under investigation visible on the affected leaves were extracted from their respective images using K-mean. The image analysis was mainly focuses on the extraction of shape features and their color based segmentation. The image analysis technique is done using Gray-level co-occurrence matrix [11]. The affected areas vary in color and texture and are dominant in classifying disease symptoms. So, we have considered both color and texture features for recognition and classification purpose. Picture texture, explained as a function of the spatial variation in pixel intensities (gray values). The use of color features in the noticeable light spectrum provided additional image characteristic features over traditional gray-scale representation. GLCM is a method in which both color and texture features are taken into account to arrive at unique features which represent that
under investigation visible on the affected leaves were extracted from their respective images using K-mean. The image analysis was mainly focuses on the extraction of shape features and their color based segmentation. The image analysis technique is done using Gray-level co-occurrence matrix [11]. The affected areas vary in color and texture and are dominant in classifying disease symptoms. So, we have considered both color and texture features for recognition and classification purpose. Picture texture, explained as a function of the spatial variation in pixel intensities (gray values). The use of color features in the noticeable light spectrum provided additional image characteristic features over traditional gray-scale representation. GLCM is a method in which both color and texture features are taken into account to arrive at unique features which represent that