Saffron is the most expensive spice in the world which is traded internationally. Hence, Saffron adulterants is always a challenging issue. It should be noted that traditional methods that are being used to solve this problem, are costly and time-consuming. In this study, for the first time, Saffron adulterants was detected using a deep neural network and RGB photos taken under uncontrolled/unstructured conditions. There were five classes of fake Saffron include mixed saffron stamen and dyed straw, mixed stigma and stamens, dyed orange blossom, Safflower and dyed stamens, and two classes of genuine Saffron consisting of freeze-drying and microwave- drying saffron used to design, develop and optimize a Convolutional Neural Network (CNN). 60% of the images were used to train and optimize the neural network model. For preventing over-training, the number of images used in the training phase increased by applying the augmentation method. The general structure of the model to detect saffron adulterants consisted of multiple convolutional (input layer), pooling layers and fully-connected layers, respectively. In this study, five structures (S1, S2, S3, S4 and S5) were proposed for CNN using Batch Normalization methods. Additionally, this paper compared these five structures with Popular Convolutional Networks consisted of VGG11, ResNet18, ResNet50, Inception V3 and DarkNet53. According to the results, the best configuration was S5 that achieved the accuracy of 99.8% and the cross-entropy loss of 0.019. Although the time process of this structure was 36.26 ms, the shortest time process was 19.38ms related to the S1. This number was more than 300 ms for Popular CNN. Based on the results of this study, imaging without restrictions with CNN can detect Saffron adulterants.

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