{"ID":2885917,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2508.04658","arxiv_id":"2508.04658","title":"YOLOv8-Based Deep Learning Model for Automated Poultry Disease Detection and Health Monitoring paper","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.","short_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 sys...","url_abs":"https://arxiv.org/abs/2508.04658","url_pdf":"https://arxiv.org/pdf/2508.04658v1","authors":"[\"Akhil Saketh Reddy Sabbella\",\"Ch. Lakshmi Prachothan\",\"Eswar Kumar Panta\"]","published":"2025-08-06T17:27:48Z","proceeding":"cs.CV","tasks":"[\"cs.CV\",\"cs.AI\"]","methods":"[]","has_code":false}