Literature DB >> 24689735

Applications of Computer Vision for Assessing Quality of Agri-food Products: A Review of Recent Research Advances.

Ji Ma1, Da-Wen Sun1,2, Jia-Huan Qu1, Dan Liu1, Hongbin Pu1, Wen-Hong Gao1, Xin-An Zeng1.   

Abstract

With consumer concerns increasing over food quality and safety, the food industry has begun to pay much more attention to the development of rapid and reliable food-evaluation systems over the years. As a result, there is a great need for manufacturers and retailers to operate effective real-time assessments for food quality and safety during food production and processing. Computer vision, comprising a nondestructive assessment approach, has the aptitude to estimate the characteristics of food products with its advantages of fast speed, ease of use, and minimal sample preparation. Specifically, computer vision systems are feasible for classifying food products into specific grades, detecting defects, and estimating properties such as color, shape, size, surface defects, and contamination. Therefore, in order to track the latest research developments of this technology in the agri-food industry, this review aims to present the fundamentals and instrumentation of computer vision systems with details of applications in quality assessment of agri-food products from 2007 to 2013 and also discuss its future trends in combination with spectroscopy.

Keywords:  3D; Computer vision; agri-food products; applications; hyperspectral imaging; image processing; quality and safety; sonar

Mesh:

Year:  2016        PMID: 24689735     DOI: 10.1080/10408398.2013.873885

Source DB:  PubMed          Journal:  Crit Rev Food Sci Nutr        ISSN: 1040-8398            Impact factor:   11.176


  4 in total

1.  Recognition of Mould Colony on Unhulled Paddy Based on Computer Vision using Conventional Machine-learning and Deep Learning Techniques.

Authors:  Ke Sun; Zhengjie Wang; Kang Tu; Shaojin Wang; Leiqing Pan
Journal:  Sci Rep       Date:  2016-11-29       Impact factor: 4.379

Review 2.  Improvement strategies of food supply chain through novel food processing technologies during COVID-19 pandemic.

Authors:  Bimal Chitrakar; Min Zhang; Bhesh Bhandari
Journal:  Food Control       Date:  2021-02-27       Impact factor: 5.548

3.  Nondestructive Detection of Codling Moth Infestation in Apples Using Pixel-Based NIR Hyperspectral Imaging with Machine Learning and Feature Selection.

Authors:  Nader Ekramirad; Alfadhl Y Khaled; Lauren E Doyle; Julia R Loeb; Kevin D Donohue; Raul T Villanueva; Akinbode A Adedeji
Journal:  Foods       Date:  2021-12-21

Review 4.  A Review on Meat Quality Evaluation Methods Based on Non-Destructive Computer Vision and Artificial Intelligence Technologies.

Authors:  Yinyan Shi; Xiaochan Wang; Md Saidul Borhan; Jennifer Young; David Newman; Eric Berg; Xin Sun
Journal:  Food Sci Anim Resour       Date:  2021-07-01
  4 in total

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