  Identification of minerals in the field is a task that is wrought with many
challenges. Traditional approaches are prone to errors where there is no enough
experience and expertise. Several existing techniques mainly make use of
features of the minerals under a microscope and tend to favour a manual feature
extraction pipeline. Deep learning methods can help overcome some of these
hurdles and provide simple and effective ways to identify minerals. In this
paper, we present an algorithm for identifying minerals from hand specimen
images. Using a Convolutional Neural Network (CNN), we develop a single-label
image classification model to identify and categorise seven classes of
minerals. Experiments conducted using real-world datasets show that the model
achieves an accuracy of 90.75%.
