Title: Automated Chicken Image Classification Using Deep Learning Techniques
Authors: Ahmad A. H. Musleh, Samy S. Abu-Naser
Volume: 10
Issue: 2
Pages: 85-96
Publication Date: 2026/02/28
Abstract:
Food quality inspection and classification play an important role in ensuring public health and food safety. Traditional inspection methods rely heavily on human expertise, which can be time-consuming, subjective, and inefficient when dealing with large quantities of products. In this paper, a machine learning-based approach is presented for identifying and classifying images of chicken using a labeled image dataset. The dataset contains approximately several thousand images captured under different conditions, representing multiple classes of chicken samples. A deep learning technique widely applied to image recognition tasks was employed to automatically learn discriminative features from the images. The proposed convolutional neural network (CNN) model was trained and evaluated on the dataset, achieving a high accuracy on a held-out test set. The experimental results demonstrate the effectiveness and feasibility of using deep learning techniques for chicken image classification.