AI and IoT in biodiversity assessment
Open accessAbstract
The integration of Artificial Intelligence (AI) and Internet of Things (IoT) technologies has revolutionized biodiversity assessment practices in recent years. This comprehensive review explores the various applications of AI and IoT in biodiversity assessment, highlighting their contributions to data collection, analysis, and conservation efforts. The synergy between AI algorithms and IoT sensors enables real-time monitoring of species diversity, habitat health, and environmental changes, thereby enhancing our understanding of ecosystem dynamics and supporting effective conservation strategies. Key methodologies discussed include automated species identification, species distribution modeling, wildlife monitoring and tracking, ecological forecasting, environmental DNA (eDNA) analysis, and data integration and visualization. The review also addresses challenges such as data quality and standardization, ethical and privacy concerns, and the need for capacity building. Future directions emphasize advancements in sensor technology, AI algorithms, IoT networks, data integration, and public participation. This manuscript underscores the transformative potential of AI and IoT in advancing biodiversity science, promoting sustainable resource management, and safeguarding biodiversity for future generations.
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AI-driven pipelines now make possible real-time monitoring and automated species recognition, with the potential to forecast ecological change.
“AI-driven pipelines now make possible real-time monitoring and automated species recognition, with the potential to forecast ecological change.”
From Listening forward: emerging roles of bioacoustics in ecology, evolution, and conservation (2026) · cites this paper as Rathoure and Rathaur 2024
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Listening forward: emerging roles of bioacoustics in ecology, evolution, and conservation
Zsebők, S. (2026) · Biologia Futura · doi:10.1007/s42977-026-00322-5
- Authors
- Rathoure, A. K., & Rathaur, A. K.
- ORCID
- Rathoure, Ashok K.
- Venue
- Journal of Aquaculture & Marine Biology
- Publisher
- MedCrave Group Kft.
- Published
- 2024
- Volume
- 13
- Issue
- 3
- Pages
- 108-115
- ISSN
- 2378-3184 (ISSN-L)
- OpenAlex
- W4402950574
- Cited by
- 7
- References
- 8
- Citations by year
- 2026: 3 · 2025: 4
- FWCI
- 1.92
- Type
- journal article
- Language
- en
- Open access
- diamond
- doi.org
- Licence
- cc-by-nc
- Locations
- Journal of Aquaculture & Marine Biology (publishedVersion, cc-by-nc)
- Topics
- Physical Sciences › Environmental Science › Ecology › Wildlife Ecology and Conservation
- Life Sciences › Agricultural and Biological Sciences › Plant Science › Smart Agriculture and AI
- Physical Sciences › Computer Science › Information Systems › Internet of Things and AI
- Keywords
- artificial intelligence, Internet of Things, biodiversity assessment, species identification, species distribution modeling, wildlife monitoring, ecological forecasting, environmental DNA, habitat monitoring, biodiversity conservation, citizen participation, sensor technology
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