Nondestructive classification of saffron using color and textural analysis

• Published in: Food science & nutrition Vol. 8; no. 4; pp. 1923 - 1932
• Main Authors: Mohamadzadeh Moghadam, Morteza, Taghizadeh, Masoud, Sadrnia, Hassan, Pourreza, Hamid Reza
• Format: Journal Article
• Language: English
• Published: United States John Wiley & Sons, Inc 01-04-2020; John Wiley and Sons Inc
• Subjects: Classification; Classifiers; Color; Color imagery; Experts; Feature extraction; Image acquisition; Image analysis; Image classification; Image processing; Machine vision; Morphology; Nondestructive testing; Original Research; Quality; Saffron; Support vector machines; Vision systems; Iran; Spain; India; image processing; classification; saffron
• ISSN: 2048-7177; 2048-7177
• DOI: 10.1002/fsn3.1478

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.
Saffron classification based on machine vision techniques as well as the expert's opinion was carried out. Twenty‐one color features and ninety‐nine textural features were extracted using image analysis. The results showed that the support vector machine and Ensemble classifiers were better than other classifiers.