Food Science and Nutrition, Volume (8), No (4), Year (2020-2) , Pages (1923-1932)

Title : ( Nondestructive classification of saffron using color and textural analysis )

Authors: morteza mohammadzadeh moghaddam , Masoud Taghizadeh , Hassan Sadrnia , Hamid Reza Pourreza ,

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Saffron classification based on machine vision techniques as well as the expert\\\\\\\'s opinion is an objective and nondestructive method that can increase the accuracy of this process in real applications. The experts in Iran classify saffron into three classes Pushal, Negin, and Sargol based on apparent characteristics. Four hundred and forty color images from saffron for the three different classes were acquired, using a mobile phone camera. Twenty-one color features and 99 textural features were extracted using image analysis. Twenty-two classifiers were employed for classification using mentioned features. The support vector machine and Ensemble classifiers were better than other classifiers. Our results showed that the mean classification accuracy was up to 83.9% using the Quadratic support vector machine and Subspace Discriminant classifier.


, classification, image processing, saffron
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author = {Mohammadzadeh Moghaddam, Morteza and Taghizadeh, Masoud and Sadrnia, Hassan and Pourreza, Hamid Reza},
title = {Nondestructive classification of saffron using color and textural analysis},
journal = {Food Science and Nutrition},
year = {2020},
volume = {8},
number = {4},
month = {February},
issn = {2048-7177},
pages = {1923--1932},
numpages = {9},
keywords = {classification; image processing; saffron},


%0 Journal Article
%T Nondestructive classification of saffron using color and textural analysis
%A Mohammadzadeh Moghaddam, Morteza
%A Taghizadeh, Masoud
%A Sadrnia, Hassan
%A Pourreza, Hamid Reza
%J Food Science and Nutrition
%@ 2048-7177
%D 2020