Improving Gemstone Identification Accuracy Using Convolutional Neural Networks

Accurate identification of gemstones is crucial in various fields, including gemology research, jewelry appraisal, and consumer protection, where authenticity verification is paramount to prevent fraud and ensure trust in the gem trade. Traditional methods of gemstone identification heavily depend on the subjective expertise of gemologists, which can lead to inconsistencies and is often time-consuming, particularly when dealing with rare or complex gemstones. This study presents the development of a Convolutional Neural Network (CNN) model designed for the automated classification of gemstones. The model leverages advanced image preprocessing techniques, including resizing, data augmentation, and normalization, to enhance the robustness of the classification process. Trained on a meticulously curated dataset comprising images of 15 distinct gemstone types, the CNN model achieved a notable validation accuracy of 88%. These results highlight the potential of CNN-based approaches as a reliable and efficient alternative to traditional gemstone identification methods, offering significant improvements in speed, consistency, and scalability.