Grading and fraud detection of saffron via learning-to-augment incorporated Inception-v4 CNN. (May 2023)

• Record Type: Journal Article
• Authors: Momeny, Mohammad; Neshat, Ali Asghar; Jahanbakhshi, Ahmad; Mahmoudi, Majid; Ampatzidis, Yiannis; Radeva, Petia
• Is Part Of: Food control. Volume 147(2023)
• Publication Date: 2023-05
• Subjects: Saffron -- Quality control -- Fraud detection -- Data augmentation -- Deep learning -- Noise; Food -- Quality -- Periodicals; Food -- Analysis -- Periodicals; Food handling -- Periodicals; Food industry and trade -- Quality control -- Periodicals; Aliments -- Industrie et commerce -- Qualité -- Contrôle -- Périodiques; Aliments -- Qualité -- Périodiques; Aliments -- Analyse -- Périodiques; Hygiène alimentaire -- Périodiques; Food -- Analysis; Food handling; Food -- Quality; Periodicals; Electronic journals; 664.07
• DOI: 10.1016/j.foodcont.2022.109554
• Languages: English
• ISSNs: 0956-7135

Abstract

Abstract: Saffron is a well-known product in the food industry. It is one of the spices that are sometimes adulterated with the sole motive of gaining more economic profit. Today, machine vision systems are widely used in controlling the quality of food and agricultural products as a new, non-destructive, and inexpensive approach. In this study, a machine vision system based on deep learning was used to detect fraud and saffron quality. A dataset of 1869 images was created and categorized in 6 classes including: dried saffron stigma using a dryer; dried saffron stigma using pressing method; pure stem of saffron; sunflower; saffron stem mixed with food coloring; and corn silk mixed with food coloring. A Learning-to-Augment incorporated Inception-v4 Convolutional Neural Network (LAII-v4 CNN) was developed for grading and fraud detection of saffron in images captured by smartphones. The best policies of data augmentation were selected with the proposed LAII-v4 CNN using images corrupted by Gaussian, speckle, and impulse noise to address overfitting the model. The proposed LAII-v4 CNN compared with regular CNN-based methods and traditional classifiers. Ensemble of Bagged Decision Trees, Ensemble of Boosted Decision Trees, k-Nearest Neighbor, Random Under-sampling Boosted Trees, and Support Vector Machine were used for classification of the features extracted by Histograms of Oriented Gradients and Local Binary Patterns, and selected by the Principal Component Analysis. The results showed that the proposed LAII-v4 CNN with an accuracy of 99.5% has achieved the best performance by employing batch normalization, Dropout, and leaky ReLU. Highlights: The learning to augment strategy was used for saffron fraud detection and grading. Several AI-enhanced techniques were compared to improve classification accuracy. Corrupted images were used to address the models' overfitting problem. The LAII-v4 CNN achieved a classification accuracy of 99.5%.