Development and Application of Big Data Platform for “Bohai granary”

  • Pingzeng Liu
  • Xiujuan Wang
  • Fujiang Wen
  • Yuqi Liu
  • Zhanquan Sun
  • Chao Zhang
  • Maoling Yan
  • Linqiang Fan


In order to effectively solve series of problems which affect grain production in implementation of the “Bohai granary” project, such as the wide varieties of factors (including soil fertility, soil salinity, pH, underground water level, water salinity, crop varieties and management etc.), the huge spatial and temporal variability, the difficulty of real-time information collection, the complexity of analysis and processing etc., a “Bohai granary” big data platform is developed. With the invention of “Shennong IOAT” series of sensing equipment, the acquisition of multi-site real-time synchronization field information is achieved. And by combining a variety of data collection methods, the collection of factors comprehensively affecting the grain production has been realized. Based on the Hadoop big data platform, it becomes a reality to store, analyze and apply the collected data efficiently. Through the big data platform, the precise management of grain production, the automatic warning of soil moisture content, the disaster and typical insect damage in crop growth, and the automatic control of typical link production are all achieved.


Agricultural big data Internet of agricultural things Precision management Automatic warning Intelligent control 



This work was financially supported by Shandong independent innovation and achievements transformation Project (2014ZZCX07106).


  1. 1.
    Li, G., & Cheng, X. (2012). Research status and scientific thinking of big data. Bulletin of Chinese Academy of Sciences, 27(6), 647–657.Google Scholar
  2. 2.
    Zikopoulos, P., & Eaton, C. (2012). Understanding big data: Analytics for enterprise class Hadoop and streaming data. New York: McGraw-Hill Osborne Media.Google Scholar
  3. 3.
    Hidalgo, C. A. How to transform big data into knowledge [EB/OL] (2013).
  4. 4.
    Zhang, Y. (2013). Research on big data management with architecture of fusion of database and MapReduce. e-Science Technology & Application, 4(1), 19–29.Google Scholar
  5. 5.
    Tao, X. J., Xiao-Feng, H. U., & Liu, Y. (2013). Overview of big data research. Journal of System Simulation, 25(260S1), 142–146.Google Scholar
  6. 6.
    Jin, X., Wang, Y., & Cheng, X. (2013). Research system and status of big data. Information & Communications Technologies, 6, 35–43.Google Scholar
  7. 7.
    Sun, Z. F., Du, K. M., Zheng, F. X., et al. (2013). Perspectives of research and application of big data on smart agriculture. Journal of Agricultural Science & Technology, 15(6), 63–71.Google Scholar
  8. 8.
    Shi-Wei, X. U., Wang, D. J., & Zhe-Min, L. I. (2015). Application research on big data promote agricultural modernization. Scientia Agricultura Sinica, 48(17), 3429–3438.Google Scholar
  9. 9.
    Yu, Y., & Song, M. (2013). Big data. ZTE Technology Journal, 1, 57–60.Google Scholar
  10. 10.
    Weng, W. H., & Lin, W. T. (2014). Development trends and strategy planning in big data industry. Contemporary Management Research, 10, 203–214.CrossRefGoogle Scholar
  11. 11.
    Gong, X. Y., Bo-Hu, L. I., Chai, X. D., et al. (2014). Survey on big data platform technology. Journal of System Simulation, 26(3), 489–496.Google Scholar
  12. 12.
    Wang, H. Y. (2011). An application of Hadoop platform in cloud computing. Computer Engineering & Software, 32(4), 36–38.Google Scholar
  13. 13.
    Yan, X. F., & Zhang, D. X. (2013). Big data research. Computer Technology & Development, 23(4), 168–172.MathSciNetGoogle Scholar
  14. 14.
    Liu, Z. H., & Zhang, Q. L. (2014). Research overview of big data technology. Journal of Zhejiang University, 48(6), 957–972.MATHGoogle Scholar
  15. 15.
    Zhang, F. J. (2014). Overview on big data technology. Communications Technology, 47(11), 1240–1248.Google Scholar
  16. 16.
    Xuelong, L. I., & Gong, H. G. (2015). A survey on big data systems. Scientia Sinica Informationis, 45(1), 1–44.Google Scholar
  17. 17.
    Shepler, S., Callaghan, B., Robinson, D., et al. (2003). Network File System (NFS) version 4 Protocol. AMC Transactions on Computer Systems, 6(5), 3–4.Google Scholar
  18. 18.
    Ghemawat, S., Gobioff, H., & Leung, S. T. (2003). The Google file system. In Nineteenth ACM symposium on operating systems principles (pp. 29–43). ACM.Google Scholar
  19. 19.
    Borthakur, D. (2008). HDFS architecture guide. HADOOP APACHE PROJECT.
  20. 20.
    Beaver, D., Kumar, S., Li, H. C., et al. (2010). Finding a needle in Haystack: Facebook’s photo storage. In: Usenix conference on operating systems design and implementation (pp. 47–60). USENIX Association.Google Scholar
  21. 21.
  22. 22.
    Decandia, G., Hastorun, D., Jampani, M., et al. (2007). Dynamo: Amazon’s highly available key-value store. In ACM sigops symposium on operating systems principles (pp. 205–220). ACM.Google Scholar
  23. 23.
    Chang, F., Dean, J., Ghemawat, S., et al. (2010). Tushar Chandra, Andrew Fikes, and Robert E. Gruber. Bigtable: A distributed storage system for structured data. In Proceedings of USENIX Symposium on Operating System.Google Scholar
  24. 24.
    Malewicz, G., Austern, M. H., Bik, A. J. C., et al. (2010). Pregel: A system for large-scale graph processing. In ACM SIGMOD international conference on management of data (pp. 135–146). ACM.Google Scholar
  25. 25.
    Melnik, S., Gubarev, A., Long, J. J., et al. (2011). Dremel: Interactive analysis of web-scale datasets. Communications of the ACM, 3(12), 114–123.CrossRefGoogle Scholar
  26. 26.
    Labrinidis, A., & Jagadish, H. V. (2012). Challenges and opportunities with big data. VLDB Endowment.Google Scholar
  27. 27.
    Crockford, D. (2006). The application/json media type for javascript object notation (json). Journal of Biological Regulators & Homeostatic Agents, 13(4), 250–251.Google Scholar
  28. 28.
    Dean, J., & Ghemawat, S. (2008). MapReduce: Simplified data processing on large clusters. ACM, 51, 107.CrossRefGoogle Scholar
  29. 29.
    Isard, M., Budiu, M., Yu, Y., et al. (2007). Dryad:distributed data-parallel programs from sequential building blocks. ACM SIGOPS Operating Systems Review, 41(3), 59–72.CrossRefGoogle Scholar
  30. 30.
    Low, Y., Bickson, D., Gonzalez, J., et al. (2012). Distributed GraphLab: A framework for machine learning and data mining in the cloud. Proceedings of the Vldb Endowment, 5(8), 716–727.CrossRefGoogle Scholar
  31. 31.
    Neumeyer, L., Robbins, B., Nair, A., et al. (2010). S4: Distributed stream computing platform. In IEEE International Conference on Data Mining Workshops (pp. 170–177). IEEE Computer Society.Google Scholar
  32. 32.
    Zwaenepoel, W., Zwaenepoel, W., & Zwaenepoel, W. (2013). X-Stream: Edge-centric graph processing using streaming partitions. In Twenty-Fourth ACM symposium on operating systems principles (pp. 472–488). ACM.Google Scholar
  33. 33.
    Ren, L., Du, Y., Ma, S., Zhang, X., & Dai, G. (2014). Visual analytics towards big data. Journal of Software, 25(9), 1909–1936. Scholar
  34. 34.
    Doctorow, C. (2008). Big data: Welcome to the petacentre. Nature, 455(7209), 16.CrossRefGoogle Scholar
  35. 35.
    Keim, D., Andrienko, G., Fekete, J. D., Görg, C., Kohlhammer, J., & Melançon, G. (2008). Visual analytics: Definition, process, and challenges. Information Visualization: Human-Centered Issues and Perspectives, 4950, 154–175.CrossRefGoogle Scholar
  36. 36.
    Min, C., & Jaenicke, H. (2010). An information-theoretic framework for visualization. IEEE Transactions on Visualization and Computer Graphics, 16(6), 1206–1215.CrossRefGoogle Scholar
  37. 37.
    Dervin, B. (1998). Sense-making theory and practice: An overview of user interests in knowledge seeking and use. Journal of Knowledge Management, 2(2), 36–46.CrossRefGoogle Scholar
  38. 38.
    Dervin, B. (1999). On studying information seeking methodologically: The implications of connecting metatheory to method. Information Processing & Management, 35(6), 727–750.CrossRefGoogle Scholar
  39. 39.
    Piaget, J. (2008). Intellectual evolution from adolescence to adulthood. Human Development, 51(1), 40–47.CrossRefGoogle Scholar
  40. 40.
    Chandramohan, V., & Christensen, K. (2002). A first look at wired sensor networks for video surveillance systems. In IEEE conference on local computer Networks, Proceedings. LCN 2002 (pp. 728–729). IEEE.Google Scholar
  41. 41.
    Post, F. H., Nielson, G. M., & Bonneau, G. P. (2003). Data visualization. New York: Springer.CrossRefGoogle Scholar
  42. 42.
    Vinh, N. Q., Yu, Q., Huang, M. L., et al. (2013). TabuVis: A tool for visual analytics multidimensional datasets. Science China, 56(5), 1–12.Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  • Pingzeng Liu
    • 1
  • Xiujuan Wang
    • 2
  • Fujiang Wen
    • 1
  • Yuqi Liu
    • 3
  • Zhanquan Sun
    • 4
  • Chao Zhang
    • 1
  • Maoling Yan
    • 1
  • Linqiang Fan
    • 1
  1. 1.College of Information Science and EngineeringShandong Agricultural UniversityTaianChina
  2. 2.College of Foreign LanguagesShandong Agricultural UniversityTaianChina
  3. 3.Wake Forest University School of BusinessWinston-SalemUnited States
  4. 4.Shandong Provincial Key Laboratory of Computer NetworksShandong Computer Science CenterJinanChina

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