YOLOv8-Based Deep Learning Model for Automated Poultry Disease Detection and Health Monitoring paper

TL;DR

This paper presents a YOLOv8-based deep learning system for real-time detection of poultry diseases, aiming to improve health monitoring and biosecurity in large-scale farms by automating illness identification.

Contribution

It introduces a novel AI system utilizing YOLOv8 for real-time poultry disease detection from high-resolution images, enhancing accuracy and efficiency over manual methods.

Findings

High accuracy in detecting illness signs

Real-time identification enables prompt farm responses

Scalable solution for large-scale poultry farms

Abstract

In the poultry industry, detecting chicken illnesses is essential to avoid financial losses. Conventional techniques depend on manual observation, which is laborious and prone to mistakes. Using YOLO v8 a deep learning model for real-time object recognition. This study suggests an AI based approach, by developing a system that analyzes high resolution chicken photos, YOLO v8 detects signs of illness, such as abnormalities in behavior and appearance. A sizable, annotated dataset has been used to train the algorithm, which provides accurate real-time identification of infected chicken and prompt warnings to farm operators for prompt action. By facilitating early infection identification, eliminating the need for human inspection, and enhancing biosecurity in large-scale farms, this AI technology improves chicken health management. The real-time features of YOLO v8 provide a scalable and effective method for improving farm management techniques.