Vision-Based AI Models for Wildlife Conservation and Species Tracking

Authors- Gokul Nandan A

Abstract--The accelerating loss of biodiversity due to habitat destruction, climate change, and poaching necessitates the development of advanced conservation tools. Traditional wildlife monitoring methods—manual tracking, field surveys, and camera traps—are often labor-intensive, costly, and geographically limited. Vision-based Artificial Intelligence (AI) models have emerged as transformative technologies capable of automating and scaling wildlife observation through image and video data. Powered by deep learning and computer vision, these models can perform species recognition, behavioral analysis, population estimation, and illegal activity detection with unprecedented speed and accuracy. This paper explores the foundations of vision-based AI in wildlife conservation, including key algorithms and data sources. It details various use cases such as species identification, habitat monitoring, and poacher detection. Real-world applications from regions including Africa, South America, and Southeast Asia demonstrate how these models are revolutionizing conservation strategies. The paper also discusses ethical and regulatory considerations related to data usage, community involvement, and potential surveillance misuse. Technical challenges such as data quality, model bias, and deployment in remote areas are evaluated. Finally, future innovations such as edge AI, explainable models, and citizen science integration are explored. Vision-based AI holds immense potential to support biodiversity preservation and empower conservationists with precise, scalable, and non-invasive monitoring tools.

DOI: /10.61463/ijset.vol.13.issue2.376