基于深度学习模型的BC驱动的基于物联网的乳制品食品质量追溯系统

IF 3.2 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS High-Confidence Computing Pub Date : 2023-09-01 DOI:10.1016/j.hcc.2023.100121
Noothi Manisha, Madiraju Jagadeeshwar
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引用次数: 0

摘要

食品可追溯性是确保食品安全的关键因素,可以提高产品的可信度,从而提高用户满意度和忠诚度。易腐食品SC(PFSC)由于产品寿命有限,因此需要高度重视确保质量。PFSC由多个利益不同的组织组成,由于缺乏信任,在彼此之间共享可追溯性细节时更可能犹豫不决,这可以通过使用区块链(BC)来克服。在本研究中,开发了一种利用BC深度残差网络(BC-DRN)为乳制品提供食品可追溯性的有效方案。在这里,食品可追溯性是通过使用各种模块来确定的,如物联网(IoT)、BC数据管理、食品可追溯BC架构和基于DRN的食品质量评估模块。设计的基于BC DRN的食品质量可追溯系统基于其性能指标(如灵敏度、响应时间和测试准确性)进行了检查,并获得了更好的值0.939、109.564 s和0.931。
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BC driven IoT-based food quality traceability system for dairy product using deep learning model

Food traceability is a critical factor that can ensure food safety for enhancing the credibility of the product, thus achieving heightened user satisfaction and loyalty. The Perishable Food SC (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 (BC). In this research, an efficient scheme using BC-Deep Residual Network (BC-DRN) is developed to provide food traceability for dairy products. Here, food traceability is determined by using various modules, like the Internet of Things (IoT), BC data management, Food traceability BC architecture, and DRN-based food quality evaluation modules. The devised BC-DRN-based food quality traceability system is examined based on its performance metrics, like sensitivity, response time, and testing accuracy, and it has attained better values of 0.939, 109.564 s, and 0.931.

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