Application of Machine Vision System in Food Detection

Xiao, Zhifei; Wang, Jilai; Han, Lu; Guo, Shubiao; Cui, Qinghao

doi:10.3389/fnut.2022.888245

Cited by 28 publications

(22 citation statements)

References 52 publications

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“…DL architectures provide critical tools for crop disease prediction and automated agricultural farm monitoring [ 144 , 149 ]. Xiao et al (2022) introduced the application of DL in food detection systems from hardware to software [ 151 ]. Liu et al (2021) introduced a CNN model to feature extractors for complex food, such as cereals, meat and aquatic products, fruits, and vegetables [ 145 ].…”

Section: Abstract and Hot Spotsmentioning

confidence: 99%

Artificial Intelligence in Food Safety: A Decade Review and Bibliometric Analysis

Liu

Wang

Zhang

et al. 2023

Foods

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Artificial Intelligence (AI) technologies have been powerful solutions used to improve food yield, quality, and nutrition, increase safety and traceability while decreasing resource consumption, and eliminate food waste. Compared with several qualitative reviews on AI in food safety, we conducted an in-depth quantitative and systematic review based on the Core Collection database of WoS (Web of Science). To discover the historical trajectory and identify future trends, we analysed the literature concerning AI technologies in food safety from 2012 to 2022 by CiteSpace. In this review, we used bibliometric methods to describe the development of AI in food safety, including performance analysis, science mapping, and network analysis by CiteSpace. Among the 1855 selected articles, China and the United States contributed the most literature, and the Chinese Academy of Sciences released the largest number of relevant articles. Among all the journals in this field, PLoS ONE and Computers and Electronics in Agriculture ranked first and second in terms of annual publications and co-citation frequency. The present character, hot spots, and future research trends of AI technologies in food safety research were determined. Furthermore, based on our analyses, we provide researchers, practitioners, and policymakers with the big picture of research on AI in food safety across the whole process, from precision agriculture to precision nutrition, through 28 enlightening articles.

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Section: Abstract and Hot Spotsmentioning

confidence: 99%

Artificial Intelligence in Food Safety: A Decade Review and Bibliometric Analysis

Liu

Wang

Zhang

et al. 2023

Foods

View full text Add to dashboard Cite

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“…Also, color and shapes, which are critical image features can be affected by poor illumination (Amani et al, 2021;Vadivambal & Jayas, 2016). Other factors that affect image quality are humidity level, noise conditions, and the performance of hardware used, for example, differences in calibration, viewing angle range, and installation conditions (Xiao et al, 2022). Furthermore, complete or total replacement of the human vision system with machine vision for quality assessment is not possible because machine vision systems perform optimally under well-controlled environments; which are not always realistic in real-life situations in manufacturing environments.…”

Section: Limitations Of Digital Image Processing Techniques For Asses...mentioning

confidence: 99%

“…Different types of filters are available to remove noise from an image, for example, median filters, Wiener filters, spatial filters, and wavelet filters. (Khojastehnazhand & Ramezani, 2020;Meenu et al, 2021;Nixon & Aguado, 2020;Vadivambal & Jayas, 2016;Wiley & Lucas, 2018;Xiao et al, 2022;Zhu et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

Applications of imaging systems for the assessment of quality characteristics of bread and other baked goods: A review

Olakanmi

Jayas

Paliwal

2023

Comp Rev Food Sci Food Safe

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One of the most widely researched topics in the food industry is bread quality analysis. Different techniques have been developed to assess the quality characteristics of bakery products. However, in the last few decades, the advancement in sensor and computational technologies has increased the use of computer vision to analyze food quality (e.g., bakery products). Despite a large number of publications on the application of imaging methods in the bakery industry, comprehensive reviews detailing the use of conventional analytical techniques and imaging methods for the quality analysis of baked goods are limited. Therefore, this review aims to critically analyze the conventional methods and explore the potential of imaging techniques for the quality assessment of baked products. This review provides an in-depth assessment of the different conventional techniques used for the quality analysis of baked goods which include methods to record the physical characteristics of bread and analyze its quality, sensory-based methods, nutritional-based methods, and the use of dough rheological data for end-product quality prediction. Furthermore, an overview of the image processing stages is presented herein. We also discuss, comprehensively, the applications of imaging techniques for assessing the quality of bread and other baked goods.These applications include studying and predicting baked goods' quality characteristics (color, texture, size, and shape) and classifying them based on these features. The limitations of both conventional techniques (e.g., destructive, laborious, error-prone, and expensive) and imaging methods (e.g., illumination, humidity, and noise) and the future direction of the use of imaging methods for quality analysis of bakery products are discussed.

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“…Computer vision systems enable noninvasive, noncontact, hygienic, rapid, high recognition accuracy, repeatable assessment of product quality. However, the recognition accuracy is subject to error under inappropriate lighting, high humidity, and high noise conditions [ 109 ]. They enable fully automated cost-effective quality assessment systems that can replace manual visual inspection and hence eliminate errors and inconsistencies in results [ 110 ].…”

Section: Application Of Imaging Techniques For the Qualitive Evaluati...mentioning

confidence: 99%

The Need for Machines for the Nondestructive Quality Assessment of Potatoes with the Use of Artificial Intelligence Methods and Imaging Techniques

Danielak

Przybył

Koszela

2023

Sensors

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This article describes chemical and physical parameters, including their role in the storage, trade, and processing of potatoes, as well as their nutritional properties and health benefits resulting from their consumption. An analysis of the share of losses occurring during the production process is presented. The methods and applications used in recent years to estimate the physical and chemical parameters of potatoes during their storage and processing, which determine the quality of potatoes, are presented. The potential of the technologies used to classify the quality of potatoes, mechanical and ultrasonic, and image processing and analysis using vision systems, as well as their use in applications with artificial intelligence, are discussed.

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Application of Machine Vision System in Food Detection

Cited by 28 publications

References 52 publications

Artificial Intelligence in Food Safety: A Decade Review and Bibliometric Analysis

Artificial Intelligence in Food Safety: A Decade Review and Bibliometric Analysis

Applications of imaging systems for the assessment of quality characteristics of bread and other baked goods: A review

The Need for Machines for the Nondestructive Quality Assessment of Potatoes with the Use of Artificial Intelligence Methods and Imaging Techniques

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