Scopus
YÖKSİS Eşleşti
Classification of Biscuit Quality With Deep Learning Algorithms
Journal of Food Science · Temmuz 2025
YÖKSİS Kayıtları
Classification of Biscuit Quality with Deep Learning Algorithms
Journal of Food Science · 2025 SCI-Expanded
DOÇENT MURAT KÖKLÜ →
Makale Bilgileri
DergiJournal of Food Science
Yayın TarihiTemmuz 2025
Cilt / Sayfa90
Scopus ID2-s2.0-105011080335
Özet
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.
Yazarlar (3)
1
Oya Kilci
ORCID: 0000-0002-7993-9875
2
Yusuf Eryesil
ORCID: 0000-0001-8735-3666
3
Murat Koklu
ORCID: 0000-0002-2737-2360
Anahtar Kelimeler
biscuit clasification
deep learning
grad-CAM
machine learning
quality control
Kurumlar
Selçuk Üniversitesi
Selçuklu Turkey