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

Kadukothanahally Nagaraju Shivaprakash, Niraj Swami, Sagar Mysorekar, Roshni Arora, Aditya Gangadharan, Karishma Vohra, Madegowda Jadeyegowda and Joseph M. Kiesecker
Additional contact information
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

Sustainability, 2022, vol. 14, issue 12, 1-20

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.

Keywords: forest; artificial intelligence; forest resource management; machine learning; biodiversity conservation (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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