A Remote Access Server with Chatbot User Interface for Coffee Grinder Burr Wear Level Assessment Based on Imaging Granule Analysis and Deep Learning Techniques

Abstract

Coffee chains are very popular around the world. Because overly worn coffee grinder burrs can downgrade the taste of coffee, coffee experts and professional cuppers in an anonymous coffee chain have developed a manual method to classify coffee grinder burr wear so that worn burrs can be replaced in time to maintain the good taste of coffee. In this paper, a remote access server system that can mimic the ability of those recognized coffee experts and professional cuppers to classify coffee grinder burr wear has been developed. Users only need to first upload a photo of coffee granules ground by a grinder to the system through a chatbot interface; then, they can receive the burr wear classification result from the remote server in a minute. The system first uses image processing to obtain the coffee granules’ size distribution. Based on the size distributions, unified length data inputs are then obtained to train and test the deep learning model so that it can classify the burr wear level into initial wear, normal wear, and severe wear with more than 96% accuracy. As only a mobile phone is needed to use this service, the proposed system is very suitable for both coffee chains and coffee lovers.