Treffer: Machine Learning Approaches for Real-Time Mineral Classification and Educational Applications.

Featured Application: This real-time mineral classification system is designed for an interactive quiz game where users identify minerals in response to game-generated questions. Players present a mineral to the camera, and the system recognizes and evaluates their answers, seamlessly combining education with entertainment. By showcasing the everyday uses of minerals, this application promotes awareness and sparks interest in geology and natural sciences through an engaging and interactive learning experience. The main objective of the present study was to develop a real-time mineral classification system designed for multiple detection, which integrates classical computer vision techniques with advanced deep learning algorithms. The system employs three CNN architectures—VGG-16, Xception, and MobileNet V2—designed to identify multiple minerals within a single frame and output probabilities for various mineral types, including Pyrite, Aragonite, Quartz, Obsidian, Gypsum, Azurite, and Hematite. Among these, MobileNet V2 demonstrated exceptional performance, achieving the highest accuracy (98.98%) and the lowest loss (0.0202), while Xception and VGG-16 also performed competitively, excelling in feature extraction and detailed analyses, respectively. Gradient-weighted Class Activation Mapping visualizations illustrated the models' ability to capture distinctive mineral features, enhancing interpretability. Furthermore, a stacking ensemble approach achieved an impressive accuracy of 99.71%, effectively leveraging the complementary strengths of individual models. Despite its robust performance, the ensemble method poses computational challenges, particularly for real-time applications on resource-constrained devices. The application of this methodology in Mineral Quest, an educational Python-based game, underscores its practical potential in geology education, mining, and geological surveys, offering an engaging and accurate tool for real-time mineral classification. [ABSTRACT FROM AUTHOR]

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