Abstract:
"The gemstone business is filled with difficulties, particularly the widespread problem of
distinguishing genuine gemstones from counterfeit imitations, which are frequently carefully
made to look like the real thing. This deceitful method erodes consumer trust and confidence,
requiring strong solutions to differentiate authentic gemstones from counterfeit ones. Moreover,
the task of differentiating between different gem cuts presents a substantial difficulty, hence
adding complexity to the buying process for purchasers.
To address these urgent concerns, thorough research project focused on solving the dual
problems of gemstone identification and shape recognition. By utilising advanced image
processing techniques and machine learning approaches, specifically, Convolutional Neural
Networks (CNNs), created a complex classification system that can effectively identify the name
of the gemstone with high accuracy. In addition, by employing novel methods for identifying
gem shapes, which involve the utilisation of shape recognition and Photogrammetry principles,
the aim is to equip consumers with the necessary information to make well-informed purchasing
choices.
This research highlights the significance of technical innovation in reducing fraudulent practices
in the gem industry. The goal is to improve transparency and rebuild trust in the gemstone
industry by offering purchasers dependable tools for gemstone authentication and cut
recognition. This will create a fair and secure trading environment for all parties concerned."
