Fergus, P, Chalmers, C, Longmore, S and Wich, S (2024) Harnessing Artificial Intelligence for Wildlife Conservation. Conservation, 4 (4). pp. 685-702.

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Abstract

The rapid decline in global biodiversity demands innovative conservation strategies. This paper examines the use of artificial intelligence (AI) in wildlife conservation, focusing on the Conservation AI platform. Leveraging machine learning and computer vision, Conservation AI detects and classifies animals, humans, and poaching-related objects using visual spectrum and thermal infrared cameras. The platform processes these data with convolutional neural networks (CNNs) and transformer architectures to monitor species, including those that are critically endangered. Real-time detection provides the immediate responses required for time-critical situations (e.g., poaching), while non-real-time analysis supports long-term wildlife monitoring and habitat health assessment. Case studies from Europe, North America, Africa, and Southeast Asia highlight the platform’s success in species identification, biodiversity monitoring, and poaching prevention. The paper also discusses challenges related to data quality, model accuracy, and logistical constraints while outlining future directions involving technological advancements, expansion into new geographical regions, and deeper collaboration with local communities and policymakers. Conservation AI represents a significant step forward in addressing the urgent challenges of wildlife conservation, offering a scalable and adaptable solution that can be implemented globally.

Item Type:	Article
Subjects:	G Geography. Anthropology. Recreation > GE Environmental Sciences Q Science > QA Mathematics > QA75 Electronic computers. Computer science T Technology > TD Environmental technology. Sanitary engineering
Divisions:	Astrophysics Research Institute Biological and Environmental Sciences (from Sep 19) Computer Science and Mathematics
Publisher:	MDPI
SWORD Depositor:	A Symplectic
Date Deposited:	14 Nov 2024 10:30
Last Modified:	14 Nov 2024 10:30
DOI or ID number:	10.3390/conservation4040041
URI:	https://researchonline.ljmu.ac.uk/id/eprint/24775

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