Chalmers, C, Fergus, P, Wich, SA, Longmore, SN, Walsh, ND, Oliver, L, Warrington, J, Quinlan, J and Appleby, K (2025) AI-Driven Real-Time Monitoring of Ground-Nesting Birds: A Case Study on Curlew Detection Using YOLOv10. Remote Sensing, 17 (5). pp. 1-21. ISSN 2072-4292

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Abstract

Effective monitoring of wildlife is critical for assessing biodiversity and ecosystem health as declines in key species often signal significant environmental changes. Birds, particularly ground-nesting species, serve as important ecological indicators due to their sensitivity to environmental pressures. Camera traps have become indispensable tools for monitoring nesting bird populations, enabling data collection across diverse habitats. However, the manual processing and analysis of such data are resource-intensive, often delaying the delivery of actionable conservation insights. This study presents an AI-driven approach for real-time species detection, focusing on the curlew (Numenius arquata), a ground-nesting bird experiencing significant population declines. A custom-trained YOLOv10 model was developed to detect and classify curlews and their chicks using 3/4G-enabled cameras linked to the Conservation AI platform. The system processes camera trap data in real time, significantly enhancing monitoring efficiency. Across 11 nesting sites in Wales, the model achieved high performance, with a sensitivity of 90.56%, specificity of 100%, and F1-score of 95.05% for curlew detections and a sensitivity of 92.35%, specificity of 100%, and F1-score of 96.03% for curlew chick detections. These results demonstrate the capability of AI-driven monitoring systems to deliver accurate, timely data for biodiversity assessments, facilitating early conservation interventions and advancing the use of technology in ecological research.

Item Type:	Article
Uncontrolled Keywords:	0203 Classical Physics; 0406 Physical Geography and Environmental Geoscience; 0909 Geomatic Engineering; 3701 Atmospheric sciences; 3709 Physical geography and environmental geoscience; 4013 Geomatic engineering
Subjects:	G Geography. Anthropology. Recreation > GE Environmental Sciences
Divisions:	Biological and Environmental Sciences (from Sep 19) Computer Science and Mathematics Astrophysics Research Institute
Publisher:	MDPI
SWORD Depositor:	A Symplectic
Date Deposited:	27 Feb 2025 13:33
Last Modified:	27 Feb 2025 13:45
DOI or ID number:	10.3390/rs17050769
URI:	https://researchonline.ljmu.ac.uk/id/eprint/25742

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