ABSTRACT
The conservation of elephants, a keystone species in Africa and Asia’s protected areas, is hindered by the complexity and scale of monitoring their behavior, habitat, and population dynamics. Recent advances in artificial intelligence (AI) and machine learning (ML) offer a paradigm shift in wildlife conservation, enabling the efficient analysis of vast amounts of data from various sensors, such as camera traps, drones, and satellites. This review article provides a comprehensive overview of AI-driven monitoring methods for elephant conservation, highlighting their potential to enhance our understanding of elephant behavior, habitat use, and population dynamics. We discuss the applications of AI-powered computer vision, acoustic monitoring, and predictive modeling in elephant conservation, as well as the challenges and limitations associated with these approaches. Furthermore, we emphasize the importance of interdisciplinary collaboration between AI experts, ecological researchers, and conservation practitioners to ensure the effective development and deployment of AI-driven conservation solutions.
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