“Gemo”: An AI-Powered Approach to Color, Clarity, Cut Prediction, and Valuation for Gemstones

Abstract

“Gemo” is an AI-powered smartphone application that aims to improve the gem industry by replacing human-based approaches with computer-based ones. A mix of well-trained machine learning models that are capable of color identification, cut projection, recommendation, and pricing prediction is competent in offering experience and information to the industry. Traditional gem industry predictions are often subjective and inaccurate due to reliance on human labor. Erroneous output caused financial loss. Gemo is developed to overcome these problems by applying Artificial intelligence-based feature identification of gemstones and leads to expect more accurate and real-time results. Integration of Machine learning, Artificial Intelligence, and Natural Language Processing based technology, boosted the realism of the gem analysis process. “Gemo” is applied via advanced machine learning-based algorithms that capture the features of color, clarity shape, and intrinsic attributes of gemstones. Color detection model extraction of the Hue, Saturation, and Value (HSV) colors from gemstone pictures, delivering a cutting-edge and accurate approach to color recognition. The cut prediction approach lowers subjectivity and inaccuracy during the prediction of the cut by employing 3D image processing methods. The recommendation model gathers human preferences and forecasts the optimum solution using Natural Language Processing (NLP). Lastly, the valuation model utilizes the 4Cs features to provide a pricing range for the gemstones, resulting in a complete and advanced gemstone analysis system. The "Gemo" model, which integrates multiple ML-based models, increases the criteria for the gemstone sector. This will help gem experts excel and increase industry competitiveness.