Reviewing the role of AI in environmental monitoring and conservation: A data-driven revolution for our planet
1 National Examinations Council (NECO), Nigeria.
2 Independent National Electoral Commission (INEC) Nigeria.
3 Independent Researcher, Uyo Nigeria.
4 Department of Building, Ambrose Alli University, Ekpoma.
5 IHS Towers Nigeria Plc, Nigeria.
6 Saltwire Network, Halifax, Canada.
Review Article
World Journal of Advanced Research and Reviews, 2024, 21(01), 161–171
Publication history:
Received on 23 November 2023; revised on 30 December 2023; accepted on 01 January 2024
Abstract:
The rapid increase in human activities is causing significant damage to our planet's ecosystems, necessitating innovative solutions to preserve biodiversity and counteract ecological threats. Artificial Intelligence (AI) has emerged as a transformative force, providing unparalleled capabilities for environmental monitoring and conservation. This research paper explores the applications of AI in ecosystem management, including wildlife tracking, habitat assessment, biodiversity analysis, and natural disaster prediction. AI's role in environmental monitoring and conservation includes wildlife tracking, habitat assessment, resource conservation, biodiversity analysis, and species identification. AI algorithms analyze camera trap footage, drone imagery, and GPS data to identify and estimate population sizes, leading to improved anti-poaching efforts and enhanced protection of diverse species. Habitat assessment and resource conservation involve AI-powered image analysis, which aids in assessing forest health, detecting deforestation, and identifying areas in need of restoration. Biodiversity analysis and species identification are achieved through AI algorithms that analyze acoustic recordings, environmental DNA (eDNA), and camera trap footage. These innovations identify different species, assess biodiversity levels, and even discover new or endangered species. AI-powered flood prediction systems provide early warnings, empowering communities with better preparedness and evacuation efforts. Challenges, such as data quality and availability, algorithmic bias, and infrastructure limitations, are acknowledged as opportunities for growth and improvement. In policy and regulation, the paper advocates for clear frameworks prioritizing data privacy and security, algorithmic transparency, and equitable access. Responsible development and ethical use of AI are emphasized as foundational pillars, ensuring that the integration of AI into environmental conservation aligns with principles of fairness, transparency, and societal benefit.
Keywords:
Artificial intelligence; Environmental monitoring; Conservation; Wildlife tracking; Habitat assessment; Biodiversity analysis; Natural disaster prediction; Case studies; Best practices; Challenges; Future directions.
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Copyright information:
Copyright © 2024 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0