Subscribe Now Subscribe Today
Research Article

Application of Neural Networks Model to Assess Agricultural Products Safety Risks

Shen Xin, Liu Qiao and Zhao Dawei
Facebook Twitter Digg Reddit Linkedin StumbleUpon E-mail

Presently lack of scientific evaluation method would be an obstacle to development of Chinese agricultural products industry. According to its own characteristics of agricultural products , we selected five first-level indicators such as land uses, land analysis and detection, irrigation water, environmental management and availability of base management system and 17 secondary indicators such as historical security of cultivated fields, suitability of soil structure condition, suitability of microbial content, resources protection and usage of information technology, to establish evaluation index system. In evaluation with artificial neural network, the established BP neural network was trained with data collected from famous food base of China. Simulation shows the maximum error between output of BP network and the assessing score of the experts is merely 0.36% and the result supports the application of back-propagation model to evaluate the Chinese agriculture products safety risks.

Related Articles in ASCI
Similar Articles in this Journal
Search in Google Scholar
View Citation
Report Citation

  How to cite this article:

Shen Xin, Liu Qiao and Zhao Dawei , 2013. Application of Neural Networks Model to Assess Agricultural Products Safety Risks. Journal of Applied Sciences, 13: 3049-3054.

DOI: 10.3923/jas.2013.3049.3054


1:  Ahumada, O. and J.R. Villalobos, 2009. Application of planning models in the agri-food supply chain: A review. Eur. J. Oper. Res., 4: 1-20.
CrossRef  |  

2:  Balachandran, K.R. and S. Radhakrishnan, 2005. Quality implications of warranties in a supply Chain. Manage. Sci., 51: 1266-1270.
CrossRef  |  

3:  Chen, G., Y. Wang and G. Han, 2004. Reliability-based supply Chain construction. Ind. Eng. Manage., 9: 72-74.
Direct Link  |  

4:  Van der Vorst, J.G.A.J., 2006. Product traceability in food-supply Chains. Accreditation Q. Assurance, 11: 33-37.
CrossRef  |  

5:  Lai, K.H., T.C.E. Cheng and A.C.L. Yeung, 2005. Relationship stability and supplier commitment to quality. Int. J. Prod. Econ., 3: 397-400.
CrossRef  |  

6:  Liu, Q., X. Shen and L. Liu, 2011. Survey and its development strategy analysis of heilongjiang province agricultural cold chain logistics. Agric. Sci. Hubei, 24: 64-66.

7:  Huo, H., X. Shen and Z. Huang, 2011. Analysis of supply Chain model of agricultural products and quality safety: A case study of heilongjiang province. Asian Agric. Res., 3: 50-57.
Direct Link  |  

8:  Robinson, C.J. and M.K. Malhot, 2005. Two-commodity reliability evaluation for a stochastic-flow network with node failure. Comput. Oper. Res., 3: 1929-1939.

9:  Seth, N., S.G. Deshmukh and P. Vrat, 2006. A framework for measurement of quality of service in supply chains. Supply Chain Manage., 11: 82-94.
CrossRef  |  

10:  Thomas, M.U., 2002. Supply chain reliability for contingency operations. Proceedings of the Annual Reliability and Maintainability Symposium, January 28-31, 2002, Seattle, WA, pp: 61-67.

11:  Tian, Y., 2006. Quality game behavior in third-party logistics service subcontract management. Manage. Sci. China, 19: 2-5.

12:  Wang, J. and W. Zhang, 2009. Analysis on system reliability of supply chain. J. China Safety, 13: 73-76.

13:  Shen, X., 2011. An application of bayesian nash equilibrium model to quality surveillance of service outsourcing. Adv. Inform. Sci. Ser. Sci., 10: 90-97.

©  2021 Science Alert. All Rights Reserved