LIVESTOCK MONITORING SYSTEM USING YOLOV8 DETECTION MODEL

AGBO, DANIEL CHIDERA (2024) LIVESTOCK MONITORING SYSTEM USING YOLOV8 DETECTION MODEL. Other thesis, GODFREY OKOYE UNIVERSITY, ENUGU.

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Abstract

This project presents the design and implementation of a livestock health monitoring system using computer vision and real-time video analysis. Developed in Python with a Tkinter-based graphical interface, the system enables farmers to detect signs of illness in livestock through the automated analysis of video frames. At the core of the system is the YOLOv8 object detection model, integrated via the Ultralytics Python package and trained using a curated dataset from Roboflow Universe. The system processes live or recorded video frame by frame, identifying livestock and classifying them as healthy or unhealthy based on visual features.

Item Type: Thesis (Other)
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: Faculty of Natural Sciences and Environmental Studies
Depositing User: HILARY OBIEKWE
Date Deposited: 13 Aug 2026 14:06
Last Modified: 13 Aug 2026 14:06
URI: http://eprints.gouni.edu.ng/id/eprint/6261

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