Scopus
🔓 Açık Erişim YÖKSİS Eşleşti
Automated Classification of Biscuit Quality Using YOLOv8 Models in Food Industry
Food Analytical Methods · Ocak 2025
YÖKSİS Kayıtları
Automated Classification of Biscuit Quality Using YOLOv8 Models in Food Industry
Food Analytical Methods · 2025 SCI-Expanded
DOÇENT MURAT KÖKLÜ →
Makale Bilgileri
DergiFood Analytical Methods
Yayın TarihiOcak 2025
Scopus ID2-s2.0-85217253727
Erişim🔓 Açık Erişim
Özet
It is of great importance for food safety and consumer satisfaction that industrial food products are durable, hygienic, and flawless. Robust products protect the physical integrity of the product by preventing damage that may occur during the production and transportation processes, which meets the expectations of the consumer. Hygienic production conditions prevent foodborne diseases by minimizing the risk of microbial contamination and protect consumer health. Perfect products strengthen the brand image with their aesthetic and satisfactory features and increase consumer loyalty. In the study conducted in this context, the classification of defect and no defect conditions of biscuits in the food industry was examined using YOLOv8 models. A summary dataset consisting of 4990 biscuit images was created and the biscuits were initially divided into two categories: defect and no defect. Later, defect biscuits were classified into three subcategories: not complete, overcooked, and texture defect. As a result of experiments with YOLOv8 models, binary classification (defect, no defect), the highest accuracy rate was achieved in the YOLOv8-m, YOLOv8-l, and YOLOv8-x models with 96.78%, while the highest accuracy rate in the triple classification (not complete, overcooked, and texture defect) performance was achieved in the YOLOv8-m model with 96.99%.
Yazarlar (2)
1
Oya Kılcı
ORCID: 0000-0002-7993-9875
2
Murat Koklu
ORCID: 0000-0002-2737-2360
Anahtar Kelimeler
Biscuit classification
Deep learning
YOLOv8
Kurumlar
Selçuk Üniversitesi
Selçuklu Turkey