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Potential for Artificial Intelligence (AI) and Machine Learning (ML) Applications in Biodiversity Conservation, Managing Forests, and Related Services in India

Author

Listed:
  • Kadukothanahally Nagaraju Shivaprakash

    (The Nature Conservancy Center, 37 Link Road, Lajpatnagar-3, New Delhi 110024, India)

  • Niraj Swami

    (The Nature Conservancy, Arington, VA 22201, USA)

  • Sagar Mysorekar

    (The Nature Conservancy Center, 37 Link Road, Lajpatnagar-3, New Delhi 110024, India)

  • Roshni Arora

    (The Nature Conservancy Center, 37 Link Road, Lajpatnagar-3, New Delhi 110024, India)

  • Aditya Gangadharan

    (The Nature Conservancy Center, 37 Link Road, Lajpatnagar-3, New Delhi 110024, India)

  • Karishma Vohra

    (The Nature Conservancy Center, 37 Link Road, Lajpatnagar-3, New Delhi 110024, India)

  • Madegowda Jadeyegowda

    (College of Forestry, Keladi Shivappa Nayaka University of Agricultural and Horticultural Sciences, Ponnampet 571216, India)

  • Joseph M. Kiesecker

    (Global Lands Program, The Nature Conservancy, Fort Collins, CO 80524, USA)

Abstract

The recent advancement in data science coupled with the revolution in digital and satellite technology has improved the potential for artificial intelligence (AI) applications in the forestry and wildlife sectors. India shares 7% of global forest cover and is the 8th most biodiverse region in the world. However, rapid expansion of developmental projects, agriculture, and urban areas threaten the country’s rich biodiversity. Therefore, the adoption of new technologies like AI in Indian forests and biodiversity sectors can help in effective monitoring, management, and conservation of biodiversity and forest resources. We conducted a systematic search of literature related to the application of artificial intelligence (AI) and machine learning algorithms (ML) in the forestry sector and biodiversity conservation across globe and in India (using ISI Web of Science and Google Scholar). Additionally, we also collected data on AI-based startups and non-profits in forest and wildlife sectors to understand the growth and adoption of AI technology in biodiversity conservation, forest management, and related services. Here, we first provide a global overview of AI research and application in forestry and biodiversity conservation. Next, we discuss adoption challenges of AI technologies in the Indian forestry and biodiversity sectors. Overall, we find that adoption of AI technology in Indian forestry and biodiversity sectors has been slow compared to developed, and to other developing countries. However, improving access to big data related to forest and biodiversity, cloud computing, and digital and satellite technology can help improve adoption of AI technology in India. We hope that this synthesis will motivate forest officials, scientists, and conservationists in India to explore AI technology for biodiversity conservation and forest management.

Suggested Citation

  • Kadukothanahally Nagaraju Shivaprakash & Niraj Swami & Sagar Mysorekar & Roshni Arora & Aditya Gangadharan & Karishma Vohra & Madegowda Jadeyegowda & Joseph M. Kiesecker, 2022. "Potential for Artificial Intelligence (AI) and Machine Learning (ML) Applications in Biodiversity Conservation, Managing Forests, and Related Services in India," Sustainability, MDPI, vol. 14(12), pages 1-20, June.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:12:p:7154-:d:836214
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    References listed on IDEAS

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    1. Lilian Danil & Siti Jahroh & Rizal Syarief & Asep Taryana, 2025. "Technological Innovation in Start-Ups on a Pathway to Achieving Sustainable Development Goal (SDG) 8: A Systematic Review," Sustainability, MDPI, vol. 17(3), pages 1-45, February.
    2. Alex Vinicio Gavilanes Montoya & Danny Daniel Castillo Vizuete & Marina Viorela Marcu, 2023. "Exploring the Role of ICTs and Communication Flows in the Forest Sector," Sustainability, MDPI, vol. 15(14), pages 1-23, July.
    3. Bo Yu & Haytham F. Isleem, 2025. "Deep learning applications for multispectral remote sensing analysis of carbon sequestration dynamics in restored forest landscapes," Mitigation and Adaptation Strategies for Global Change, Springer, vol. 30(7), pages 1-28, October.
    4. Obasi S.N & Tenebe V. A & Obasi C.C & Jokthan G.E & Adjei E.A & Keyagha E.R, 2024. "Harnessing Artificial Intelligence For Sustainable Agriculture: A Comprehensive Review Of African Applications In Spatial Analysis And Precision Agriculture," Big Data In Agriculture (BDA), Zibeline International Publishing, vol. 6(1), pages 01-13, April.

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