Skin defect detection of pomegranates using color texture features and DWT

Various Skin disorders lower the quality of fruits due to environmental stress such as high temperature and solar radiation some other skin disorders are induced by chemical treatments and pathogens. Skin defect detection is important in the development of automatic grading and sorting system for pomegranate, because manual sorting process is very expensive and time consuming to automate this process skin defect can be identified with the help of color texture feature and discrete wavelet transform. For color texture feature analysis, acquired image is transformed into HSI color space, which is further used for generating SGDM matrix. Total 12 texture features were computed for hue (H), saturation (S) and intensity (I) images from each image samples. Then wavelet transform is used to compute statistical features, Total 3 features were computed for R, G & B components of each image samples. Best features were used as an input to Support Vector Machine (SVM) classifier and tests were performed to identify best classification model. Features showing optimal results were mean (99%), variance (99.80), cluster shade (99.88%), cluster prominence (99.88%), Mean intensity (99.81%).