Poultry Pathogen Detection: Using Deep Learning for Coccidiosis Identification

Poultry farming plays a crucial part in fulfilling the need for animal protein worldwide, but the industry faces significant challenges in controlling infectious diseases that impact bird health and productivity. Protozoan parasites of the genus Eimeria are the source of coccidiosis, which is common and economically significant poultry disease. Traditional methods of coccidiosis diagnosis rely on labor-intensive and time-consuming microscopic examination of fecal samples, leading to delayed detection and response. This study investigates the use of deep learning methods for the quick and precise diagnosis of coccidiosis in poultry. A novel deep learning model trained on a comprehensive dataset of microscopic images of fecal samples obtained from infected birds has been proposed in this study. This study presents an innovative approach to coccidiosi detection using the VGGNet architecture, a deep convolutional neural network renowned for its hierarchical feature learning capabilities. Leveraging this architecture, we explore the potential for automated identification of coccidia-infected samples through microscopic image analysis. This study evaluates the performance of the deep learning model against traditional diagnostic methods and also architectures like YOLOv5, ResNet and demonstrating its ability to significantly reduce the time required for coccidiosis detection without compromising accuracy. We also discuss the model’s flexibility in a variety of environments and its potential for early identification, enabling prompt intervention and disease management. This research contributes to the ongoing efforts to modernize poultry disease diagnostics through the utilization of deep learning.