to build an intelligent, end-to-end monitoring framework from farm to table, providing a more transparent and sustainable foundation for global food safety assurance.

Copyright

© 2026 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Pengyu Xie
PY  - 2026
DA  - 2026/04/24
TI  - Food Safety Monitoring Based on Machine Vision
BT  - Proceedings of the International Workshop on Advances in Deep Learning for Image Analysis and Computer Vision (IWADIC 2025)
PB  - Atlantis Press
SP  - 418
EP  - 425
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6239-648-7_46
DO  - 10.2991/978-94-6239-648-7_46
ID  - Xie2026
ER  -