Espresso Crema Analysis with f-AnoGAN

This study proposes a system that evaluates the quality of espresso crema in real time using the deep learning-based anomaly detection model, f-AnoGAN. The system integrates mobile devices to collect sensor data during the extraction process, enabling quick adjustments for optimal results. Using the GrabCut algorithm to separate crema from the background, the detection accuracy is improved. The experimental results show an increase of 0.13 in ROC-AUC in the CIFAR-10 dataset and, in crema images, ROC-AUC improved from 0.963 to 1.000 by VAE and hyperparameter optimization, achieving the classification of optimal anomalies in the image. A Pearson correlation coefficient of 0.999 confirms the effectiveness of the system. Key contributions include hyperparameter optimization, improved f-AnoGAN performance using VAE, integration of mobile devices, and improved image preprocessing. This research demonstrates the potential of AI in the management of coffee quality.