A Survey on Machine Learning Model for Building Efficient Agriculture Management System in Cloud Computing
Namita Naganur1, Kuldeep Sambrekar2, Vijay S. Rajpurohit3
1Namita Naganur, Department of Computer Science, KLS, Gogte Institute of Technology, Belagavi (Karnataka), India.
2Kuldeep Sambrekar, Department of Computer Science, KLS, Gogte Institute of Technology, Belagavi (Karnataka), India.
3Vijay S. Rajpurohit, Department of Computer Science, KLS, Gogte Institute of Technology, Belagavi (Karnataka), India.
Manuscript received on 18 April 2019 | Revised Manuscript received on 25 April 2019 | Manuscript published on 30 April 2019 | PP: 1044-1049 | Volume-8 Issue-4, April 2019 | Retrieval Number: D6141048419/19©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Agriculture is considered as a backbone of major countries economic such as India, China, and United states. In these countries good agriculture productivity will aid in attaining better industrial growth. Timely information need to be passed to farmers for obtaining better agriculture productivity. Thus, the geographic information system (GIS) and remote sensing forecasting (RSF) technology deployed different kind of sensor to collect information such as wind, temperature, humidity level and so on and are stored in cloud computing platform. These data can later be analyzed by applying machine learning (ML) technique for forecasting weather related information to farmers. This paper presents a deep rooted survey of various existing agriculture management and ML based model for enhancing agriculture management system (AMS). From survey it can be seen existing access control and machine learning model incurs computation overhead for processing and storing information on cloud platform. Thus, this paper gives a research direction for modelling scalable and cost-effective agriculture management system using ML and cloud computing environment..
Keywords: Access Control Mechanism, Agriculture Management System, Big Data, Cloud Computing, Machine Learning
Scope of the Article: Cloud Computing