## 4 Discussion
By leveraging this framework, researchers could strike a more efficient balance between data augmentation and model performance, potentially reducing the reliance on extensive empirical testing and streamlining the development process for mineral classification models. The analysis reveals that all three models--VGG16, Xception, and MobileNet--demonstrated strong performance in mineral classification, with each offering unique strengths suited to specific applications. MobileNet emerged as the most efficient model, achieving the highest overall accuracy (98.98%) and excelling in both precision and recall across most mineral types, while also being lightweight and fast, making it ideal for resource-constrained or real-time applications. Xception balanced high accuracy (98.69%) with moderate computational efficiency, showcasing robust feature extraction capabilities and strong classification performance. Although VGG16 lagged slightly in accuracy (96.51%), its ability to extract detailed features and achieve minimal loss indicates its reliability for tasks prioritizing precision. Overall, the MobileNet model emerged as the best performing model with the highest accuracy and lowest loss, combining efficiency and accuracy, making it ideal for real-time applications such as the mineral classification quiz game. The comparative analysis highlights the strengths of each model, with MobileNet excelling in efficiency and accuracy, making it ideal for resource-constrained or real-time applications, while Xception demonstrates superior feature extraction and classification performance, better suited for tasks requiring detailed analysis. However, deploying heavier models like Xception in environments with limited computational resources or strict real-time requirements poses significant challenges. To bridge the gap between performance and efficiency, innovative strategies such as model optimization--quantization, pruning, and knowledge distillation--can be employed alongside hardware accelerators like GPUs or TPUs. These techniques effectively reduce model size and computational demands, enabling the deployment of advanced models like Xception in constrained settings without compromising their accuracy or feature extraction capabilities, thereby expanding their applicability to a wider range of real-world scenarios. The Grad-CAM visualizations revealed key insights into the feature extraction capabilities of the models and their alignment with the distinctive traits of various minerals. For Aragonite, known for its sharp crystalline formations, both MobileNet and Xception accurately focused on the central structure, with MobileNet striking a balance between capturing fine details and broader contextual regions. In the case of Gypsum, characterizedby its intricate rosette-like patterns, Xception's precise focus effectively highlighted the unique structures, while MobileNet provided a balanced approach, making both models highly effective for capturing its distinctive features. For Obsidian, with its smooth texture and darker tones, MobileNet excelled by emphasizing broader textural features, while Xception delivered precision by targeting core edges. Similarly, Pyrite's reflective crystalline structure was confidently captured by both models, with MobileNet offering enhanced robustness through its adaptability to broader feature coverage. These findings emphasize MobileNet's ability to balance precision with contextual awareness, making it particularly adept at capturing the diverse and complex physical characteristics of minerals in real-world scenarios. Concerning the stacking ensemble approach, it significantly enhances classification performance by combining the complementary strengths of base models, achieving outstanding precision, recall, and F1-scores across diverse mineral classes, with an overall accuracy of 99.71%, demonstrating remarkable robustness.