Machine learning applied to emerald gemstone grading: framework proposal and creation of a public dataset
It addresses the subjectivity and manual effort in gemstone grading for gemologists, but the dataset is small and the method is incremental.
The paper proposes a machine learning framework for automated emerald gemstone grading, achieving 98% accuracy on a new public dataset of 192 images, outperforming a deep learning baseline.
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.