Comparative analysis of deep learning models for post-roasting coffee bean classification

The classification of coffee beans is crucial for maintaining quality and consistency within the coffee industry. Manual inspection, however, is labor-intensive, error-prone, and susceptible to human biases. To address these challenges, this study aims to automate coffee bean classification using deep learning models to improve accuracy and efficiency. Four pre-trained models—Xception, ResNet50V2, EfficientNetB0, and VGG16—were evaluated for predicting post-roasting coffee bean quality based on two datasets: a Kaggle dataset and a self-collected dataset with an image scanner. The datasets included images of coffee beans at four roast levels: dark, green, light, and medium. The models were trained and tested using standard deep learning techniques, with performance assessed through metrics such as accuracy, precision, recall, and F1-score. The results demonstrated that Xception and EfficientNetB0 achieved the highest classification performance. On the Kaggle dataset, both models achieved 100% accuracy, while on the self-collected dataset, Xception achieved 99.3%, and EfficientNetB0 achieved 99.07%. These findings underscore the robustness of applying deep learning models in automating coffee quality control, reducing human intervention, and enhancing classification reliability.