Exploring the Use of Artificial Intelligence and Mixed Reality in Conservation Efforts for Endangered Wildlife in Pakistan
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Pakistan is a country with a rich and diverse wildlife. However, it is under considerable anthropogenic and environmental pressures (poaching, habitat destruction, and global warming) that pose significant threats to endangered species, including the snow leopard and the Indus River dolphin. To address this urgent conservation need, it is important to adopt new, high-tech solutions. This paper explores the transformative potential of artificial intelligence (AI) and real-time mixed reality (MR) to strengthen wild animal conservation efforts in Pakistan. The study is based on a synthesis of the recent scholarly literature (2019-2024). It examines the successful international implementation of these technologies, particularly in Australia, which is renowned for its unique biodiversity. It expands on how AI can change real-time species recognition, habitat conservation, and anti-poaching solutions. At the same time, augmented reality (AR) and virtual reality (VR) are practical means to foster empathy, raise awareness, and support environmental education. Despite the relevant issues, including data biases, limitations of technological infrastructure, and the ethical implications of privacy, this study argues for the phased implementation of advanced digital applications that do not conflict with traditional cultural and modern conservation processes in Pakistan. The purpose of the presented conceptual framework is to convey the proposed ideas to professionals and stakeholders in the field, thereby helping them endorse the hybrid strategy, which would make the conservation of endangered species in the country more effective and efficient in the long run
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Ali, S. H., & Hussain, Z. (2023). Impact of climate change on South Asia: Shifting marine ecosystems in Pakistan. Pakistan Horizon, 76(4), 65–83.
Ahmad, A., Ullah, S., & Hussain, M. S. (2022). Indigenous knowledge and practices for sustainable wildlife conservation in Pakistan. Environmental Science and Pollution Research, 29(25), 22357–22369.
Ahmad, I., Guo, P., Zhao, M.-X., Zhong, Y., Zheng, X.-Y., Zhang, S.-Q., Qiu, J.-W., Shi, Q., Yan, H.-Q., Tao, S.-C., & Xu, L.-J. (2024). Coral reefs of Pakistan: A comprehensive review of anthropogenic threats, climate change, and conservation status. Frontiers in Marine Science, 11, Article e1466834. https://doi.org/10.3389/fmars.2024.1466834
Ahmed, A. (2025, August 17). Eyes in the sky: Pakistan’s new satellite goes live to power progress: High-tech remote sensing satellite to boost agriculture and disaster response. Gulf News. https://gulfnews.com/world/asia/pakistan/eyes-in-the-sky-pakistans-new-satellite-goes-live-to-power-progress-1.500235704
Alabdali, S. A., Pileggi, S. F., & Cetindamar, D. (2023). Influential factors, enablers, and barriers to adopting smart technology in rural regions: A literature review. Sustainability, 15(10), Article e7908. https://doi.org/10.3390/su15107908
Ashley, C., & Barnes, J. (2020). Wildlife use for economic gain: The potential for wildlife to contribute to development in Namibia. In F. Smith (Ed.), Environmental sustainability (pp. 163–191). CRC Press.
Associated Press Pakistan. (2024, October 23). AI-based camera traps enable humans, wildlife to co-exist in GB. https://www.app.com.pk/national/ai-based-camera-traps-enable-humans-wildlife-co-existence-in-gb/
Baltistan Wildlife Conservation & Development Organization. (n.d.). BWCDO. Retrieved July 22, 2025, from https://www.bwcdo.org/
Bajaj, S., & Amin, P. (2025). Exploring the future of environmental education through extended reality. In S. K. Gupta, N.Maurya, D-N. Le, & T. Mzili (Eds.), Exploring the impact of extended reality (XR) technologies on promoting environmental sustainability (pp. 417–437). Springer Nature.
Berger‐Tal, O., & Lahoz‐Monfort, J. J. (2018). Conservation technology: The next generation. Conservation Letters, 11(6), Article e12458. https://doi.org/10.1111/conl.12458
Bilyk, Z. I., Shapovalov, Y. B., Shapovalov, V. B., Megalinska, A. P., Zhadan, S. O., Andruszkiewicz, F., Dołhańczuk-Śródka, A., & Antonenko, P. D. (2022). Comparison of Google Lens recognition performance with other plant recognition systems. Educational Technology Quarterly, 4, 328–346.
Brondízio, E. S., Settele, J., Diaz, S., Ngo, H. T., & Mohamed, A. A. A. (2019). Global assessment report on biodiversity and ecosystem services. Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services. https://www.ipbes.net/global-assessment
Buddin, S., Ibrahim, M., Ullah, A., Dastagir, G., Bibi, S., Sara, Ahmad, I., Ullah, I., Ahmad, M., & Khan, B. (2024). Ethnobotanical studies and conservation status of the medicinal flora of Bara Gali Forests, District Abbottabad, Pakistan. Ethnobotany Research and Applications, 27, 1–22.
Business Recorder. (2021, August 26). First ‘smart forest’ launched: Ravi Riverfront project may generate $40bn: PM. https://www.brecorder.com/news/40115826
Business Recorder. (2025, March 4). WWF launches app to combat wildlife crimes. https://www.brecorder.com/news/40351115
Business Research Company. (2025). AI in virtual reality and augmented reality global market report 2025. https://www.thebusinessresearchcompany.com/report/ai-in-virtual-reality-and-augmented-reality-global-market-report
Chisom, O. N., Biu, P. W., Umoh, A. A., Obaedo, B. O., Adegbite, A. O., & Abatan, A. (2024). Reviewing the role of AI in environmental monitoring and conservation: A data-driven revolution for our planet. World Journal of Advanced Research and Reviews, 21(1), 161–171.
Chokkattu, J. (2024, December 12). Hands-on with Android XR and Google’s AI-powered smart glasses. WIRED. https://www.wired.com/story/google-android-xr-demo-smart-glasses-mixed-reality-headset-project-moohan/
Conservation International. (n.d.). Virtual reality. Retrieved July 22, 2025, from https://www.conservation.org/stories/virtual-reality
Cosio, L. D., Buruk, O., Fernández Galeote, D., De Villiers Bosman, I., & Hamari, J. (2023, April 23–28). Virtual and augmented reality for environmental sustainability: A systematic review [Paper presentation]. Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems, Hamburg Germany.
Australian Government. (2023, July 19). Take a virtual tour of the Great Barrier Reef. https://www.dcceew.gov.au/about/news/take-a-virtual-tour-great-barrier-reef
Doull, K. E., Chalmers, C., Fergus, P., Longmore, S., Piel, A. K., & Wich, S. A. (2021). An evaluation of the factors affecting “poacher” detection with drones and the efficacy of machine learning for detection. Sensors, 21(12), Article e4074. https://doi.org/10.3390/s21124074
Ennakri, M., Ziti, S., & Dakki, M. (2024). Habitat intelligence: How machine learning reveals species preferences for ecological planning and conservation. International Journal of Advanced Computer Science and Applications, 16(6), 819–827.
Fergus, P., Chalmers, C., Longmore, S., & Wich, S. (2024). Harnessing artificial intelligence for wildlife conservation. Conservation, 4(4), Article e41. https://doi.org/10.3390/conservation4040041
Galligan, T. H., Mallord, J. W., Prakash, V. M., Bhusal, K. P., Alam, A. B. M. S., Anthony, F. M., Dave, R., Dube, A., Shastri, K., Kumar, Y., Prakash, N., Ranade, S., Shringarpure, R., Chapagain, D., Chaudhary, I. P., Joshi, A. B., Paudel, K., Kabir, T., Ahmed, S., . . . Green, R. E. (2021). Trends in the availability of the vulture-toxic drug, diclofenac, and other NSAIDs in South Asia, as revealed by covert pharmacy surveys. Bird Conservation International, 31(3), 337–353. https://doi.org/10.1017/S0959270920000477
Gerup, J., Soerensen, C. B., & Dieckmann, P. (2020). Augmented reality and mixed reality for healthcare education beyond surgery: An integrative review. International Journal of Medical Education, 11, 1–18. https://doi.org/10.5116/ijme.5e01.eb1a
Gleeson, M. (2025, March 22). Drone tech and Indigenous knowledge protect North Stradbroke koalas. Courier Mail. https://www.couriermail.com.au/lifestyle/pets-and-wildlife/north-stradbroke-island-koalas-fireproofed-with-drone-detection/news-story/0981020e74d332f37843af22c5cd62a6
Gulzar, A., Islam, T., Hamid, M., & Marifatul Haq, S. (2024). Environmental ethics towards the sustainable development in Islamic perspective: A brief review. Ethnobotany Research and Applications, 22, 1–10.
He, T. (2020). Image monitoring and artificial intelligence recognition technology for rare animal protection. Revista Científica de la Facultad de Ciencias Veterinarias, 30(5), 2390–2399.
Hoffmann, S. (2022). Challenges and opportunities of area-based conservation in reaching biodiversity and sustainability goals. Biodiversity and Conservation, 31(2), 325–352. https://doi.org/10.1007/s10531-021-02340-2
Hua, A., Martin, K., Shen, Y., Chen, N., Mou, C., Sterk, M., Reinhard, B., Reinhard, F. F., Lee, S., Alibhai, S., & Jewell, Z. C. (2022). Protecting endangered megafauna through AI analysis of drone images in a low-connectivity setting: A case study from Namibia. PeerJ, 10, Article e13779.
Jabeen, R., Elahi, N., & Alam, A. (2025). An analysis of joint forest management in Khyber Pakhtunkhwa, Pakistan through strength, weakness, opportunity and threat lens. Society & Natural Resources, 38(2), 174–195. https://doi.org/10.1080/10549811.2025.2451441
Khan, K. A., & Ghramh, H. A. (2023). Beekeeping in Pakistan: History, potential, and current status. Pakistan Journal of Zoology, 56(1), 421–428.
Khan, S., & Khan, F. (2024). Augmented reality in zoos (ZAR): Employing an innovative image enhancement technique and localization with offline features. Research Square. https://doi.org/10.21203/rs.3.rs-4710403/v1
Kumar, B., & Ghosh, O. (2025). An overview of AI applications in wildlife conservation. In Y. Y. Raghav & S. B. Khan (Eds.), AI and machine learning techniques for wildlife conservation (pp. 19–48). IGI Global.
Kuruppu, S. (2023). AI system to protect endangered animal population and prevent poaching threats using weapon detection. International Journal of Innovative Science and Research Technology, 8(9), 1270–1275.
Ladykova, T. I., Sokolova, E. I., Grebenshchikova, L. Y., Sakhieva, R. G., Lapidus, N. I., & Chereshneva, Y. V. (2024). Augmented reality in environmental education: A systematic review. EURASIA Journal of Mathematics, Science and Technology Education, 20(8), Article e2488. https://doi.org/10.29333/ejmste/14914
Latif, A. (2024, October 23). AI turns human–wildlife conflict into “human–wildlife coexistence” in northern Pakistan. Anadolu Agency. https://www.aa.com.tr/en/asia-pacific/ai-turns-human-wildlife-conflict-into-human-wildlife-coexistence-in-northern-pakistan/3371643
Luccioni, A. S., & Rolnick, D. (2022). Bugs in the data: How ImageNet misrepresents biodiversity. Arxiv. https://doi.org/10.48550/arXiv.2208.11695
Manikandan, S., Kaviya, R. S., Shreeharan, D. H., Subbaiya, R., Vickram, S., Karmegam, N., Kim, W., & Govarthanan, M. (2025). Artificial intelligence–driven sustainability: Enhancing carbon capture for sustainable development goals – A review. Sustainable Development, 33(2), 2004–2029. https://doi.org/10.1002/sd.3222
Mukul, S. A., Alamgir, M., Sohel, M. S. I., Pert, P. L., Herbohn, J., Turton, S. M., Khan, M. S. I., Munim, S. A., Reza, A. A., & Laurance, W. F. (2019). Combined effects of climate change and sea-level rise project dramatic habitat loss of the globally endangered Bengal tiger in the Bangladesh Sundarbans. The Science of the Total Environment, 663, 830–840. https://doi.org/10.1016/j.scitotenv.2019.01.383.
Nandutu, I., Atemkeng, M., & Okouma, P. (2023). Integrating AI ethics in wildlife conservation AI systems in South Africa: A review, challenges, and future research agenda. AI & Society, 38, 245–257. https://doi.org/10.1007/s00146-021-01285-y
Ostrom, E. (2009). A general framework for analyzing sustainability of social-ecological systems. Science, 325(5939), 419–422. https://doi.org/10.1126/science.1172133
Petso, T., Jamisola, R. S., & Mpoeleng, D. (2022). Review on methods used for wildlife species and individual identification. European Journal of Wildlife Research, 68, Article e1. https://doi.org/10.1007/s10344-021-01549-4
Pimentel, D. (2022). Saving species in a snap: On the feasibility and efficacy of augmented reality-based wildlife interactions for conservation. Journal for Nature Conservation, 66, Article e126151. https://doi.org/10.1016/j.jnc.2022.126151
Prepp. (2023). TRAFFIC: The wildlife trade monitoring network. https://prepp.in/news/e-492-traffic-the-wildlife-trade-monitoring-network-environment-notes
Rasch, J., Müller, F., & Chiossi, F. (2025). A vision for AI-driven adaptation of dynamic AR content to users and environments. Arxiv. https://arxiv.org/abs/2504.16562
Reynolds, S. A., Beery, S., Burgess, N., Burgman, M., Butchart, S. H. M., Cooke, S. J., Coomes, D., Danielsen, F., Di Minin, E., Durán, A. P., Gassert, F., Hinsley, A., Jaffer, S., Jones, J. P. G., Li, B. V., Mac Aodha, O., Madhavapeddy, A., O’Donnell, S. A. L., Oxbury, W. M., . . . Sutherland, W. J. (2024). The potential for AI to revolutionize conservation: A horizon scan. Trends in Ecology & Evolution, 40(2), 191–207. https://doi.org/10.1016/j.tree.2024.11.013
Robbins, J. (2025, May 19). Out of the wild: How A.I. is transforming conservation science. Yale Environment 360. https://e360.yale.edu/features/artificial-intelligence-conservation
Ronoh, E. (2023). Development of early warning system for human wildlife conflict using deep learning, IoT and SMS [Doctoral dissertation, Nelson Mandela African Institution of Science and Technology]. NM-AIST https://dspace.nm-aist.ac.tz/handle/20.500.12479/2574
Segun-Falade, O. D., Osundare, O. S., Kedi, W. E., Okeleke, P. A., Ijoma, T. I., & Abdul-Azeez, O. Y. (2024). Evaluating the role of cloud integration in mobile and desktop operating systems. International Journal of Management & Entrepreneurship Research, 6(8), 19–31. https://doi.org/10.56781/ijsret.2024.4.1.0019 .
Shahid, J., & Nagri, J. (2024, October 24). AI-powered cameras foster “harmonious” human–wildlife bond. Dawn. https://www.dawn.com/news/1867228
Sheehan, S., Nevins, D., & Samiuddin, S. (2014). Pakistan. Cavendish Square Publishing.
Shirazi, Q. (2025, May 22). Drones to safeguard forests from fires. The Express Tribune. https://tribune.com.pk/story/2547130/drones-to-safeguard-forests-from-fires
Snow Leopard Trust. (2023, September 13). How camera traps in a virtual world can help protect snow leopards in the real world. https://snowleopard.org/how-camera-traps-in-a-virtual-world-can-help-protect-snow-leopards-in-the-real-world
Solove, D. J. (2025). Artificial intelligence and privacy. Florida Law Review, 77, 3–73.
Störmer, N., Weaver, L. C., Stuart-Hill, G., Diggle, R. W., & Naidoo, R. (2019). Investigating the effects of community-based conservation on attitudes towards wildlife in Namibia. Biological Conservation, 233, 193–200. https://doi.org/10.1016/j.biocon.2019.02.033
Talgorn, E., & Ullerup, H. (2023). Invoking ‘empathy for the planet’ through participatory ecological storytelling: From human-centered to planet-centered design. Sustainability, 15(10), Article e7794. https://doi.org/10.3390/su15107794
Tang, Y. M., Kuo, W. T., & Lee, C. K. M. (2023). Real-time mixed reality (MR) and artificial intelligence (AI) object recognition integration for digital twin in Industry 4.0. Internet of Things, 23, Article e100753. https://doi.org/10.1016/j.iot.2023.100753
Taronga Conservation Society Australia. (n.d.). Taronga Institute of Science & Art. Retrieved July 22, 2025, from http://taronga.org.au/education/taronga-institute-of-science-and-learning.
University of Melbourne. (2018, October 26). Innovative technology used to track platypuses. https://www.unimelb.edu.au/newsroom/news/2018/october/innovative-technology-used-to-track-platypuses
University of Queensland. (2025, June 13). Koalas set to benefit from new genetic screening tool. https://news.uq.edu.au/2025-06-13-koalas-set-benefit-new-genetic-screening-tool
Wagner, B., & Nitschke, C. (2025, September 15). Thermal drones are helping to monitor some of Australia’s most elusive wildlife: Thermal camera-equipped drones are revolutionising wildlife surveys in Victoria’s native forests. University of Melbourne. https://pursuit.unimelb.edu.au/articles/Thermal-drones-are-helping-to-monitor-some-of-Australias-most-elusive-wildlife
Wang, R., Sun, Y., Zong, J., Wang, Y., Cao, X., Wang, Y., Cheng, X., & Zhang, W. (2024). Remote sensing application in ecological restoration monitoring: A systematic review. Remote Sensing, 16(12), Article e2204. https://doi.org/10.3390/rs16122204
Wildlife of Pakistan. (n.d.). Conservation of wildlife in Pakistan. Retrieved July 22, 2025, from https://wildlife.com.pk/conservation/
WWF-Australia. (2020, December 16). Google’s AI technology to identify animals impacted by bushfires. https://www.wwf.org.au/news/news/2020/googles-ai-technology-to-identify-animals-impacted-by-bushfires
Zhou, Z., Uprety, S., Nie, S., & Yang, H. (2025). GS-GVINS: A tightly-integrated GNSS-visual-inertial navigation system augmented by 3D Gaussian splatting. arXiv. https://doi.org/10.48550/arXiv.2502.10975
ZunNurain. (2025, June 3). Endangered animals in Pakistan: Wildlife conservation in crisis. Truly Pakistan. https://trulypakistan.net/endangered-animals-in-pakistan/
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