Classification of Normal and Affected (Decayed) Fruit Images - P. T, Priyanka; Angadi, S. A (International Journal of Computer Sciences and Engineering, 2014)
Digital image processing has its applications in the field of Agriculture. Many techniques of image processing can be applied to detect plant and fruit diseases. One such approach is using Neural Networks. Many people have worked on detecting plant diseases using image processing, but reported works are very less in detecting fruit diseases. In the present work reduced feature set based approach is used for recognition and classification of images of fruits into normal and affected. Color and texture features are used to differentiate between normal and affected (decayed) fruits of all types. The RGB (Red Green Blue) color features and GLCM (Gray-level Co-occurrence Matrix) texture features are reduced. The reduced feature set comprises of most appropriate features. Neural Network classifier is used to classify normal and affected (decayed) fruit images. The combination of reduced color and reduced texture features are able to prove the effectiveness in classifying normal and affected (decayed) fruits images. The work finds application in developing a machine vision system in agriculture and horticulture fields.