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

Faiza Osama Abdalla Hashim (), Boon Chin Yeo (), Boon Chin Yeo (), Akaraphunt Vongkunghae (), Way Soong Lim (), Jiraporn Pooksook (), Kia Wai Liew () and Jakir Hossen ()

Edelweiss Applied Science and Technology, 2025, vol. 9, issue 8, 624-640

Abstract: 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.

Keywords: Coffee bean classification; Deep learning; EfficientNet; ResNet; VGG16; Xception. (search for similar items in EconPapers)
Date: 2025
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