Siamese network‐based computer vision approach to detect papaya seed adulteration in black peppercorns

Abstract

Food adulteration is a growing peril for consumers, traders, and manufacturers worldwide. Food fraud costs the economy a fortune and creates mistrust among consumers and merchants. Black Pepper is a valuable and heavily adulterated spice. This study explores the ability of deep learning coupled with image processing to identify black pepper contaminated with its common adulterant papaya seeds. Prevalent methods work on a relatively small sample, requiring specialization and resources. Our research proposes a system to quickly and economically obtain a method to trace added impurities. It is a problematic venture for one‐shot image classification systems to differentiate between identical entities. Therefore, we implement a Siamese network that is proficient in it. A data set of 2,000 images of each class is created. After experimentation, the training and validation accuracy of 0.96 and 0.92 is obtained, while human eye judgment of the same images gave a correctness score of 0.97. Novelty impact statement A novel non‐destructive, agile, cost‐effective, user‐friendly technique to trace papaya seeds' adulteration in black peppercorns using Artificial Intelligence. The proposed system is better in accuracy than the majority of existing approaches.

Detection of Cumin Seeds Adulteration Based on Thermal Imaging Using Deep Learning Technique

“…the number of classes in the classification [18]. These interpret the result as a forecast between the categories.…”

Application of Machine Vision System in Food Detection

“…For food defect detection, Patel et al ( 17 ) developed a method using monochrome cameras for automatic mango sorting, and result showed that the system’s detection efficiency and accuracy reached 97.88% and 88.75%, respectively. Noor Fatima et al ( 18 ) used industrial cameras combined with deep learning to develop a device for tracking the adulteration of papaya seeds in black pepper. Furthermore, machine vision can identify and classify food products based on their color.…”

Applications of Electronic Nose, Electronic Eye and Electronic Tongue in Quality, Safety and Shelf Life of Meat and Meat Products: A Review

“…Other interesting applications include the use of E-nose for the classification of carrots in terms of surface defects (presence of bad spots, abnormalities, and formation of fibrous roots) [ 15 ] and the discrimination of cheese according to aging period [ 16 ], E-eye to detect adulteration of black peppercorns with papaya seeds [ 17 ], and E-tongue to identify adulterated wine [ 18 ]. All these studies support the emerging role of electronic tools in several lines of research related to food quality.…”