ReviewFrontiers in nutrition2022
Application of Machine Vision System in Food Detection.
Review in Frontiers in nutrition, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
- Vision-Based Automated Inspection of Box Meals for Food Portion Defects and Foreign Object Detection.Sensors (Basel, Switzerland) · 2026Article
- Detection of Kernel-Level Spoilage Adulteration in Dried Goji Berries Using Zero-Shot Learning and Computer Vision.Foods (Basel, Switzerland) · 2026Article
- Automated Detection of Quality Deviations in Poultry Processing Using Step-Specific YOLOv12 Models.Foods (Basel, Switzerland) · 2026Article
- Mung bean seed classification based on multimodal features and Kepler-optimized stacking ensemble learning model.PloS one · 2026Article
- Insights of freshness phenotype detection for postharvest fruit and vegetables.Plant phenomics (Washington, D.C.) · 2025Review
- Research Progress on Methods for Improving the Stability of Non-Destructive Testing of Agricultural Product Quality.Foods (Basel, Switzerland) · 2024Review
- Prediction of Salicornia europaea L. biomass using a computer vision system to distinguish different salt-tolerant populations.BMC plant biology · 2024Article
- A Candy Defect Detection Method Based on StyleGAN2 and Improved YOLOv7 for Imbalanced Data.Foods (Basel, Switzerland) · 2024Article
- Intelligent System/Equipment for Quality Deterioration Detection of Fresh Food: Recent Advances and Application.Foods (Basel, Switzerland) · 2024Review
- Assessing the Impact of Botanical Origins, Harvest Years, and Geographical Variability on the Physicochemical Quality of Serbian Honey.Foods (Basel, Switzerland) · 2024Article
- Design and Experimentation of a Machine Vision-Based Cucumber Quality Grader.Foods (Basel, Switzerland) · 2024Article
- Feasibility Study of Combining Hyperspectral Imaging with Deep Learning for Chestnut-Quality Detection.Foods (Basel, Switzerland) · 2023Article
- Artificial Intelligence in Food Safety: A Decade Review and Bibliometric Analysis.Foods (Basel, Switzerland) · 2023Review
- The Need for Machines for the Nondestructive Quality Assessment of Potatoes with the Use of Artificial Intelligence Methods and Imaging Techniques.Sensors (Basel, Switzerland) · 2023Review
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Food processing technology is an important part of modern life globally and will undoubtedly play an increasingly significant role in future development of industry. Food quality and safety are societal concerns, and food health is one of the most important aspects of food processing. However, ensuring food quality and safety is a complex process that necessitates huge investments in labor. Currently, machine vision system based image analysis is widely used in the food industry to monitor food quality, greatly assisting researchers and industry in improving food inspection efficiency. Meanwhile, the use of deep learning in machine vision has significantly improved food identification intelligence. This paper reviews the application of machine vision in food detection from the hardware and software of machine vision systems, introduces the current state of research on various forms of machine vision, and provides an outlook on the challenges that machine vision system faces.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.