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Home»Articles»Big Data Analytics to Increase the Agricultural Yield by Using Machine Learning Approaches

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Asian Journal of Computer Science and Technology (AJCST)

Editor Dr. K. Ganesh
Print ISSN : 2249-0701
Frequency : Quarterly

Big Data Analytics to Increase the Agricultural Yield by Using Machine Learning Approaches

Author : V. Sudha, S. Mohan and S. Arivalagan
Volume 7 No.1 Special Issue:November 2018 pp 82-86

Abstract

Agriculture is the backbone of Indian economy. Big data are emerging précised and viable analytical tool in agricultural research field. This review paper facilitates the farmers in selecting the right crops and appropriate cropping pattern for a particular locality. A modern trend in the Agriculture domain has made people realize the importance of big data. It provides a methodology for facing challenges in agricultural production, by applying this Algorithm, using machine learning techniques. The different machine learning techniques survey is presented in this paper to realize enhanced monitory benefits in a particular area. A study of machine learning algorithms for big data Analytic is also done and presented in this paper.

Keywords

Big Data Analytics, Machine Learning Algorithm, Precision Agriculture, Enhancement

Full Text:

References

[1] Gao, “The impact of climate change on China’s crop production: A CMIP5 ensemble assessment,”1st Int. Conf. Agro-Geoinformatics, Agro-Geoinformatics 2012, pp. 208–212, 2012.
[2] P.S. Cornish et al., “Improving crop production for food security and improved livelihoods on the East India Plateau II. Crop options, alternative cropping systems, and capacity building,” Agric. Syst., vol. 137, pp. 180–190, 2015.
[3] K. Sabarina and N. Priya, “Lowering data dimensionality in big data for the benefit of precision agriculture,” Procedia Comput. Sci., vol. 48, no. C, pp. 548–554, 2015.
[4] M.R. Bendre, R.C. Thool, and V.R. Thool, “Big data in precision agriculture: Weather forecasting for future farming,” Proc. 2015 1st Int. Conf. Next Gener. Comput. Technol. NGCT 2015, pp. 744–750, September 2016.
[5] J.W. Kruize, J. Wolfert, H. Scholten, C.N. Verdouw, A. Kassahun, and A.J.M. Beulens, “A reference architecture for Farm Software Ecosystems,” Comput. Electron. Agric., vol. 125, pp. 12–28, 2016.
[6] Ahrary and D. Ludena, “Research Studies on the Agricultural and Commercial Field,” Proc. – 2015 IIAI 4th Int. Congr. Adv. Appl. Informatics, IIAI-AAI 2015, pp. 669–673, 2016.
[7] K.S.S. Reddy and C.S. Bindu, “A review on density-based clustering algorithms for big data analysis,” 2017 Int. Conf. I-SMAC (IoT Soc. Mobile, Anal. Cloud), pp. 123–130, 2017.
[8] S. Athmaja, M. Hanumanthappa, and V. Kavitha, “A survey of machine learning algorithms for big data analytics,” 2017 Int. Conf. Innov. Information, Embed. Commun. Syst., pp. 1-4, 2017.
[9] J. Majumdar, S. Naraseeyappa, and S. Ankalaki, “Analysis of agriculture data using data mining techniques: application of big data,” J. Big Data, vol. 4, no. 1, 2017.
[10] P. Shah, D. Hiremath, and S. Chaudhary, “Big data analytics architecture for an agro advisory system,” Proc. – 23rd IEEE Int. Conf. High Perform. Comput. Work. HiPCW 2016, pp. 43–49, 2017.
[11] R. Kaur, R. Garg, and H. Aggarwal, “Big data analytics framework to identify crop disease and recommendation a solution,” Proc. Int. Conf. Inven. Comput. Technol. ICICT 2016, vol. 2, 2017.
[12] H. Jain and R. Jain, “Big data in weather forecasting: Applications and challenges,” 2017 Int. Conf. Big Data Anal. Comput. Intell. pp. 138–142, 2017.
[13] P. Shah, “Towards Development of Spark Based Agricultural Information System including Geo-Spatial Data,” pp. 3394–3399, 2017.
[14] S. Rajeswari, K. Suthendran, and K. Rajakumar, “A smart agricultural model by integrating IoT, mobile and cloud-based big data analytics,” Proc. 2017 Int. Conf. Intell. Comput. Control. I2C2 2017, pp. 1–5, Jan. 2018.

Asian Journal of Computer Science and Technology is a peer-reviewed international journal that publishes high-quality scientific articles (both theory and practice) and research papers covering all aspects of future computer and Information Technology areas. Topics include, but are not limited to:

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Agriculture is the backbone of Indian economy. Big data are emerging précised and viable analytical tool in agricultural research field. This review paper facilitates the farmers in selecting the right crops and appropriate cropping pattern for a particular locality. A modern trend in the Agriculture domain has made people realize the importance of big data. It provides a methodology for facing challenges in agricultural production, by applying this Algorithm, using machine learning techniques. The different machine learning techniques survey is presented in this paper to realize enhanced monitory benefits in a particular area. A study of machine learning algorithms for big data Analytic is also done and presented in this paper.

Editor-in-Chief
Dr. K. Ganesh
Global Lead, Supply Chain Management, Center of Competence and Senior Knowledge
Expert at McKinsey and Company, India
[email protected]
Editorial Advisory Board
Dr. Eng. Hamid Ali Abed AL-Asadi
Department of Computer Science, Basra University, Iraq
[email protected]
Dr. Norjihan Binti Abdul Ghani
Department of Information System, University of Malaya, Malaysia
[email protected]
Dr. Christos Bouras
Department of Computer Engineering & Informatics, University of Patras, Greece
[email protected]
Dr. Maizatul Akmar Binti Ismail
Department of Information System, University of Malaya, Malaysia
[email protected]
Dr. Harold Castro
Department of Systems Engineering and Computing, University of the Andes, Colombia
[email protected]
Dr. Busyairah Binti Syd Ali
Department of Software Engineering, University of Malaya, Malaysia
[email protected]
Dr. Sri Devi Ravana
Department of Information system, University of Malaya, Malaysia
[email protected]
Dr. Karpaga Selvi Subramanian
Department of Computer Engineering, Mekelle University, Ethiopia
[email protected]
Dr. Mazliza Binti Othman
Department of Computer System & Technology, University of Malaya, Malaysia
[email protected]
Dr. Chiam Yin Kia
Department of Software Engineering, University of Malaya, Malaysia
[email protected]
Dr. OUH Eng Lieh
Department of Information Systems, Singapore Management University, Singapore
[email protected]

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    Editorial Note

    Editorial Dr. K. Ganesh

    Editor-in-Chief
    Dr. K. Ganesh
    Global Lead, Supply Chain Management, Center of Competence and Senior Knowledge
    Expert at McKinsey and Company, India
    [email protected]
    Editorial Advisory Board
    Dr. Eng. Hamid Ali Abed AL-Asadi
    Department of Computer Science, Basra University, Iraq
    [email protected]
    Dr. Norjihan Binti Abdul Ghani
    Department of Information System, University of Malaya, Malaysia
    [email protected]
    Dr. Christos Bouras
    Department of Computer Engineering & Informatics, University of Patras, Greece
    b[email protected]
    Dr. Maizatul Akmar Binti Ismail
    Department of Information System, University of Malaya, Malaysia
    [email protected]
    Dr. Harold Castro
    Department of Systems Engineering and Computing, University of the Andes, Colombia
    [email protected]
    Dr. Busyairah Binti Syd Ali
    Department of Software Engineering, University of Malaya, Malaysia
    [email protected]
    Dr. Sri Devi Ravana
    Department of Information system, University of Malaya, Malaysia
    [email protected]
    Dr. Karpaga Selvi Subramanian
    Department of Computer Engineering, Mekelle University, Ethiopia
    [email protected]
    Dr. Mazliza Binti Othman
    Department of Computer System & Technology, University of Malaya, Malaysia
    [email protected]
    Dr. Chiam Yin Kia
    Department of Software Engineering, University of Malaya, Malaysia
    [email protected]
    Dr. OUH Eng Lieh
    Department of Information Systems, Singapore Management University, Singapore
    [email protected]

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