Review on agricultural products smart traceability system affected by new generation information technology

被引：0
|
作者：
Qian J. [1 ]
Wu W. [1 ]
Yang P. [1 ]
机构：
[1] Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Key Laboratory of Agricultural Remote Sensing, Beijing
来源：
Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering | 2020年 / 36卷 / 05期
关键词：
Agricultural products; Artificial intelligence; Big data; Block chain; Food safety; New generation information technology; Traceability;
D O I：
10.11975/j.issn.1002-6819.2020.05.021
中图分类号：
学科分类号：
摘要：
From the problem of mad cow disease to date, the traceability system as effective means to ensure food safety has been introduced for nearly 30 years. Now, reducing breakage degree for traceability chain, enhancing traceability credibility, and improving early warning capacity for agri-food quality and safety, these has increasingly become hot topics of traceability system research, which is also urgent problems in traceability system application. Focusing on these issues, the agricultural products smart traceability system affected by new generation information technologies based on the relevant literature was summarized and analyzed. Firstly, the development stages of traceability system from 1.0 to 3.0 were presented. The features of the three stages were information recording for 1.0, data integration for 2.0, and intelligent decision-making for 3.0, respectively. Secondly, the relationship of new generation information technology including internet of things (IoT), big data, cloud computing, blockchain, artificial intelligence (AI) was described. The new generation of information technology is not only the vertical promotion of single technology, but also the horizontal penetration of technology integration. The development trend about big data, artificial intelligence and block chain were summarized. Finally, the relevant research on the issues of reducing the breakage degree for traceability with AI, improving early warning capability for agri-food quality and safety with big data, and enhancing the traceability credibility of the whole supply chain with blockchain were summarized. The orientation of in-depth study was put forward in the light of the technology development trend. For combing AI, traceability granularity should be improved from within and among supply chains through establishment some flexible and intelligent traceability models. For coming big data, prediction quality and optimization processing model should be enhanced through data mining and analysis from different levels. For coming blockchain, traceability block structure optimization, privacy protection, block chain consensus algorithm should be performed to meet the requirement agri-food. In fact, AI, big data, block chain and other technologies have different characteristics. In order to improve the intelligent level for traceability system, the different technologies should be integrated deeply. This paper provides a useful reference for grasping the development trend of traceability system, for knowing the research hot spot and for understanding the application bottleneck. © 2020, Editorial Department of the Transactions of the Chinese Society of Agricultural Engineering. All right reserved.
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页码：182 / 191
页数：9
相关论文
共 81 条
  • [1] Kennedy S., Why can't we test our way to absolute food safety?, Science, 322, 5908, pp. 1641-1643, (2009)
  • [2] McEntire J.C., Arens S., Bernstein M., Et al., Traceability (product tracing) in food systems: An IFT report submitted to the FDA, volume 1: Technical aspects and recommendations, Comprehensive Reviews in Food Science & Food Safety, 9, 1, pp. 92-158, (2010)
  • [3] Tarjan L., Senk I., Tegeltija S., Et al., A readability analysis for QR code application in a traceability system, Computers and Electronics in Agriculture, 109, 11, pp. 1-11, (2014)
  • [4] Karlsen K.M., Donnelly K.A.M., Olsen P., Granularity and its importance for traceability in a farmed salmon supply chain, Journal of Food Engineering, 102, 1, pp. 1-8, (2011)
  • [5] Qian J., Song Y., Wang S., Et al., Quality and safety traceable information mining in wheat flour processing based on risk matrix and flow analysis method, Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE), 35, 2, pp. 302-308, (2019)
  • [6] Bosona T., Gerbresenbet G., Food traceability as an integral part of logistics management in food and agricultural supply chain, Food Control, 33, 1, pp. 32-48, (2013)
  • [7] Smith G.C., Tatum J.D., Belk K.E., Et al., Traceability from a US perspective, Meat Science, 71, 1, pp. 174-193, (2005)
  • [8] Stewart W.G., Food authentication and traceability: An Asian and Australian perspective, Food Control, 72, 2, (2017)
  • [9] Xie J., Lu C., Li B., Et al., Implementation of pork traceability system based on. NET framework, Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE), 22, 6, pp. 218-220, (2006)
  • [10] Yang X., Qian J., Sun C., Et al., Design and application of safe production and quality traceability system for vegetable, Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE), 24, 3, pp. 162-166, (2008)
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