An Edge-Deployable YOLOv8 System for Real-Time Detection of Health and Behavioural Abnormalities in Poultry Farming
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https://doi.org/10.63300/Abstract
Poultry farming is an important part of the food industry, but keeping track of the health and behavior of birds can be difficult, especially on large farms. This project presents a real-time object detection system using YOLOv8, a deep learning model known for its speed and accuracy. The system is trained to recognize various conditions in poultry, such as weak legs, abnormal movements, crowding, and feeding habits, by analyzing images taken from the farm environment. With YOLOv8, farmers can monitor their birds continuously through cameras and get instant alerts when something unusual is detected. The model is efficient enough to run on mobile or edge devices, making it practical for real-world farm use. This approach can help farmers detect problems early, reduce losses, and improve overall poultry health and farm productivity.
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This work is licensed under a Creative Commons Attribution 4.0 International License.
Article published in Academic Research Journal of Science and Technology (ARJST) is an open access under the Creative Commons Attribution 4.0 International License -BY, (http://creativecommons.org/licenses/by/4.0/), which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
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