Poultry Disease Detection Using Machine Learning

Abstract

Poultry farming plays an important role in ensuring food security and economic stability, especially in developing countries. However, the poultry industry continues to face important challenges due to the frequent outbreak of infectious diseases, resulting in significant economic losses and public health concerns. The recent progress in machine learning (ML) has shown great promise in the early detection of poultry diseases and increasing diagnosis, which enables timely intervention and control. This review paper presents a comprehensive observation of the existing ML approaches applied to the paper poultry disease detection, including supervised, unsupervised, and deep learning methods. It analyzes the functioning, dataset, and performance matrix employed in various studies, which highlights their strength, boundaries, and genuine world praise. Additionally, the paper identifies the current research interval and outlines the directions for the future to improve the systems of detection of the disease through the hybrid model, better dataset availability, and increased model accuracy. Review underlines ML's transformative ability to revolutionize poultry health management and support its adoption in accurate livestock farming.