Abstract: Poultry farming is critical for global food security, yet it faces significant challenges in disease diagnosis, leading to economic losses and public health risks. Traditional methods are often time-consuming and inaccurate. Recently, deep learning (DL) techniques have emerged as powerful tools for disease diagnosis. This paper reviews the application of DL methods in poultry disease diagnosis. First, we discuss various poultry diseases, emphasizing early and accurate diagnosis. Next, we explore deep learning concepts, highlighting its ability to learn complex patterns from large datasets. We survey state-of-the-art deep learning architectures like convolutional neural networks (CNNs) optimized for poultry disease diagnosis. We address challenges such as dataset availability, model interpretability, and generalization to diverse conditions. Finally, we outline future research directions, including transfer learning and multi-modal data fusion, to enhance poultry disease diagnosis and mitigate its impact on global food security.
Keywords: Poultry, Disease diagnosis, Deep Learning, Dataset, Preprocessing, Convolutional Neural Networks (CNNs), Image classification, transfer learning, fine tuning, accuracy.
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DOI:
10.17148/IJARCCE.2024.134163
