Difference between revisions of "Journal:A blockchain-driven IoT-based food quality traceability system for dairy products using a deep learning model"
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==Introduction== | ==Introduction== | ||
In supply chains, the process of perishable products management is extremely complex. This process differs from other products based on various aspects, like deterioration rate, cold storage requirement, and shelf life, from the production unit to the end user. [1][2] Among the various foods in the perishable food supply chain (PFSC), milk and dairy products are the major sources of nutrients. However, poor work practices, animal feed, and the environment may introduce chemical components and contaminants in those milk and dairy products, putting consumers at risk. [3] Increasingly, consumers have become more attentive towards the [[Quality (business)|quality]], source, transport [4][5], and shipping regulations surrounding purchasable food items. [6] Depending on the scenario, there is a varying necessity to trace those food items for achieving better supply chain management (SCM). [7] While globalization has complicated SCM and the [[traceability]] of food items [8], blockchain, a type of distributed ledger technology (DLT), may have a role to play in solving such problems. | |||
Blockchain is considered to be a significant DLT technology impacting commerce, wherein information can be stored in a database and shared among organizations, which have no mutual trust. [9] Blockchain is an underdeveloped technology [10], where few of the applications require trust, which is already devised. [11][12] The data in blockchain cannot be falsified [13] and has the capability for providing trustworthy, secure, and transparent data in public as well as private domains. [14] It is founded on a distributed ledger that is not controlled or owned by a single user. The major benefit of using blockchain is that it can be employed in the PFSC for enhancing the [[Data integrity|integrity of data]] gathered from multiple entities in the supply chain. [15] | |||
Food traceability is component of PFSC logistics, where the data regarding any food item at every stage is captured, stored, and transmitted across the supply chain for enabling quality checks of the product at various points. Moreover, it also provides a way for tracking the product at any required time. [3][16] The primary step for providing traceability is the adequate depiction of traceable resource units (TRUs) for identifying the logistic and production units, traceability of products, and so on for managing the PFSC. [17] | |||
Various studies have focused on developing traceability solutions in the PFSC for various products. For instance, Bumblauskas ''et al.'' proposed a blockchain-oriented traceability scheme considering the eggs, wherein the eggs were tracked from farms to the end users. [18] Cao ''et al.'' described a traceability scheme for strengthening the trust in the existing supply chain between China and Australia. [6] Similarly, Khan ''et al.'' proposed a food provenance system for handling data on the traceability of meat products using blockchain, [[internet of things]] (IoT), and sophisticated deep learning approaches [19], each with their own complexities. [20–23] Other similar approaches have considered a variety of perishable products, like beef, fish, and other agricultural products. [24] A dairy case study was considered by Casino ''et al.'', wherein a distributed trustless and secure scheme was devised for tracing the dairy products of a company. Additionally, Cocco ''et al.'' developed a blockchain-based model for managing the supply chain of a traditional bakery. Finally, the major critical aspects affecting the safety and quality of food, while storing and distributing the products, demand monitoring the humidity and temperature. The utilization of radio frequency identification (RFID) traceability schemes can be highly effective during monitoring of these and other processes. [27][28] | |||
This research work mainly focuses on the development of a food quality traceability system using blockchain, IoT, and deep learning architecture. Here, the devised food traceability scheme is implemented using various modular tools, like the [[internet of things]] (IoT), blockchain [[Information management|data management]], food traceability blockchain architecture, and deep [[wikipedia:Residual neural network|residual network]] (DRN)-based food quality evaluation tools. Data from dairy product manufacturing and its associated supply chains are collected using IoT technologies. The collected data is then transformed into a sequence of blocks in the blockchain network, wherein each node represents the entity involved. The food traceability blockchain is developed by considering the consumers’ purchases of dairy products, product IDs, etc. This blockchain-driven IoT-based deep learning system for improving the food traceability process is designed to monitor the effects of dairy product supply chains, wherein DRN techniques are employed for grading the milk. | |||
The remaining portions of this work are as such. The next section details the related works in food traceability, while the third section elaborates on the system model of IoT-based blockchain-driven food traceability schemes, and the fourth section discusses the methodology used to develop those schemes. The results and discussion of the developed method are discussed in the penultimate section, followed by conclusions about the work. | |||
==Literature review== | |||
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==Notes== | ==Notes== | ||
This presentation is faithful to the original, with only a few minor changes to presentation and updates to spelling and grammar (including to the title). In some cases important information was missing from the references, and that information was added. No other changes were made in accordance with the "No-Derivatives" portion of the distribution license. | This presentation is faithful to the original, with only a few minor changes to presentation and updates to spelling and grammar (including to the title). In some cases important information was missing from the references, and that information was added. Some citations were vague as to how they contributed, or some statements didn't match well with their citations; minor tweaks were made to have the text and citations match up better. The key contribution portion of the introduction was removed for this version, as it was redundant and simply repeated the prior paragraph. No other changes were made in accordance with the "No-Derivatives" portion of the distribution license. | ||
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Revision as of 21:49, 22 January 2024
| Full article title | A blockchain-driven IoT-based food quality traceability system for dairy products using a deep learning model |
|---|---|
| Journal | High-Confidence Computing |
| Author(s) | Manisha, Noothi; Jagadeeshwar, Madiraju |
| Author affiliation(s) | Chaitanya Deemed to be University |
| Primary contact | noothimanisha6 at gmail dot com |
| Year published | 2023 |
| Volume and issue | 3(3) |
| Article # | 100121 |
| DOI | 10.1016/j.hcc.2023.100121 |
| ISSN | 2667-2952 |
| Distribution license | Attribution-NonCommercial-NoDerivs 4.0 International |
| Website | https://www.sciencedirect.com/science/article/pii/S2667295223000193 |
| Download | https://www.sciencedirect.com/science/article/pii/S2667295223000193/pdfft (PDF) |
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Abstract
Food traceability is a critical factor that can ensure food safety while enhancing the credibility of the manufactured product, thus achieving heightened user satisfaction and loyalty. The perishable food supply chain (PFSC) requires paramount care for ensuring quality owing to the limited product life. The PFSC comprises of multiple organizations with varied interests and is more likely to be hesitant in sharing the traceability details among one another owing to a lack of trust, which can be overcome by using blockchain. In this research, an efficient scheme using a blockchain-enabled deep residual network (BC-DRN) is developed to provide food traceability for dairy products. Here, food traceability is determined by using various modular tools, like the internet of things (IoT), blockchain data management, food traceability blockchain architecture, and DRN-based food quality evaluation tools. The devised BC-DRN-based food quality traceability system is examined based on performance metrics such as sensitivity, response time, and testing accuracy, and it has attained better values of 0.939, 109.564 s, and 0.931, respectively.
Keywords: perishable food supply chain, blockchain, internet of things, food traceability, deep learning
Introduction
In supply chains, the process of perishable products management is extremely complex. This process differs from other products based on various aspects, like deterioration rate, cold storage requirement, and shelf life, from the production unit to the end user. [1][2] Among the various foods in the perishable food supply chain (PFSC), milk and dairy products are the major sources of nutrients. However, poor work practices, animal feed, and the environment may introduce chemical components and contaminants in those milk and dairy products, putting consumers at risk. [3] Increasingly, consumers have become more attentive towards the quality, source, transport [4][5], and shipping regulations surrounding purchasable food items. [6] Depending on the scenario, there is a varying necessity to trace those food items for achieving better supply chain management (SCM). [7] While globalization has complicated SCM and the traceability of food items [8], blockchain, a type of distributed ledger technology (DLT), may have a role to play in solving such problems.
Blockchain is considered to be a significant DLT technology impacting commerce, wherein information can be stored in a database and shared among organizations, which have no mutual trust. [9] Blockchain is an underdeveloped technology [10], where few of the applications require trust, which is already devised. [11][12] The data in blockchain cannot be falsified [13] and has the capability for providing trustworthy, secure, and transparent data in public as well as private domains. [14] It is founded on a distributed ledger that is not controlled or owned by a single user. The major benefit of using blockchain is that it can be employed in the PFSC for enhancing the integrity of data gathered from multiple entities in the supply chain. [15]
Food traceability is component of PFSC logistics, where the data regarding any food item at every stage is captured, stored, and transmitted across the supply chain for enabling quality checks of the product at various points. Moreover, it also provides a way for tracking the product at any required time. [3][16] The primary step for providing traceability is the adequate depiction of traceable resource units (TRUs) for identifying the logistic and production units, traceability of products, and so on for managing the PFSC. [17]
Various studies have focused on developing traceability solutions in the PFSC for various products. For instance, Bumblauskas et al. proposed a blockchain-oriented traceability scheme considering the eggs, wherein the eggs were tracked from farms to the end users. [18] Cao et al. described a traceability scheme for strengthening the trust in the existing supply chain between China and Australia. [6] Similarly, Khan et al. proposed a food provenance system for handling data on the traceability of meat products using blockchain, internet of things (IoT), and sophisticated deep learning approaches [19], each with their own complexities. [20–23] Other similar approaches have considered a variety of perishable products, like beef, fish, and other agricultural products. [24] A dairy case study was considered by Casino et al., wherein a distributed trustless and secure scheme was devised for tracing the dairy products of a company. Additionally, Cocco et al. developed a blockchain-based model for managing the supply chain of a traditional bakery. Finally, the major critical aspects affecting the safety and quality of food, while storing and distributing the products, demand monitoring the humidity and temperature. The utilization of radio frequency identification (RFID) traceability schemes can be highly effective during monitoring of these and other processes. [27][28]
This research work mainly focuses on the development of a food quality traceability system using blockchain, IoT, and deep learning architecture. Here, the devised food traceability scheme is implemented using various modular tools, like the internet of things (IoT), blockchain data management, food traceability blockchain architecture, and deep residual network (DRN)-based food quality evaluation tools. Data from dairy product manufacturing and its associated supply chains are collected using IoT technologies. The collected data is then transformed into a sequence of blocks in the blockchain network, wherein each node represents the entity involved. The food traceability blockchain is developed by considering the consumers’ purchases of dairy products, product IDs, etc. This blockchain-driven IoT-based deep learning system for improving the food traceability process is designed to monitor the effects of dairy product supply chains, wherein DRN techniques are employed for grading the milk.
The remaining portions of this work are as such. The next section details the related works in food traceability, while the third section elaborates on the system model of IoT-based blockchain-driven food traceability schemes, and the fourth section discusses the methodology used to develop those schemes. The results and discussion of the developed method are discussed in the penultimate section, followed by conclusions about the work.
Literature review
References
Notes
This presentation is faithful to the original, with only a few minor changes to presentation and updates to spelling and grammar (including to the title). In some cases important information was missing from the references, and that information was added. Some citations were vague as to how they contributed, or some statements didn't match well with their citations; minor tweaks were made to have the text and citations match up better. The key contribution portion of the introduction was removed for this version, as it was redundant and simply repeated the prior paragraph. No other changes were made in accordance with the "No-Derivatives" portion of the distribution license.
