YOLOv8-Based Deep Learning Model for Automated Poultry Disease Detection and Health Monitoring paper
This addresses the problem of labor-intensive and error-prone manual disease monitoring in the poultry industry, offering an incremental improvement through automation for farm management.
The paper tackles automated poultry disease detection by developing a YOLOv8-based deep learning model that analyzes high-resolution chicken photos to identify illness signs like behavioral and appearance abnormalities, achieving accurate real-time identification and prompt warnings for farm operators.
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.