Classification of Biscuit Quality With Deep Learning Algorithms

ABSTRACT

This study aims to reduce time, costs, and human errors in quality control processes for biscuit production by employing deep learning models to detect defective products. Two datasets were created. One for binary classification (defect and no defect) and another for multi-class classification (overcooked, texture defect, and not complete). Among the tested models, EfficientNet achieved the highest performance, with 93.89% accuracy, 96.74% precision, and a 95.38% F1 score in binary classification, and 95.03% accuracy in multi-class classification. ResNet showed comparable performance with accuracy rates of 93.38% and 95.23% for the respective datasets. While XceptionNet and MobileNet exhibited slightly lower accuracy rates, they delivered competitive F1 scores, particularly in detecting texture defects. Grad-CAM visualizations highlighted EfficientNet's superior focus on critical defect regions, reinforcing its suitability for industrial applications. These findings demonstrate the potential of deep learning models for efficient and precise quality control in industrial food production.