Bidirectional dynamic interaction model of seed traceability based on location analysis and its implementation

FANG Yu1 ZHU Jingbo1 XU Xue2 QIN Ruiying2 GUO Shupu1 ZHANG Liping1

(1.Institute of Agricultural Economy and Information, Anhui Academy of Agricultural Sciences, Hefei, Anhui Province, China 230031)
(2.Institute of Rice Research, Anhui Academy of Agricultural Sciences, Hefei, Anhui Province, China 230031)
【Knowledge Link】data stream/data flow

【Abstract】Agricultural product safety concerns the national economy and the people’s livelihood. From the starting point of agriculture production, the quality and safety of seeds are crucial and need to be traced. To realize the traceability of crop seeds and help the production enterprise to analyze the operation status and realize the interactive marketing, in this paper, we construct a bidirectional dynamic model of seed traceability based on location analysis. Through the seed electronic code of seeds, the model divides the traditional logistics information chain data into different sets of geographic codes, which are nested according to the geographical location of the distribution. The commodity seed from the factory to the user will experience a lot of path node, the distributor of each node dealer is required to writes its own geographic code. The geographic code contains the company information of the distributors at all levels and the geographic name information of the sale’s area. In the pre-sale stage of the seed, the geographic code set information is independent of each other. When the end user queries the seed electronic code, the flag is activated and the model starts to dynamically analyze the location of the user. By converting the user’s latitude and longitude, this model receives the detailed address, starts to match the geographical code set of information from low to high levels, and ultimately finds the distributor information or returns warning information. The query action of the user connects the geographical code sets of all levels, and the whole traceability data chain provides the traceability result to both the enterprise and the user at this time. The enterprise-side model consisted of a collection layer, a data sharing analysis layer, and an application presentation layer. When the enterprise obtains the traceability information, it can communicate with users, collecting user’s behavior data and pushing enterprise marketing information. The enterprise-side model can be combined with the supply chain data for data analysis to help improve and guide production. Through the correlation analysis of the data of sales and feedback of Huacheng 3366 wheat in ten counties, the goodness-of-fit is 0.997 8, which shows that the feedback from the code scanning can promote the sales. The model adds the bidirectional interactive data link on the traditional traceability model, so that the enterprise can interact with the user when scanning and obtain the user behavior data and push the marketing information. The model’s commissioning of the geocode location conversion rate, the number of days, the site safe operation and other technical indicators can effectively help companies master the actual operation, effectively prevent the occurrence of the transregional behavior, and optimize the enterprise distribution channels. It also provides a more reliable means of information for the regulatory department to manage the seed market.

【Keywords】 seed; crops; models; seed traceability; location analysis; geographic code set; seed electronic code;

【DOI】

【Funds】 Science and Technology Major Project of the Ministry of Science and Technology of Anhui Province (15czz03117) Presidential Youth Innovation Foundation of Anhui Academy of Agricultural Sciences, China (15B1424)

Download this article

(Translated by LIU T)

    References

    [1] Huo Xixue. Characteristics of development of foreign seed industry and its management system [J]. Science & Technology review, 2002 (3): 49–52.

    [2] Li Enpu, Mao Xuefei. Development status and enlightenment of foreign seed quality inspection system [J]. Modern Seed Industry, 2011 (8): 8–11.

    [3] Reynoso W E C, Aguilar L J. Traceability and inventory online by integrating GPS and RFID in a geographic information system “GIS” [J]. American Scientific Research Journal for Engineering, Technology, and Sciences, 2015, 14 (1): 107–118.

    [4] Abenavoli L M, Cuzzupoli F, Chiaravalloti V, et al. Traceability system of olive oil: A case study based on the performance of a new software cloud [J]. Agronomy Research, 2016, 14 (4): 1247–1256.

    [5] Bosona T, Gebresenbet G. Food traceability as an integral part of logistics management in food and agricultural supply chain [J]. Food Control, 2013, 33 (1): 32–48.

    [6] Feng Jun, Xie Zhenming, Ding Yi, et al. Necessity of seed traceability and realization of using QR code technology [J]. Bulletin of Agricultural Science and Technology, 2015 (10): 7–8.

    [7] Li Yan, Li Dongming, Yuan Chao. Research on seed tracing management platform based on private cloud [J]. Journal of Chinese Agricultural Mechanization, 2016, 37 (9): 154–158 (in Chinese).

    [8] Mo Xuezhi, Li Xingwu, Jiang Juan, et al. Design and implementation of traceability information management system for germplasm bank of Guangxi Academy of Agricultural Sciences [J]. Light Industry Science and Technology, 2016 (6): 83–85.

    [9] Huang Qinglin, Zhang Lixin, Jiao Yucong, et al. Cotton quality and quality traceability system based on RFID technology [J]. Jiangsu Agricultural Sciences, 2016, 44 (5): 395–400.

    [10] Zhu Yanni, Lei Jian, Long Chenfeng. A dark tea anti-counterfeiting traceability system build based on bidirectional model [J]. Journal of Hunan Agricultural University: Natural Sciences, 2014, 40 (5): 552–555 (in Chinese).

    [11] Shin Kwangyong, Yang Jing, Kim Hongwoo, et al. Research on application mode of interactive integrated marketing Communications in supply chain management [J]. Logistics Technology, 2012, 31 (7): 304–308 (in Chinese).

    [12] Gao Jie. Study on game model of profit allocation in seed distribution channel dominated by seed enterprises [J]. Seed, 2014, 33 (6): 61–64.

    [13] Thomas Jech. Set Theory [M]. Beijing: World Publishing Corporation, 2007.

    [14] Pramod J. Sadalage, Martin Fowler. No SQL Essence [M]. Beijing: China Machine Press, 2013.

    [15] Wang Hao, Gu Jun, Su Xinning. Research on the model and its application of ontology-driven knowledge management system [J]. Journal of Library Science in China, 2013, 39 (2): 98–110 (in Chinese).

    [16] Yan Bo, Shi Ping, Ding Delong. Risk assessment and control of agricultural supply chain on internet of things [J]. Journal of Industrial Engineering and Engineering Management, 2014, 28 (3): 196–202 (in Chinese).

    [17] Wang Ronghai. Research on the construction of enterprise mobile application system based on hybrid app technology [J]. Software Engineering, 2016, 19 (7): 46–49 (in Chinese).

    [18] Long Guijie. Marketing communication strategy of corporate self-media [J]. News and Writing, 2015 (9): 110–112.

    [19] Fang Yu, Zhang Liping, Guo Shupu, et al. Practice and research for building the internet of crops seed things [J]. Journal of Anhui Agricultural Sciences, 2014, 42 (28): 10003–10006 (in Chinese).

    [20] Crop variety ID code coding specification DB34/T2082-2014 [S]. 2014-02-17.

    [21] Lu Xuzhong, Ni Jinlong, Li Li, et al. Construction of rice variety identity using SSR fingerprint and commodity information [J]. Acta Agronomica Sinica, 2014, 40 (5): 823–829 (in Chinese).

    [22] Zhang Liping, Fang Yu, Dong Wei, et al. Implementation and application of crop seeds electronic code [J]. Guizhou Agricultural Sciences, 2015, 43 (11): 209–211 (in Chinese).

    [23] Chen Ruizhi, Chen Liang. Indoor positioning with smartphones: the state-of-the-art and the challenges [J]. Acta Geodaetica et Cartographica Sinica, 2017, 46 (10): 1316–1326 (in Chinese).

    [24] Bi Jingxue, Zhen Jie, Guo Ying. Accuracy of GPS and A-GPS positioning on Android Phone [J]. Bulletin of Surveying and Mapping, 2016 (7): 10–13 (in Chinese).

    [25] Erik Dahlman, Stefan Parkvall, Johan Skold. 4G LTE/LTE Advanced for Mobile broadband (second edition) [M]. Beijing: Posts and Telecom Press, 2015.

    [26] Yu Jianguo. Construction of new security traceability ecology based on one object and one code [J]. Label Technology, 2016 (4): 33–36.

    [27] Su Weixuan. Analysis on how agricultural producers deal with the market risk of cyclical fluctuations in product prices [J]. Money China, 2016 (2): 53–54.

    [28] Driscoll's uses food traceability technology to collect consumer feedback [EB/OL]. International fruit and vegetable report, http://www.guojiguoshu.com/article/3054,2017-04-18.

    [29] Chen Ying, Zhang Youhua, Guo Shupu, et al. Design and implementation of wheat seeds anti-counterfeiting early warning system based on wisdom packaging [J]. Journal of Anhui Agricultural University, 2016, 43 (6): 1033–1038 (in Chinese).

    [30] Ulrich Sindler. Industry 4.0: The forthcoming fourth industrial revolution [M]. Beijing: China Machine Press, 2014.

    [31] Gao Qijuan, Zhang Youhua, Gu Lichuan. Logistics platform development of crop seeds based on IOT [J]. Journal of Luoyang Institute of Science and Technology: Natural Science Edition, 2015, 25 (2): 47–50 (in Chinese).

    [32] Liu Hongwei. Analysis on application of We Chat small program [J]. Wireless Internet Technology, 2016 (23): 11–13 (in Chinese).

This Article

ISSN:1002-6819

CN: 11-2047/S

Vol 33, No. 24, Pages 207-214

December 2017

Downloads:2

Share
Article Outline

Knowledge

Abstract

  • 0 Introduction
  • 1 Establishment of the bidirectional dynamic interaction model
  • 2 Implementation of bidirectional dynamic interaction model of seed traceability
  • 3 Analysis and comparison of bidirectional dynamic interaction model of seed traceability and traditional traceability model
  • 4 Discussion
  • 5 Conclusions
  • References