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A Brief Review of Big Data in the Agriculture Domain

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Technologies and Innovation (CITI 2019)

Abstract

Agriculture is one of the most important sectors in the world. Agricultural productivity is important for a country’s economy. Big Data technology has been successfully used to solve problems from several sectors such as health, finance, and energy for mention a few. In agriculture, Big data is being used for making better decisions and improving productivity. The increasing interest of Big Data technology in agriculture calls for a clear review. The objective of this review is to collect all relevant research on Big Data technology in agriculture to detect current research topics, benefits of Big Data in Agriculture, Big Data sources, algorithms, approaches, and techniques used. We have extracted 18 primary studies from scientific repositories published between 2017 and 2019. The results show that 67% of the studies are dominated by Indian and China research community. The results also show that half of the studies are focused on crop quality and productivity.

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References

  1. Elhoseny, M., Abdelaziz, A., Salama, A.S., Riad, A.M., Muhammad, K., Sangaiah, A.K.: A hybrid model of Internet of Things and cloud computing to manage big data in health services applications. Futur. Gener. Comput. Syst. 86, 1383–1394 (2018). https://doi.org/10.1016/J.FUTURE.2018.03.005

    Article  Google Scholar 

  2. Wang, Y., Kung, L., Wang, W.Y.C., Cegielski, C.G.: An integrated big data analytics-enabled transformation model: application to health care. Inf. Manag. 55, 64–79 (2018). https://doi.org/10.1016/J.IM.2017.04.001

    Article  Google Scholar 

  3. Liang, Y.C., Lu, X., Li, W.D., Wang, S.: Cyber physical system and big data enabled energy efficient machining optimisation. J. Clean. Prod. 187, 46–62 (2018). https://doi.org/10.1016/J.JCLEPRO.2018.03.149

    Article  Google Scholar 

  4. Zhang, Y., Ma, S., Yang, H., Lv, J., Liu, Y.: A big data driven analytical framework for energy-intensive manufacturing industries. J. Clean. Prod. 197, 57–72 (2018). https://doi.org/10.1016/J.JCLEPRO.2018.06.170

    Article  Google Scholar 

  5. Begenau, J., Farboodi, M., Veldkamp, L.: Big data in finance and the growth of large firms. J Monet. Econ. 97, 71–87 (2018). https://doi.org/10.1016/J.JMONECO.2018.05.013

    Article  Google Scholar 

  6. Müller, O., Fay, M., vom Brocke, J.: The effect of big data and analytics on firm performance: an econometric analysis considering industry characteristics. J. Manag. Inf. Syst. 35, 488–509 (2018). https://doi.org/10.1080/07421222.2018.1451955

    Article  Google Scholar 

  7. Brereton, P., Kitchenham, B.A., Budgen, D., Turner, M., Khalil, M.: Lessons from applying the systematic literature review process within the software engineering domain. J. Syst. Softw. 80, 571–583 (2007). https://doi.org/10.1016/j.jss.2006.07.009

    Article  Google Scholar 

  8. Rajeswari, S., Suthendran, K., Rajakumar, K.: A smart agricultural model by integrating IoT, mobile and cloud-based big data analytics. In: 2017 International Conference on Intelligent Computing and Control (I2C2), pp 1–5. IEEE (2017)

    Google Scholar 

  9. Chang, H.-Y., Wang, J.-J., Lin, C.-Y., Chen, C.-H.: An agricultural data gathering platform based on Internet of Things and big data. In: 2018 International Symposium on Computer, Consumer and Control (IS3C), pp. 302–305. IEEE (2018)

    Google Scholar 

  10. Sahu, S., Chawla, M., Khare, N.: An efficient analysis of crop yield prediction using Hadoop framework based on random forest approach. In: 2017 International Conference on Computing, Communication and Automation (ICCCA), pp. 53–57. IEEE (2017)

    Google Scholar 

  11. Tseng, F.-H., Cho, H.-H., Wu, H.-T.: Applying big data for intelligent agriculture-based crop selection analysis. IEEE Access 7, 116965–116974 (2019). https://doi.org/10.1109/ACCESS.2019.2935564

    Article  Google Scholar 

  12. Sharma, S., Rathee, G., Saini, H.: Big data analytics for crop prediction mode using optimization technique. In: 2018 Fifth International Conference on Parallel, Distributed and Grid Computing (PDGC), pp. 760–764. IEEE (2018)

    Google Scholar 

  13. Kumar, M., Nagar, M.: Big data analytics in agriculture and distribution channel. In: 2017 International Conference on Computing Methodologies and Communication (ICCMC), pp. 384–387. IEEE (2017)

    Google Scholar 

  14. Alves, G.M., Cruvinel, P.E.: Big data infrastructure for agricultural tomographic images reconstruction. In: 2018 IEEE 12th International Conference on Semantic Computing (ICSC), pp. 346–351. IEEE (2018)

    Google Scholar 

  15. Bhosale, S.V., Thombare, R.A., Dhemey, P.G., Chaudhari, A.N.: Crop yield prediction using data analytics and hybrid approach. In: 2018 Fourth International Conference on Computing Communication Control and Automation (ICCUBEA), pp. 1–5. IEEE (2018)

    Google Scholar 

  16. Li, D., Zheng, Y., Zhao, W.: Fault analysis system for agricultural machinery based on big data. IEEE Access 7, 99136–99151 (2019). https://doi.org/10.1109/ACCESS.2019.2928973

    Article  Google Scholar 

  17. Sekhar, C.C., Sekhar, C.: Productivity improvement in agriculture sector using big data tools. In: 2017 International Conference on Big Data Analytics and Computational Intelligence (ICBDAC), pp. 169–172. IEEE (2017)

    Google Scholar 

  18. Shah, P., Hiremath, D., Chaudhary, S.: Towards development of spark based agricultural information system including geo-spatial data. In: 2017 IEEE International Conference on Big Data (Big Data), pp. 3476–3481. IEEE (2017)

    Google Scholar 

  19. Ji, G., Hu, L., Tan, K.H.: A study on decision-making of food supply chain based on big data. J. Syst. Sci. Syst. Eng. 26, 183–198 (2017). https://doi.org/10.1007/s11518-016-5320-6

    Article  Google Scholar 

  20. Ngo, V.M., Le-Khac, N.-A., Kechadi, M.-T.: Designing and implementing data warehouse for agricultural big data. In: Chen, K., Seshadri, S., Zhang, L.-J. (eds.) BIGDATA 2019. LNCS, vol. 11514, pp. 1–17. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-23551-2_1

    Chapter  Google Scholar 

  21. Chen, X., Gong, J.: Research on precision marketing model of Beijing agricultural products under big data environment. In: Xhafa, F., Patnaik, S., Tavana, M. (eds.) IISA 2018. AISC, vol. 885, pp. 805–812. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-02804-6_105

    Chapter  Google Scholar 

  22. Parvin, S., et al.: Smart food security system using IoT and big data analytics. Advances in Intelligent Systems and Computing, vol. 800, pp. 253–258. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-14070-0_35

    Book  Google Scholar 

  23. Huang, J., Zhang, L.: The big data processing platform for intelligent agriculture. In: AIP Conference Proceedings, p. 20033. AIP Publishing LLC (2017)

    Google Scholar 

  24. Chandra Sekhar, Ch., Uday Kumar, J., Kishor Kumar, B., Sekhar, Ch.: Effective use of big data analytics in crop planning to increase agriculture production in India. Int. J. Adv. Sci. Technol. 113, 31–40 (2018)

    Article  Google Scholar 

  25. Kliangkhlao, M., Limsiroratana, S.: Towards the idea of agricultural market understanding for automatic event detection. In: Proceedings of the 2019 8th International Conference on Software and Computer Applications - ICSCA 2019, pp. 81–86. ACM Press, New York, USA (2019)

    Google Scholar 

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Correspondence to William Bazán-Vera .

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Bazán-Vera, W., Bermeo-Almeida, O., Cardenas-Rodriguez, M., Ferruzola-Gómez, E. (2019). A Brief Review of Big Data in the Agriculture Domain. In: Valencia-García, R., Alcaraz-Mármol, G., Del Cioppo-Morstadt, J., Vera-Lucio, N., Bucaram-Leverone, M. (eds) Technologies and Innovation. CITI 2019. Communications in Computer and Information Science, vol 1124. Springer, Cham. https://doi.org/10.1007/978-3-030-34989-9_6

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  • DOI: https://doi.org/10.1007/978-3-030-34989-9_6

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