A Big Data Analytics Framework in Climate Smart Agriculture

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 31st 201

A Machine Learning Framework to Predict Determinant Factors of Seeds

In this paper, we audit the machine learning apparatuses for foreseeing determinant components of seeds. We depict this issue regarding Big Data, ANN, Hadoop and R. We consider Machine-learning techniques especially suited to forecasts dependent on existing information, yet exact expectations about the far off future are frequently on a very basic level unthinkable. Farming is an industry where recorded and current information flourish. This survey researches the various information sources accessible in the horticultural field and dissects them for utilization in Seed determinant factor Predictions. We recognized certain relevant information and researched techniques for utilizing this information to improve forecast inside the farming action