AGRICULTURAL INTELLIGENCE DECISION SYSTEM
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Abstract
In the recent years, the huge volume of real time data in the agricultural sector and its need for an efficient and effective processing, stimulate the use of novel technologies and platform to acquire, store, process, analyze and visualize large data sets for future predictions and decision making.
A field which uses information technology to aggregate data from multiple sources is something called the practice of precision agriculture.
Big Data is an evolving term given to a good area of data-intensive technologies during which the datasets are extremely large that handling them become tougher than how it was before. Due to the critical challenges facing the agriculture sector farmers feel more forced to adopt intensive farming practices and sustainable.
Analysis of agricultural land use in development and environment projects, also for preventing and assessing climate events, or for monitoring and forecasting food security crisis, all these need a reliable crop classification.
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References
 1] R. D. Ludena, A. Ahrary et al., Big Data approach in an ICT Agriculture project, in Awareness Science and Technology and Ubi-Media Computing (iCAST-UMEDIA), 2013 International Joint Conference on, 2013, pp. 261–265.
 [2] N. Alexandratos, J. Bruinsma, et al., World agriculture towards 2030/2050: the 2012 revision, ESA Work Pap, vol. 3, 2012.
 [3] A. F. McCalla, Challenges to world agriculture in the 21st Century, Update Agric. Resour. Econ. Univ. Calif. Davis, vol. 4, no. 3, 2001.
 [4] R. D. Grisso, M. M. Alley, P. McClellan, D. E. Brann, and S. J. Donohue, Precision Farming. A Comprehensive Approach, 2009.
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 Authors Profile:
Aishwarya M N, Student at REVA University, Bengaluru, India. Pursuing my 8th semester of B-tech in Computer Science and Engineering.
 Abhishek R, Student at REVA University, Bengaluru, India. Pursuing my 8th semester of B-tech in Computer Science and Engineering.
Prof. Vinay Kumar M, Assistant Professor at REVA University, Bengaluru, India.