MineralVisio: A Deep Learning Based Mineral Identification System

Mineral identification is a very challenging task. Existing approaches are expensive, involve human expertise and therefore are prone to errors. To overcome this challenge, deep learning technique is utilized to identify the minerals into relevant categories. The advantage of using Deep learning approach is that it is cost effective and also reduces the error. The MineralVisio application as proposed in the research aims to create an identification tool for Silicate class of minerals that has 7 minerals, using Convolutional Neural Network (CNN) and utilizing the VGG16 and VGG19 architectures. The results of the study show that VGG16 had better accuracy as compared to VGG19. Here, along with the overall accuracy of the model, precision, recall and F1 score of each category has been computed to evaluate the performance of the model. Based on the findings, the research suggests the use of VGG16 for mineral identification in the mining industry. Overall, the MineralVisio initiative can be extended to several other mineral classes. It has the potential to revolutionize mineral identification, dramatically improving the mining industry's efficiency and accuracy.