Paper Title
Region Based Segmentation with Hybrid Classification Method for Plant Disease Detection

Abstract
Image processing is majorly utilized for diagnosing the diseases occurred on plants due to the deployment of complex data in input. Various stages are executed to detect the infections in plants. This task is accomplished using diverse algorithms. The former work suggests a SVM (Support Vector Machine) model for diagnosing the infection. This research work projects a voting system to improve several evaluation components like accuracy, precision and recall generated through the former work. MATLAB is executed to simulate the projected model. An analysis is conducted on the results with respect to some metrics. The outcomes indicated that the projected model performed more effectively in contrast to the conventional techniques on the basis of accuracy, precision and recall. Keywords – Plant Disease, GLCM, K-mean, SVM, Voting Classifier