A HYBRID ALGORITHM IN BIG DATA FRAMEWORK FOR PEST CLASSIFICATION

Agriculture is an intuitive space which is usually transformed from one generation to another generation. In agriculture there are many problems which effect the crop growth out of which pests attack is a complex threat. One of the major causes of pest's attack is unpredictable weather conditions, soil quality and natural calamities. Some may attack due to poor seed quality. Protection of crops become a foremost confront in agriculture, among them predicting the pest attack is one of the important defy. The increase demand for food and changes in climate, policy makers and technology force like Big data is used by industry exports to take assistants. Where for analysis the clouded data is collected which is integrated with larger amount of data is taken to determine patterns to price the models. The data set is trained and predicted in computer by involving an algorithm called Machine learning. This ML will classify and predict the types of pest attacks on the crops during different climatic conditions. By time prediction and classification of the pest is done to understand and protect the crops effectively to allow farmers to learn. In this context, a new algorithm called hybrid machine learning algorithm is proposed which is made by comparative study of individual algorithms to attain accurate pest classification of cotton data set. The proposed functionalities improvise the performance when compared to individual algorithms and it helps to analyse the classification to the maximum specification.