The algorithm in this study involved four main steps: color-based image segmentation using the K-means clustering algorithm, extraction of six geometrical shape parameters from each segmented region, classification with k-Nearest Neighbor (kNN), Radial Basis Function (RBF), and Support Vector Machine (SVM), and combination of classifier outputs using the FuzzySugeno Integral (FSI) method. Experimental results showed that the SVM with a polynomial kernel yields the highest accuracy at 94.4%, and the fuzzy fusion method further improves classification accuracy by up to 9.4%, significantly enhancing the results from a petroleum geology perspective. In advancing mineral identification techniques, [PERSON] et al. [18] developed an intelligent system for real-time and reliable segmentation and identification of minerals in thin sections. Utilizing RGB and HSI color spaces along with texture features, the system operates through a cascade approach comprising two phases: segmentation and identification. This methodology involved artificial neural networks to accurately classify minerals, achieving an overall accuracy of 93.81% for 23 igneous minerals. The system's ability to predict mineral composition and probable target rock types offers significant applications in petrography and NASA Mars explorations. [PERSON] et al. [19] presented an analysis of a vision-based rock type and classification algorithm, demonstrating its efficacy with samples from the Novin limestone mine in Iran. The study achieved classification accuracies of approximately 80% for limestone and 90% for dolomite samples, and effective chemical composition predictions using a multi-layer perceptron neural network, underscoring the potential of this technique in various stages of mining projects. Moreover, further studies by [PERSON] et al. [20] have explored the use of kNN, SVM and multinomial logistic regression, for the improvement of accuracy in mineral classification and geochemical discrimination. In addition, [PERSON] et al. [21] proposed an automated mineral identification system that integrates hyperspectral imaging with deep learning to identify mineral types in preprocessing stages. Also, [PERSON] et al. [22] addressed the challenge of mineral identification in geological analysis, a process traditionally reliant on expert knowledge and specialized equipment. These conventional methods are labor-intensive and often impractical for the identification of associated minerals in natural environments. To overcome this limitation, the researchers proposed a deep learning model based on transformer architecture and multi-label image classification. This model analyzes mineral images and outputs the probabilities of various minerals' presence within a single image. Experimental results on 36 common minerals demonstrated that the model achieves a mean average precision of 85.26%. Additionally, class activation mapping visualizations indicated that the model effectively locates identified minerals, showcasing its potential for more efficient and accurate geological analysis. [PERSON] et al. [23] introduced a method combining photo image features with mineral hardness, employing the EfficientNet-b4 model for feature extraction. Similarly, [PERSON] et al. [24] examined the impact of integrating deep learning with FinTech solutions to enhance mineral identification and promote sustainable natural resource extraction. By employing the EfficientDet architecture and a post-training quantization approach, the researchers achieved significant improvements in model efficiency and accuracy. The quantized EfficientDet model demonstrated superior performance, with a precision of 0.97 and a recall of 0.89, compared to the floating-point model. The findings highlight the potential of FinTech platforms in enabling data-driven, environmentally responsible investment decisions in the mining industry. [PERSON] et al.