Open AccessNondestructive classification of saffron using color and textural analysis
Author(s) -
Mohamadzadeh Moghadam Morteza,
Taghizadeh Masoud,
Sadrnia Hassan,
Pourreza Hamid Reza
Publication year - 2020
Publication title -
food science and nutrition
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.614
H-Index - 27
ISSN - 2048-7177
DOI - 10.1002/fsn3.1478
Subject(s) - artificial intelligence , support vector machine , pattern recognition (psychology) , linear discriminant analysis , quadratic classifier , computer science , subspace topology , classifier (uml) , contextual image classification , random subspace method , computer vision , image (mathematics)
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.