Augmented Reality in Food production traceability – use case

Todorović, Vladimir; Milić, Neda; Lazarević, Milovan

doi:10.1109/eurocon.2019.8861734

Cited by 11 publications

(12 citation statements)

References 10 publications

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“…The inclusion stopped when no more relevant content was present in the results. The outcome was 19 publications with actual applications [25,27,28,[30][31][32][33][34][35][36][37][38], and reviewing references to existing applications [3,18,19,22,26,29,39]. It became obvious that most of the actual applications from research resources are limited and rather outdated [31,32,34,35,37].…”

Section: Methodsmentioning

confidence: 99%

“…The outcome was 19 publications with actual applications [25,27,28,[30][31][32][33][34][35][36][37][38], and reviewing references to existing applications [3,18,19,22,26,29,39]. It became obvious that most of the actual applications from research resources are limited and rather outdated [31,32,34,35,37]. References from reviewing papers to actual applications are mostly commercial [26,33,39].…”

Section: Methodsmentioning

confidence: 99%

“…Marker: The application is activated when seated near a dining surface. Safe Food AR application[37] implemented with Vuforia shows the usage of AR in food packaging for providing nutritional information for sealed food. Context: A food analysis app that provides detailed information about the origin and content of sealed food products.…”

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confidence: 99%

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Augmented Reality in Food Promotion and Analysis: Review and Potentials

Styliaras

2021

Digital

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The paper presents the current state of using augmented reality (AR) in the sectors of food analysis and food promotion through products and orders. Based on an extensive literature review, 34 indicative augmented reality applications of various purposes, target audiences and implementations have been selected and presented. Applications are research-based, commercial, or oriented just for entertainment. Eight classification criteria are defined, especially for these applications, and used for presenting them, including content, context, execution scenario, markers, devices supported, implementation details and appeals based on evaluation, downloads, or sales. Additionally, 16 implementation and supportive platforms that have been used in the presented applications are described. The paper discusses advantages and limitations of current applications leading to proposals of further use of augmented reality in these food sectors towards a uniform handling of all parameters related to food processing, from production until consumption. These parameters include content use, design considerations, implementation issues, use of AR markers, etc.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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Augmented Reality in Food Promotion and Analysis: Review and Potentials

Styliaras

2021

Digital

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“…the authors tried to apply mobile augmented reality in the food market. This can help consumers to obtain information on goods quickly and in a user-friendly manner [18].…”

Section: Literature Reviewmentioning

confidence: 99%

“…Table 1 shows a summary of the findings of the literature review. IoT V [11][12][13][14] V [11][12][13][14] V [12,14] Informational Assistance V [15][16][17][18] Blockchain V [19][20][21] V [19][20][21] Data Analysis V [22][23][24] V [24]…”

Section: Literature Reviewmentioning

confidence: 99%

Design of an IOTA Tangle-Based Intelligent Food Safety Service Platform for Bubble Tea

Chi

Ling

2021

Processes

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Many food safety incidents have occurred in the world in the past 20 years, causing major threats and harm to human life and health. Each country or region has established different food safety management systems (FSMSs) in response, to increase food safety and to reduce food safety risks. Hence, it is important to develop an FSMS service platform with convenience, consistency, effectiveness, scalability, and lightweight computing. The aim of this study is to design and propose an IOTA Tangle-based intelligent food safety service platform for bubble tea—called IF4BT—which modularizes and integrates hazard analysis and critical control point (HACCP) principles to increase data transparency. The deep learning inference engine is based on long short-term memory and Siamese networks to check and extract significant rare data of high-risk factors, exception factors, and noises, depending on daily check and audit. IF4BT can ensure the correctness of the information of food manufacturers, so as to increase food safety and to reduce food safety issues such as allergen cross-contamination, food expiration, food defense, and food fraud.

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