MINERAL IDENTIFICATION USING ENSEMBLE OF HANDCRAFTED AND DEEP LEARNING FEATURES WITH XGBOOST

The industry that utilizes beach sand minerals containing titanium zirconium and other strategic elements requires precise classification of mineral grains. The identification techniques commonly used today take long periods to complete and need experts to interpret them while producing results that lack consistency. A well-annotated MINET (Mineral Identification NETwork) dataset of high-resolution mineral grain images serves this study to develop a powerful automated classification machine learning pipeline which addresses previous limitations. The proposed framework uses both handcrafted texture color and shape features with deep ResNet50 model features from extracted representations. These joint representations produce an advanced system which improves the identification of intricate mineral formations. Our framework uses XGBOOST as its classifier to show how features drawn from both handcrafted and deep learning extraction boost automated petrography systems and strengthens MINET's position as a critical benchmark for mineral recognition intelligence.