EVALUATION AND CLASSIFICATION OF MATURITY OF FRESH TOMATO FRUITS USING COLOR-BASED IMAGE ANALYSIS TECHNIQUES

Abstract

In this study, a simple machine vision system was developed for sorting three maturity classes of tomatoes grown in Ethiopia. For the sorting analysis, RGB color features were extracted from each class of tomato images. Six different color features were calculated from RGB color space. An artificial neural network classifier with Back propagation method was tested. The input layer consists of six color features, the hidden layer consists of 40 nodes and the output layer consists of three nodes representing three tomato classes (green, pink and red). The best sorting accuracies in testing data set was 76% for all the three classes (green, pink and red) of tomato images. That means the overall sorting accuracy was 76%. Finally, based on the obtained results, a tomato sorting machine can be designed to categorize 3 colors of tomatoes decreasing human labor and to reducing sorting time