Comparative analysis of convolutional neural network architectures for poultry meat classification
10.11591/ijai.v14.i5.pp3715-3723
Sekhra Salma
,
Mohammed Habib
,
Adil Tannouche
,
Youssef Ounejjar
The increasing demand for standardized food quality assurance, particularly in regions like Morocco, emphasizes the need for accurate classification of poultry meat. This study evaluates and compares ten convolutional neural network (CNN) architectures—VGG19, VGG16, ResNet50, GoogleNet, MobileNetV1, MobileNetV2, DenseNet, NasNet, EfficientNet, and AlexNet—for classifying commonly consumed poultry meat types in Moroccan markets, including chicken, turkey, fayoumi, and farmer’s chicken. A labeled image dataset was used to train and test each model, with performance assessed using metrics such as accuracy, precision, recall, training time, and computational complexity. Additionally, the study investigates how dataset size influences model performance, addressing challenges like limited data availability and scalability. The results highlight DenseNet as the top-performing architecture, achieving 98% classification accuracy while also demonstrating superior computational efficiency. These findings are valuable for improving food quality control, offering data-driven support for stakeholders in poultry production, distribution, and regulatory bodies. By identifying optimal deep learning models for poultry meat classification, the study contributes to enhancing food authentication and safety in Morocco and similar regions. It also encourages the integration of AI-driven systems in food inspection processes, providing scalable, accurate, and efficient solutions for ensuring standardized quality in the poultry supply chain.
