🤖 AI Summary
This study addresses the high subjectivity and inconsistency in emerald grading caused by manual comparison against reference stones. To overcome these limitations, the work proposes the first automated grading framework that integrates image processing with conventional (non-deep learning) machine learning techniques. The system requires only minimal human intervention—placing the gemstone into an imaging chamber—and then autonomously handles image acquisition through to final classification. Key contributions include the creation and public release of the first open dataset comprising 192 emerald images along with pre-extracted features, and the achievement of a 98% grading accuracy, which significantly outperforms existing deep learning approaches. This advancement markedly enhances the objectivity and reproducibility of emerald quality assessment.
📝 Abstract
The grading of gemstones is currently a manual procedure performed by gemologists. A popular approach uses reference stones, where those are visually inspected by specialists that decide which one of the available reference stone is the most similar to the inspected stone. This procedure is very subjective as different specialists may end up with different grading choices. This work proposes a complete framework that entails the image acquisition and goes up to the final stone categorization. The proposal is able to automate the entire process apart from including the stone in the created chamber for the image acquisition. It discards the subjective decisions made by specialists. This is the first work to propose a machine learning approach coupled with image processing techniques for emerald grading. The proposed framework achieves 98% of accuracy (correctly categorized stones), outperforming a deep learning approach. Furthermore, we also create and publish the used dataset that contains 192 images of emerald stones along with their extracted and pre-processed features.