Classifying Minerals using Deep Learning Algorithms

• Published in: IOP conference series. Earth and environmental science Vol. 1032; no. 1; pp. 12046 - 12060
• Main Authors: Singh, Tajendra, Jhariya, D.C., Sahu, Mridu, Dewangan, Pankaj, Dhekne, P.Y.
• Format: Journal Article
• Language: English
• Published: Bristol IOP Publishing 01.06.2022
• Subjects: Algorithms; Artificial neural networks; Biotite; Chemical bonds; Chemical composition; Classification; Conformation; Convolutional neural network; Deep learning; Gems; Image classification; Image processing; Image segmentation; Keras; Learning algorithms; Machine learning; Minerals; Neural networks; Object recognition; Physical properties; Quartz; Sandstone; TensorFlow
• ISSN: 1755-1307; 1755-1315; 1755-1315
• DOI: 10.1088/1755-1315/1032/1/012046

A mineral is an inorganic substance that occurs in nature with specific chemical content and an ordered atomic positioning. Minerals are identified by their physical properties. Minerals’ physical properties are related to their chemical composition and bonding. Quartz is extremely valuable economically. Valuable minerals and some examples of gemstones are citrine, amethyst, quartz with smoky texture and quartz of rose color can be said as rose quartz are some examples of gemstones. Sandstone, primarily composing quartz, is the most used building stone. Biotite has limited number of applications for commercial use. Deep learning is the subset of machine learning. It is based on self-learning and improvement through the examination of computer algorithms. TensorFlow library of machine learning combines a number of different algorithms and models which allows users to build deep neural networks for projects/model such as image recognition/classification and many more. Image Classification is the assignment of one label from a fixed set of categories to an input image. In this paper Convolutional neural networks (CNNs) are used primarily for image processing, classification, segmentation, and other auto-correlated data. This paper will explain the techniques and explanation for classifying minerals images using a deep learning algorithm called a convolutional neural network. Identifying minerals on a field is a tedious activity and requires a lot of information and conformation here with the help of deep learning algorithms we made a deep learning model which has all its feature already embedded in it that can be used to classify minerals with a reasonable accuracy furthermore in future it can be made more accurate and fit accordingly to the conditions.