Assessment of Deep Learning Models for Poultry Disease Detection and Diagnostics: A Survey Paper

Abstract

This study focuses on the assessment of a deep learning model for the detection and diagnostics of poultry diseases. The model utilizes a convolutional neural network architecture to automatically analyze images of diseased poultry and accurately classify the type of disease present. The performance of the model is evaluated by comparing its predictions with expert- annotated data. The results show that the deep learning model achieves high accuracy in detecting common poultry diseases, outperforming traditional methods. This novel approach has the potential to revolutionize the field of poultry healthcare by providing fast and accurate diagnostics, leading to improved disease management and welfare for poultry populations.