ABSTRACT:
India is home to some of the world’s richest biodiversity, including tigers, elephants, rhinoceros, and numerous endemic species. However, this biodiversity is seriously threatened by poaching, climate change, fast habitat loss, and growing human-wildlife conflict. Artificial Intelligence (AI) has been a game-changing tool for managing and protecting wildlife in India in recent years. The main uses of AI in Indian wildlife conservation are examined in this review, including automated species identification using camera traps, predictive models for anti-poaching patrols, drone surveillance enabled by AI, habitat and land-use monitoring using satellite imagery, and early warning systems for conflicts between humans and wildlife. Large ecological datasets may be processed in real-time thanks to the integration of big data analytics and machine learning, which increases the precision and effectiveness of conservation decisions. By enabling smarter monitoring, faster threat detection, and more effective resource allocation, AI significantly enhances the capacity of conservation agencies to protect endangered species and fragile ecosystems. The study highlights the growing role of AI in building a technology-assisted, sustainable framework for wildlife protection in India and emphasizes its potential to support long-term biodiversity conservation goals.
Cite this article:
Vandana Kalra, Neha Bhatnagar, Kanta Rani, Manisha Agrawal (2025), Applications of Artificial Intelligence in Wildlife Protection in India. Spectrum of Emerging Sciences, 5 (2) 72-76., DOI: https://doi.org/10.558/SES2025-5-2-19
References:
[1] Wearn OR, Freeman R, Jacoby DMP. Responsible
AI for conservation. Nat Ecol Evol. 2019;3(11):1577–1580.
[2] Stephenson PJ. Technological advances in
biodiversity monitoring: applicability, opportunities and challenges. Curr
Opin Environ Sustain. 2019;39:67–76.
[3] Christin S, Hervet É, Lecomte N. Applications
for deep learning in ecology. Methods Ecol Evol. 2019;10(10):1632–1644.
[4] Beery S, Morris D, Yang S. Efficient pipeline
for camera trap image review using deep learning. Methods Ecol Evol.
2020;11(1):66–75.
[5] Kays R, Crofoot MC, Jetz W, Wikelski M.
Terrestrial animal tracking as an eye on life and planet. Science.
2015;348(6240):aaa2478.
[6] Norouzzadeh MS, Nguyen A, Kosmala M, Swanson
A, Palmer MS, Packer C, et al. Automatically identifying, counting, and
describing wild animals in camera-trap images with deep learning. Proc Natl
Acad Sci U S A. 2018;115(25):E5716–E5725.
[7] Sugai LSM, Silva TSF, Ribeiro JW Jr, Llusia
D. Terrestrial passive acoustic monitoring: review and perspectives. Bioscience.
2019;69(1):15–25.
[8] Pettorelli N, Laurance WF, O’Brien TG,
Wegmann M, Nagendra H, Turner W. Satellite remote sensing for applied
ecologists. J Appl Ecol. 2014;51(4):839–848.
[9] Dujon AM, Schofield G, Lester RE,
Papafitsoros K, Hays GC. Complex machine learning techniques for the analysis
of animal movement data. Methods Ecol Evol. 2021;12(2):373–385.
[10] van Eeden LM, et al. The role of artificial
intelligence in addressing human–wildlife conflict. Conserv Biol.
2021;35(2):455–467.