Big data analytics framework to identify crop disease and recommendation a solution

Due to technology the term data is replaced by transforming big data in many fields. Rapidly advancements in the technology causes agricultural data enter into the era of big data. Traditional tools and techniques are unable to store and analyze this massive amount of data. To store and analyze this type of data parallel computing and analyze paradigm is required. Big data analytic is used as a solution to this. In the paper big data analytic Agriculture framework is developed that identify disease based on symptoms similarity and recommend a solution based on high similarity. To achieve this objective Hadoop and Hive tools has been used. The data is collected, cleansed and normalized. Data is collected from laboratory reports, web sites etc. then cleansing of data is done that is important information is extracted from unstructured redundant data. In the next step normalization is done that is features are extracted from cleaned data. Normalized data is uploaded on HDFS and save in a file supported by hive. HiveQL is a SQL like query language and used to analyze the agricultural data. It finds out disease name based on crop disease symptoms and purpose a solution based on evidence from historical data. Result is represented in form of graphs that will useful for recommending a solution that is highly used or high symptoms similarity.