## 1 Introduction
Among them, convolutional layers perform a dot product of the convolution kernel for obtaining feature maps; pooling layers to further downsample the feature maps, reduce its dimensionality, and retain the most important information; and fully connected layers for high-level reasoning and decision-making based on the extracted features. Since then, other improved and optimized methods inspired by AlexNet have been developed, such as VGG [34], ResNet [35], Inception [36], and so on. YOLO (You Only Look Once) [37] and the derived models are also CNN-based deep learning models designed for real-time object detection. CNN and its derived methods can directly use images as inputs to train the deep learning model for classification applications, and it has become very popular in coffee-related applications. For example, the CNN-based or derived models have been successfully applied in coffee bean inspection machines [38], coffee bean quality screening [13], coffee bean defects [39; 40], coffee leaf disease classification using ResNet50 [41] and MobileNetV3 [42], coffee maturity classification [43], and roasting coffee bean quality assessment using MobileNetV2 [44]. A good comparison of CNN-derived models is available in [44]. In addition, CNN-based methods have also worked well for particle size distributions (PSD)-related applications such as using YoLov5 to detect ore PSD [45] and using mask R-CNN (Regional CNN) to evaluate the PSD of on-site rockfill [26]. Although CNN-based methods using images as an input appear to be very effective and convenient to apply, annotations or labels are usually needed in the training and validation sets for object detection [26; 45] and image classifications [13; 39; 40; 41; 42]. A comprehensive review on image segmentation algorithms based on deep learning models with annotated datasets is also available in [32]. However, in our case, across the hundreds of training and validation images, the coffee granules are much smaller and much more numerous than the ones of only 10-20 in training images in [45] using YoLov5; manual annotations are difficult and time-consuming. In addition, a convolutional neural network (CNN)-based method that downsamples images several times through convolution and pooling is not suitable for obtaining small coffee granule size information based on images from mobile phones. Furthermore, the size of flat images used as input vectors for deep learning models is quite large, which can result in very complex models that require better computers and more time for training. Therefore, we do not want to use CNN model and need to develop our image processing method and deep learning model. On the other hand, another innovative technique proposed in AlexNet, such as the rectified linear unit (ReLU) activation function [46] and dropout [47], were adopted to build the deep learning model in this study, because the ReLU activation function alleviates the vanishing gradient problem, leading to faster convergence during training; a better learning of complex features and dropout involves randomly \"dropping\" (deactivating) a percentage of neurons during training, which encourages the network to learn more robust and general features. The coffee experts provided us 600 coffee granule images taken by mobile phones upon our request. In the images, coffee granules were ground by all fifteen possible combinations of three different burr wear levels and five grinding settings. Each coffee granule image came with its label of the burr wear level. Therefore, we obtained a dataset of 600 images and partitioned it into training dataset of 480 images and validation dataset of 120 images. As every image is labeled, the deep learning model is trained in a supervised learning setting. The deep learning model has also been applied on indirect tool wear prediction [29; 48; 49], not using images, but instead using physical signals such as force signals, vibration signals, acoustic emission signals, and current signals.