A Computer Vision Based System for Classification of Chemically and Naturally Ripened Mangoes

Abstract

Recently there was news indicating that mangoes might cause cancer. The news was based on the fact that mangoes were being artificially ripened using a chemical- calcium carbide and Ethrel, a well- known carcinogenic. The consumers hence have to be careful in buying the mangoes. In this study, we have proposed a model for classification of artificially and naturally ripened mangoes using k-NN and SVM classifiers. In order to improve the efficacy of the model, color space features such like RGB, HSV, L*a*b are extracted. Along with the color space features, 14 Haralick texture features are also extracted here. An mango is automatically segmented in an image using modified K-means clustering segmentation method. For the experimental study, mangoes of 2 varieties such as Badami and Raspuri have been taken. In each variety, three different classes of ripened mangoes are taken such as naturally and in chemical, two artificial ripening treatments were applied like calcium carbide and Ethrel solution. The obtained experimental result in terms of F-measure is ranging from 64% to 84% for two different varieties of mangoes using two different chemicals. Further this proposed model can be implemented for different variety of mangoes.