Asian Journal of Computer Science and Technology (AJCST)
Big Data Analytics to Increase the Agricultural Yield by Using Machine Learning Approaches
Author : V. Sudha, S. Mohan and S. ArivalaganVolume 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.