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Front. Hortic.
Sec. Postharvest Physiology, Management and Technology
Volume 2 - 2023 | doi: 10.3389/fhort.2023.1225683

Digging for Gold: Evaluating the Authenticity of Saffron (Crocus sativus L.) via Deep Learning Optimization

  • 1South Valley University, Egypt
  • 2Data Scientist at Epsilon AI, Cairo, Egypt, Egypt
  • 3Food Quality Laboratory, Beltsville Agricultural Research Center, Agricultural Research Service (USDA), United States

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Saffron is one of the most coveted and one of the most tainted products in the global food market. A major challenge for the saffron industry is the difficulty to distinguish between adulterated and authentic dried saffron along the supply chain. Current approaches to analyzing the intrinsic chemical compounds (crocin, picrocrocin, and safranal) are complex, costly, and time-consuming. Computer vision improvements enabled by deep learning have emerged as a potential alternative that can serve as a practical tool to distinguish the pureness of saffron. In this study, a deep learning approach for classifying the authenticity of saffron is proposed. The focus was on detecting major distinctions that help sort out fake samples from real ones using a manually collected dataset that contains an image of the two classes (saffron and non-saffron). A deep convolutional neural model MobileNetV2 and Adaptive Momentum Estimation (Adam) optimizer were trained for this purpose. The observed metrics of the deep learning model were: 99% accuracy, 99% recall, 97% precision, and 98% F-score, which demonstrated a very high efficiency. A discussion is provided regarding key factors identified for obtaining positive results. This novel approach is an efficient alternative to distinguish authentic from adulterated saffron products, which may be of benefit to the saffron industry from producers to consumers and could serve to develop models for other spices.

Keywords: Classification, Convolutional Neural Networks (CNN), Deep Neural Networks (DNN), Red Spices, saffron

Received: 19 May 2023; Accepted: 29 Aug 2023.

Copyright: © 2023 Elaraby, Ali, Zhou and Fonseca. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence: Prof. Ahmed Elaraby, South Valley University, Qena, Egypt