In the material science industry field, there are a variety of applications for the mass production of graphene. Some examples are transparent conductor, flexible electronics, and mechanical composites. Due to this interest, many research groups study the CVD-synthesized method of graphene 9 growth. In order to centralize a repository of the graphene data, software tools are developed on 10 nanoHUB. One of the main features of the analysis tool includes determination of the graphene 11 coverage of SEM images. The research proposed in this study improves the existing template 12 matching method of the graphene coverage by generating a model that automatically detects the 13 graphene in an image. With the constructed dataset, a supervised learning approach and machine 14 vision techniques are applied to the model. The model will be trained to automatically observe the 15 percent of graphene coverage of a SEM image.