An agriculture survey of big data mining applications

The data is collected from various sources like laboratory reports, agriculture information web pages, and expert recommendation for the developed framework. After the collection of raw data, the irrelevant or the redundant data that is also known as the noise, should be removed. The next step is to extract the features from cleaned data, normalization of data is done in order to remove the technical variations. Once normalization is complete the data is uploaded on HDFS and save in a file that is supported by Hive. Thus classified data is finally located on the specific place. In the next step HiveQL is used to analyze agriculture data based on features and then prioritize the outcome based on crop disease symptoms and in the last a high priority solution is recom¬mended. In the paper prioritize outcomes are useful for agriculture officers, researchers to easily understand, and helpful for recommending a solution based on evidence from historical data. The major aspire of this paper is to make a study on the concept Big data and its application in data mining. The paper mainly concentrating different types of big data and its application in knowledge discovery.