Hybrid approach for apple fruit diseases detection and classification using random forest classifier

Nowadays, abroad trade has expanded definitely in numerous nations. Plenty fruit products are foreign from alternate countries, for example, oranges, apples and so forth. Manual distinguishing proof of infected fruit is extremely tedious. The utilization of image processing procedures is of outstanding implication for the analysis of agro based applications. In any case, detection of infections in the fruit products utilizing images is still risky because of the regular changes of skin color in distinctive sorts of fruit products. In this paper three normal infections of apple fruit are considered i.e. Apple scab, apple rot and apple blotch. The image processing based proposed methodology is made out of the accompanying some state of the art color and texture features are extracted from the test image, then color and texture features are fused together and random forest classifier is used for diseases classification and if the fruit is infected by any of the one disease then the infected part is segmented using k-means clustering technique. The accuracy of the diseases classification will improve by feature level fusion.