## 1 Introduction
Among them, the coffee granule size distribution [7], obtained using sieves or meshes, is a quantitative analysis. Therefore, coffee granule size distribution is well suited as the input for the grinder burr wear prediction system because coffee granules are ground by the grinder burr with direct contact, consequently meaning that coffee granule sizes are directly affected by grinder burr wear. Image processing [8] that can separate the ground coffee granules and estimate their granule sizes is ideal for quickly obtaining coffee granule size distribution in coffee granule images. The target coffee grinder of the cooperating coffee chain has five grinding settings. It was observed that the coffee granule size distributions of burrs with different wear levels and different grinding settings could be very similar to each other. Therefore, further intelligent processing of the coffee granule size distribution is needed so that the grinder burr wear level can be correctly assessed. A deep learning model [9; 10] derived from neural networks is very suitable for handling this type of classification problem with ambiguity. If the input is the coffee granule size distribution while the output is the wear level of grinder burr, a deep learning model can be trained to classify the wear condition. Because mobile phones and chatbots are now very popular, employees of coffee chain stores can use their mobile phones to take photos of grounded coffee granules and then submit the photos to a server computer for analysis through the chatbot messaging interface. After the analysis is complete, the wear prediction is sent back to employees through the same chatbot messaging interface. LINE [11] is a de facto messaging application in Taiwan and Asia. Therefore, a LINE bot was selected as the chatbot for this application. In this paper, a system is developed using a chatbot messaging interface that allows users to interact with a server computer that is running an image processing and deep learning model, so that remote client users can first take photos of coffee granule and then submit images of coffee granules and text messages to the server computer to trigger burr wear classification programs and obtain analysis results. With the above-proposed remote access server system, every coffee chain shop can replace the coffee grinder burr at the right time using the remote server that mimics the coffee experts' abilities in classifying burr wear to maintain the good taste of coffee without extra implementation cost and training. Consequently, all of the design requirements from the coffee chains can be fulfilled. To implement the proposed system, the following technical issues need to be addressed for a further detailed design. The first technical issue is about a granule image acquisition device with a brightness control and scale calibration to establish a relationship between pixel sizes in the image and physical distances, as the coffee granule size distribution is the input to the deep learning model. Therefore, coffee granule sizes should be properly estimated. With the advancement in camera resolution, measuring the size of objects within images has become more precise and accessible. To accurately convert the sizes of objects in images to real dimensions, details about the camera parameters, as well as the angles and positions during capture, are essential [12]. To more effectively implement the proposed method, commonly encountered coins are applied as reference objects in images. In many applications, special image sampling devices are needed. The devices should be able to fix the shooting distance between the camera and the objects, control the ambient brightness during photography, and have a calibrate function to determine the scale of the image pixels to the actual length.