SmartNoshWaste: Using Blockchain, Machine Learning, Cloud Computing and QR Code to Reduce Food Waste in Decentralized Web 3.0 Enabled Smart Cities

Dey, Somdip; Saha, Suman; Singh, Amit Kumar; McDonald-Maier, Klaus

doi:10.3390/smartcities5010011

Cited by 36 publications

(30 citation statements)

References 41 publications

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“…Quantitative 31 [31,43,79,97,109,114,118,123,125,129,136,142,151,160,161,165,167,175,176,[179][180][181]183,188,191,194,[263][264][265][266]] Qualitative 8 [109,121,129,148,182,195,267,268] Proposal (model, architecture, etc.) 24 [79,100,102,103,112,125,128,130,135,146,147,158,159,...…”

Section: Methodsmentioning

confidence: 99%

“…To assure economic, social, and environmental sustainability, modern agriculture needs to incorporate technologies like machine learning, big data analytics, cloud computing, the IoT, blockchain, and other developments (e.g., smart sensors, robotics, digital twins, and cyber-physical systems) more synergistically [99,109,110]. With less human participation and more accurate data, these cutting-edge digital technologies enable the development of interconnected, data-driven, intelligent, agile, and autonomous systems for entire supply chain processes [98,101,104,105,111,112]. Blockchain has also started to be used in sustainable e-agriculture applications [113] and it fosters cooperation in the e-agriculture supply chains [31,106,114].…”

Section: The Uses Benefits and Challenges Of Blockchain Technology Fo...mentioning

confidence: 99%

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Blockchain Technology and Sustainability in Supply Chains and a Closer Look at Different Industries: A Mixed Method Approach

2022

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Background: This study presents a comprehensive review of blockchain technology with a sustainability orientation in supply chains and logistics. Methods: The publications are extracted from the Scopus and Web of Science databases, comprising 552 publications between 2017 and 2022. Several bibliometric laws and techniques, namely three-field analysis, Bradford’s Law, Lotka’s Law, and thematic maps, are applied in R with the bibliometrix package. Content analysis is also carried out based on 185 publications to appreciate the industry-based view of the field. Results: The bibliometric results indicate that this field is on the rise. Authors, sources, affiliations, countries, keywords, and their relationships are also addressed. The findings of the content analysis and thematic maps reveal that some of the most highlighted themes in the literature include traceability, COVID-19, the internet of things, and Industry 4.0. The most popular industry in this field is discovered to be food and agriculture. Conclusions: This paper contributes to the still relatively scarce literature on how blockchain technology fosters sustainable supply chains and logistics, providing a closer look at blockchain use, methodologies, and future directions for different industries concerning food, agriculture, fashion, textile and apparel, manufacturing, automotive, maritime and shipping, healthcare and pharmaceutical, mining and mineral, and energy.

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Section: Methodsmentioning

confidence: 99%

Section: The Uses Benefits and Challenges Of Blockchain Technology Fo...mentioning

confidence: 99%

Blockchain Technology and Sustainability in Supply Chains and a Closer Look at Different Industries: A Mixed Method Approach

2022

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“…In this study, information seeking refers to consumers' search behavior for the QR label of fresh fruits. QR code in digital form has really good resistance against defacement or error correction (Dey et al, 2022).…”

Section: Data Description and Summary Statisticsmentioning

confidence: 99%

“…Among them, most cases provide product information through scanning QR codes. Dey et al (2022) proposed a blockchainbased multi-layered framework utilizing cloud computing, QR code, and reinforcement learning to reduce food waste in different phases of the supply chain. However, little research about consumers' intention to trace QR labels based on blockchain technology is known.…”

Section: Introductionmentioning

confidence: 99%

Consumers' food control risk attitude for blockchain traceable information seeking: Evidence from fresh fruit buyers in China

Zhai

Sher

et al. 2022

Front. Sustain. Food Syst.

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The blockchain-based traceability in agri-food marketing has brought a disruptive paradigm shift by removing the inherent information asymmetry problem. Likewise, revealing sufficient product quality and attributes information could break agricultural markets' “Lemon Market” dilemma. This study takes the fresh fruit with blockchain traceability QR label as a case and systematically investigates the influence of consumers' food control risk attitude on information-seeking intentions. We utilized online survey data of 1,058 fresh fruit buyers and simultaneously applied ordinary least square (OLS), ordered logit model (Ologit), and propensity score matching (PSM) approaches to overcome the potential self-selection biases and confounding factors. The results show that risk attitude significantly negatively impacts consumers' information seeking fresh fruits. The stronger consumers' risk preference, the lower the probability of information seeking. Furthermore, we used PSM to overcome potential sample selectivity bias; therefore, PSM reinforces the significance of OLS and Ologit results. The sub-sample estimation results show that young individuals with high school and below education have stronger blockchain information-seeking intentions. The study provides new insights into the role of food control risk attitude and agri-food information traceability and offers several measures for policy and practice to realize a border trajectory in agri-food information disclosure.

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“…Artificial intelligence (AI) is becoming more popular because of advances in digital and mobile technology. The impact of machine learning has grown even more in these areas because of new hardware and cloud-based solutions [ 2 ]. Mechanical or electrical failures are usually the cause of vibration.…”

Section: Introductionmentioning

confidence: 99%

Artificial Intelligence and 3D Scanning Laser Combination for Supervision and Fault Diagnostics

Vives

Palací²

2022

Sensors

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In this work, we combine some of the most relevant artificial intelligence (AI) techniques with a range-resolved interferometry (RRI) instrument applied to the maintenance of a wind turbine. This method of automatic and autonomous learning can identify, monitor, and detect the electrical and mechanical components of wind turbines to predict, detect, and anticipate their degeneration. A scanner laser is used to detect vibrations in two different failure states. Following each working cycle, RRI in-process measurements agree with in-process hand measurements of on-machine micrometers, as well as laser scanning in-process measurements. As a result, the proposed method should be very useful for supervising and diagnosing wind turbine faults in harsh environments. In addition, it will be able to perform in-process measurements at low costs.

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SmartNoshWaste: Using Blockchain, Machine Learning, Cloud Computing and QR Code to Reduce Food Waste in Decentralized Web 3.0 Enabled Smart Cities

Cited by 36 publications

References 41 publications

Blockchain Technology and Sustainability in Supply Chains and a Closer Look at Different Industries: A Mixed Method Approach

Blockchain Technology and Sustainability in Supply Chains and a Closer Look at Different Industries: A Mixed Method Approach

Consumers' food control risk attitude for blockchain traceable information seeking: Evidence from fresh fruit buyers in China

Artificial Intelligence and 3D Scanning Laser Combination for Supervision and Fault Diagnostics

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