A MULTIMODAL FRAMEWORK FOR LIVESTOCK DETECTION AND EPIZOOTIC MONITORING

This study proposes an automated computer vision framework to detect and monitor mixed herds of cattle and sheep. By integrating unmanned aerial vehicle (UAV) imagery with ground-level perspectives, a multimodal dataset was curated. Using this data, a lightweight, single-stage object detector (YOLOv8n), tailored for resource-constrained edge devices, was trained and optimised. The integration of computer vision and UAV technology offers agricultural agencies a scalable tool for proactive epizootic surveillance.

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