USK-COFFEE Dataset: A Multi-Class Green Arabica Coffee Bean Dataset for Deep Learning

Abstract

Coffee is one of the plantation commodities that plays a big role in the world economy. According to the classification of coffee, each type of coffee has various shapes and textures. Traditional human visual sorting of coffee beans is time-consuming, labor-intensive, and may result in low-quality coffee due to work stress and exhaustion. The contribution of this paper is twofold. First, a new dataset, called USK-Coffee, which contains a total of 8.000 images and is divided into 4 classes, is created and made publicly available. To the best of our knowledge, the USK-Coffee dataset is currently the most comprehensive green coffee bean dataset. Second, this study aims to offer a lightweight and understandable intelligent coffee bean sort accurately system that uses deep learning (DL) to assist farmers in sorting green bean arabica by variety. To be specific, this paper presents a baseline for classification performance on the dataset using the benchmark deep learning models, MobileNetV2, and ResNet-18. These models achieved an average classification accuracy of 81.31% and 81.12%, respectively. The dataset is available at: http://comvis.unsyiah.ac.id/usk-coffee/