A Comprehensive System for Accurate Identification of Gemstones from Corundum, Topaz, and Chrysoberyl Families Using Machine Learning Techniques

“Gemstones, mineral stones formed through geological processes with a hardness above 7 Mohs, hold significant value in the gemstone industry” [1]. This research introduces a meticulously designed software system to address challenges in the gemstone industry. The system excels in identifying gemstones from the Corundum, Topaz, and Chrysoberyl families, focusing on the key factors of gemstone valuation known as the “4 Cs” (color, clarity, cut, and carat weight). The research aims to provide an efficient and precise solution for gemstone identification, offering transformative approaches to industry concerns and enhancing the market desirability of these gemstones. Our system utilizes machine learning, specifically a pre-trained convolutional neural network (CNN), with extensive data and image processing techniques like cropping, resizing, flipping, and zooming. These enhance the CNN's training process for 722 previously unseen gemstone images, categorizing them by variety, color, and clarity. The system aims to efficiently and reliably identify and differentiate gemstones, with broad applications in gemology, the jewelry industry, and mineralogy. This research represents not only a technological advance but also a practical solution to streamline gemstone recognition and classification across various domains.