## 4 Discussion
That is why the input vector may not be susceptible to small size errors in coffee granules in our application. However, if the applications need to obtain deterministic size values, such as the applications in [13; 14; 15; 16; 17; 18; 19; 20], the simple scale calibration using a reference object of known size could not be accurate enough. One should use image acquisition systems with pixel to length scale calibration, ambient brightness control, and a fixed shooting distance. From the Monte Carlo dropout analysis for all 120 test data, the averages of max prediction probabilities (confidence scores, 0.75-0.99) are greater than 0.75, and all the standard deviations (uncertainties, 0.05-0.18) are below 0.2. Even considering the worst case, the max prediction probability is still above 0.55. Therefore, even if the selected deep learning model has uncertainties from dropout, it will not affect the consistency of prediction on the same test data in most cases. Consequently, the robustness of the selected deep learning model is assured. Furthermore, the overall average of the prediction accuracy of 12,000 tests performed while running the above Monte Carlo dropout analysis is 96.67%. The effectiveness of the proposed method using a reference coin as a scale calibration, image processing, and the deep learning model are all successfully verified. In experiments, the runtime for image processing is around 1 s while the runtime for burr wear level classification using the deep learning model is less than 0.5 s on a server computer. It only takes less than a minute for users to complete the LINE bot operation and receive the prediction with a long-term average accuracy of 96.67%. This accuracy is high enough for our application because coffee experts told us that when they performed their method twice using the same coffee granules, they had about a 2-3% chance of having inconsistent burr wear predictions. To the best of the authors' knowledge, we have not found another system with a similar concept and function for tool wear classification using the integration of a deep learning model and image processing on the granules produced by tools. Compared with the prediction accuracies of indirect tool wear prediction applications using force signals, vibration signals, acoustic emission signals, current signals, and deep learning related models such as 94.2-99.8% in [29], 98.77-100% in [48], and 87.75% in [49], the accuracy of 96.67% for the method proposed in this paper is not inferior. From the concepts of image acquisition and image data transfer and remote servers, an application to precisely measure particle size and shape in the Baijiu (Chinese liquor) brewing process using a smartphone-based imaging system [14] is similar to our system, but they need a specially built image acquisition platform with an accurate scale calibration of pixel to length. In that paper, without applying artificial intelligence, smartphones on a custom image acquisition platform can take photographs at a fixed distance using a custom designed APP to transfer the photos and communicate with the server and display the results. However, no design or operation details of that APP were provided. As the APP is custom designed, there is design overhead, and users need to use extra memory to install this APP on their smartphones and learn how to use it. In our system, we use the LINE bot to perform the same job, and no extra memory or user training are needed because LINE is already the dominant messaging APP on smartphones and almost everyone uses it daily in Taiwan. The morphological image processing for background extraction and noise removals in this paper is similar to ours. After that, their image processing departs from ours. They separate particles using a flood fill algorithm to label all pixels in the same connected components and then split the image into different connected areas and manually set upper and lower thresholds to only display the particles in the desired size range.