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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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    as
    1. Kocev, Dragi & Naumoski, Andreja & Mitreski, Kosta & Krstić, Svetislav & Džeroski, Sašo, 2010. "Learning habitat models for the diatom community in Lake Prespa," Ecological Modelling, Elsevier, vol. 221(2), pages 330-337.
    2. Zaher Mundher Yaseen & Ozgur Kisi & Vahdettin Demir, 2016. "Enhancing Long-Term Streamflow Forecasting and Predicting using Periodicity Data Component: Application of Artificial Intelligence," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(12), pages 4125-4151, September.
    3. Penczak, T. & Głowacki, Ł. & Kruk, A. & Galicka, W., 2012. "Implementation of a self-organizing map for investigation of impoundment impact on fish assemblages in a large, lowland river: Long-term study," Ecological Modelling, Elsevier, vol. 227(C), pages 64-71.
    4. Knudby, Anders & Brenning, Alexander & LeDrew, Ellsworth, 2010. "New approaches to modelling fish–habitat relationships," Ecological Modelling, Elsevier, vol. 221(3), pages 503-511.
    5. Lucas N. Joppa, 2017. "The case for technology investments in the environment," Nature, Nature, vol. 552(7685), pages 325-328, December.
    6. Alex Borowicz & Hieu Le & Grant Humphries & Georg Nehls & Caroline Höschle & Vladislav Kosarev & Heather J Lynch, 2019. "Aerial-trained deep learning networks for surveying cetaceans from satellite imagery," PLOS ONE, Public Library of Science, vol. 14(10), pages 1-15, October.
    7. Xianming Dou & Yongguo Yang & Jinhui Luo, 2018. "Estimating Forest Carbon Fluxes Using Machine Learning Techniques Based on Eddy Covariance Measurements," Sustainability, MDPI, vol. 10(1), pages 1-26, January.
    8. Watts, Michael J. & Li, Yuxiao & Russell, Bayden D. & Mellin, Camille & Connell, Sean D. & Fordham, Damien A., 2011. "A novel method for mapping reefs and subtidal rocky habitats using artificial neural networks," Ecological Modelling, Elsevier, vol. 222(15), pages 2606-2614.
    9. Zarkami, Rahmat & Sadeghi, Roghayeh & Goethals, Peter, 2012. "Use of fish distribution modelling for river management," Ecological Modelling, Elsevier, vol. 230(C), pages 44-49.
    10. Simon J Pittman & Kerry A Brown, 2011. "Multi-Scale Approach for Predicting Fish Species Distributions across Coral Reef Seascapes," PLOS ONE, Public Library of Science, vol. 6(5), pages 1-12, May.
    11. Yiannis Ampatzidis & Luigi De Bellis & Andrea Luvisi, 2017. "iPathology: Robotic Applications and Management of Plants and Plant Diseases," Sustainability, MDPI, vol. 9(6), pages 1-14, June.
    12. Devis Tuia & Benjamin Kellenberger & Sara Beery & Blair R. Costelloe & Silvia Zuffi & Benjamin Risse & Alexander Mathis & Mackenzie W. Mathis & Frank Langevelde & Tilo Burghardt & Roland Kays & Holger, 2022. "Perspectives in machine learning for wildlife conservation," Nature Communications, Nature, vol. 13(1), pages 1-15, December.
    13. Muñoz-Mas, R. & Martínez-Capel, F. & Alcaraz-Hernández, J.D. & Mouton, A.M., 2015. "Can multilayer perceptron ensembles model the ecological niche of freshwater fish species?," Ecological Modelling, Elsevier, vol. 309, pages 72-81.
    14. Valentin Bellassen & Sebastiaan Luyssaert, 2014. "Carbon sequestration: Managing forests in uncertain times," Nature, Nature, vol. 506(7487), pages 153-155, February.
    15. Daniele Silvestro & Stefano Goria & Thomas Sterner & Alexandre Antonelli, 2022. "Improving biodiversity protection through artificial intelligence," Nature Sustainability, Nature, vol. 5(5), pages 415-424, May.
    16. Diane A. Isabelle & Mika Westerlund, 2022. "A Review and Categorization of Artificial Intelligence-Based Opportunities in Wildlife, Ocean and Land Conservation," Sustainability, MDPI, vol. 14(4), pages 1-22, February.
    17. Volf, Goran & Atanasova, Nataša & Kompare, Boris & Precali, Robert & Ožanić, Nevenka, 2011. "Descriptive and prediction models of phytoplankton in the northern Adriatic," Ecological Modelling, Elsevier, vol. 222(14), pages 2502-2511.
    18. Beatrice Garske & Antonia Bau & Felix Ekardt, 2021. "Digitalization and AI in European Agriculture: A Strategy for Achieving Climate and Biodiversity Targets?," Sustainability, MDPI, vol. 13(9), pages 1-21, April.
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    1. 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.

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