Assessment of Chicken Meat Quality Through Categorized Image Analysis Using Artificial Intelligence

Biabanian, Arash and Shojaedini, Seyed Vahab and Misaghi, Nima (2026) Assessment of Chicken Meat Quality Through Categorized Image Analysis Using Artificial Intelligence. Journal of World's Poultry Research. 31 - 41. ISSN 2322455X

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Abstract

As consumer demands for high food quality have risen, the swift and non-destructive assessment of chicken meat freshness has become crucial for public health. The present study developed an artificial-intelligence-based digital imaging approach to estimate chicken meat quality by analyzing images categorized as healthy, defrosted, or rotten. A total of seven pre-trained convolutional neural network architectures, including Xception, Inception V3, MobileNet, DenseNet121, VGG16, VGG19, and a baseline convolutional neural network (CNN), were considered for the current study. Chicken meat samples were photographed under controlled lighting, and their quality categories were confirmed by expert inspections. Model performance was evaluated by overall classification accuracy. Among the tested architectures, the VGG19 network achieved the highest mean accuracy of 94.3, outperforming compared to the baseline CNN by approximately 9, while other networks exceeded 85 accuracy, indicating reliable recognition capability. This study confirmed that AI-based digital image analysis can accurately classify chicken meat freshness in a non-destructive manner. Among the tested models, VGG19 achieved the highest performance and demonstrated its strong capability for reliable meat quality assessment. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article�s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article�s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit https://creativecommons.org/licenses/by/4.0/. © The Author(s) 2026

Item Type:	Article
Keywords:	Accuracy; Artificial intelligence; Chicken meat quality; Deep learning; Transfer learning
Subjects:	S Agriculture > SF Animal culture
Divisions:	Journal of World's Poultry Research (JWPR)
Page Range:	31 - 41
Journal or Publication Title:	Journal of World's Poultry Research
Journal Index:	Scopus
Volume:	16
Number:	1
Publisher:	Scienceline Publication
Identification Number:	https://doi.org/10.36380/jwpr.2026.4
ISSN:	2322455X
Depositing User:	Dr. Daryoush Babazadeh
URI:	https://eprints.science-line.com/id/eprint/1758

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