Lightweight Depthwise Pooling Transformer for Enhanced Coffee Bean Recognition

As global trade networks rapidly expand, coffee production and consumption have increased globally, profoundly influencing modern lifestyles. However, the coffee production process still demands substantial labor, especially in the selection and processing of coffee beans. The high implementation costs have impeded its widespread adoption. Therefore, we developed a defect detection and roasting level recognition method using a lightweight vision transformer (ViT) based on the deep learning (DL) method to extract features from coffee bean images. The developed method effectively reduces the overall cost of the coffee production process, showing a recognition accuracy of 98.49% for the Coffee Cobra database and 99.68% for the Roasting Coffee Bean database. The number of the model parameters was only 0.13 M, making it appropriate to deploy to low-cost embedded platforms.