Abstract
In order to fully use Internet of things to solve the agricultural fine production, fertilizer, fine and precise control, full traceability and other bottlenecks, and to solve the quality safety of agricultural products from the source and agriculture environmental pollution, a networking application system for modern agriculture is constructed, and networking intelligent gateway based on open source hardware is designed and developed, which realizes the video monitoring function based on motion detection. In addition, basic cloud platform system for modern agriculture network monitoring system is designed and achieved. Based on the RESTful interface service system provided by cloud platform, ExtJs client technology and WeChat re applied in the development and realization of the Demo system of an application layer. As a result, it shows part of application assumption of agricuture network monitoring system, and designs the big data processing and analysis module. What’s more, the Hadoop platform is used to achieve massive data processing produced by applications of Internet of things, and combined with machine learning technology, the corresponding model is established. It is concluded that the best solution is given such as crop variety selection, production and cultivation management and time to market.
Similar content being viewed by others
References
Botta, A., De Donato, W., Persico, V., & Pescapé, A. (2016). Integration of cloud computing and internet of things: A survey. Future Generation Computer Systems, 56, 684–700.
Long, F., Zeiler, P., & Bertsche, B. (2016). Modelling the production systems in industry 4.0 and their availability with high-level petri nets. IFAC-PapersOnLine, 49(12), 145–150.
Gill, S. S., Chana, I., & Buyya, R. (2017). IoT based agriculture as a cloud and big data service: The beginning of digital India. Journal of Organizational and End User Computing (JOEUC), 29(4), 1–23.
Puthal, D., Nepal, S., Ranjan, R., & Chen, J. (2016). Threats to networking cloud and edge datacenters in the internet of things. IEEE Cloud Computing, 3(3), 64–71.
Ranjan, R., Wang, L., Zomaya, A. Y., Tao, J., Jayaraman, P. P., & Georgakopoulos, D. (2016). Advances in methods and techniques for processing streaming big data in datacentre clouds. IEEE Transactions on Emerging Topics in Computing, 4(2), 262–265.
Li, X., Li, D., Wan, J., Vasilakos, A. V., Lai, C.-F., & Wang, S. (2017). A review of industrial wireless networks in the context of industry 4.0. Wirelss Network, 23(1), 23–41.
Nobre, G. C., & Tavares, E. (2017). Scientific literature analysis on big data and internet of things applications on circular economy: A bibliometric study. Scientometrics, 111(1), 463–492.
Obitko, M., & Jirkovskỳ, V. (2015). Big data semantics in industry 4.0. In International conference on industrial applications of holonic and multi-agent systems, Springer (pp. 217–219).
Shi, W., Kleijnen, J. P. C., & Liu, Z.-X. (2014). Factor screening for simulation with multiple responses: Sequential bifurcation. European Journal of Operational Research, 237(1), 136–147.
Shi, W., Shang, J., Liu, Z.-X., & Zuo, X.-L. (2014). Optimal design of the auto parts supply chain: Sequential bifurcation factor screening and multi-response surface methodology. European Journal of Operational Research, 236(2), 664–676.
Carolan, M. (2017). Publicising food: Big data, precision agriculture, and co-experimental techniques of addition. Sociologia Ruralis, 57(2), 135–154.
Ya, Bi, & Cunfa, W. A. N. G. (2016). Analysis of the access system of photovoltaic power station based on photovoltaic power/agricultural planting hybrid. Light and Engineering, 24(31), 95–99.
Cheng, Zhou, & Juncheng, Tao. (2015). Adaptive combination forecasting model for China’s logistics freight volume based on an improved PSO-BP neural network. Kybernetes, 44(4), 646–666.
Acknowledgements
The authors acknowledge the National Natural Science Foundation of China (Grant: 71403085), Hubei society of social sciences (Grant: 2016101).
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Jinbo, C., Yu, Z. & Lam, A. Research on Monitoring Platform of Agricultural Product Circulation Efficiency Supported by Cloud Computing. Wireless Pers Commun 102, 3573–3587 (2018). https://doi.org/10.1007/s11277-018-5392-3
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11277-018-5392-3