Chicken meat freshness identification using the histogram color feature

Identifying the chicken meat freshness level is necessary since it involves the quality of the meat consumed. This research aims at identifying the freshness level of chicken meat based on the histogram color feature. The histogram color feature used is Red, Green, and Blue color (RGB) channel. RGB histogram value acquired from image sample dataset of chicken breast meat. First Order Statistical Method is used to reduce the color feature dimension such as Mean, Max, and Sum. The value is then classified using Naïve Bayes Classifier, Support Vector Machines (SVM) Classifier and C4.5 Decision Tree. The Classification method compared for analyzing their accuracy. The freshness of chicken meat level defined in three class, fresh, medium, and old. The chicken meat labeled as fresh from 0 to 4 hours after slaughtered, 4-6 hours labeled as medium, and more than 6 hours labeled as old. The result of color histogram feature by Naïve Bayes method shows 33.33%, Support Vector Machine (SVM) shows 58.33%, whereas C4.5 decision tree method shows 50% classification accuracy. The classification process of the chicken meat's freshness level based on the color histogram feature suggests using Support Vector Machine (SVM) method which indicates the highest classification accuracy of the experiments result.