Abstract
Climate Smart Agriculture incorporates information on soils, bothers, maladies, costs and different variables to increment illustrative power. Creating atmosphere strong horticulture is fundamental to accomplishing future sustenance security and environmental change objectives. Through CSA application the ranchers can anticipate crop type (crop exhortation) which is suitable for accessible condition. Climate Smart farming has been generally used to express agricultural practices that will increment horticultural efficiency and nourishment security and to foresee rural items. CSA application have Climate information (like: Max Temp, Min Temp, Humidity, Rain fall, daylight, wind course, wind speed), Fertilizer and Soil Data's (like, Black Soil, Red Soil).The review aim to help farmers better adapt to temperature extremes, droughts or excess water in fields so that they can make better decisions for the environment and maximize production or profits. The data collection is an important role in the work process. Enabling farmers to head massive amounts of data collected through sensors to predict the best time to plant, what type of seed to use, and where to plant in order to improve yields, cut operational costs, and minimize environmental impact.Big data analytics provide new ways for businesses updates and requirement for updating and government to analyze unstructured data. Now a day, big data is one of the most important and challenging point in information technology world. It is executing very important role in future. Big data changes the way of world for management and use big amount of data Keywords : Climate Smart Agriculture, Big Data Analytics and Hadoop DOI : 10.7176/CEIS/10-6-01 Publication date :July 31 st 2019
Highlights
The use of technology in agriculture has continued to grow since the early part of the 20th century, when the industry shifted from the horse-drawn plow to mechanized tractors
The ability to generate, capture, and store data in the agricultural industry has continued to grow with the use of mobile technology and data management software
Emerging technologies for Big Data realtime analytics include technologies for collection and aggregation of real-time data for Hadoop, in-memory analytic systems, and real-time analytics applications for processing of data stored in Hadoop
Summary
The use of technology in agriculture has continued to grow since the early part of the 20th century, when the industry shifted from the horse-drawn plow to mechanized tractors. The ability to generate, capture, and store data in the agricultural industry has continued to grow with the use of mobile technology and data management software. This review is intended to address research challenges in Agriculture sector, a new and interdisciplinary research area that spans the Big Data Analytics, Data Science tools, Hadoop, R and Machine learning algorithms.
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