Artificial Intelligence (AI) Conservaion Wildlife Monitoring Species Identification Habitat Monitoring Anti-Poaching Illegal Activity Detection Conservation Planning Citizen Science and Public Department Restoration Rewilding

1. Artificial Intelligence in
Conservation
Dr. A. Prabaharan
Professor & Research Director
Public Action
www.indopraba.blogspot.com

2. AI in Conservation
• Artificial Intelligence (AI) plays a
crucial role in conservation
efforts, offering innovative
solutions to address various
challenges related to
biodiversity loss, habitat
degradation, and environmental
monitoring.
• Here are several ways AI is
applied in conservation
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3. Wildlife Monitoring
• Camera Trap Analysis:
• AI-powered algorithms analyze camera trap
images to identify and count different wildlife
species.
• This aids researchers in monitoring population
trends, studying behavior, and identifying rare
or endangered species.
• Acoustic Monitoring:
• AI is used to process audio recordings from
ecosystems, helping identify and classify
animal sounds.
• This is particularly valuable in monitoring
elusive or nocturnal species.
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4. Species Identification
• Image Recognition:
• AI applications, often using deep learning
techniques, can identify plant and animal
species from images.
• This facilitates rapid biodiversity assessments
and contributes to citizen science initiatives.
• Genetic Identification:
• AI assists in analyzing genetic data to identify
and track individual animals, helping in
population genetics studies and monitoring of
endangered species.
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5. Habitat Monitoring
• Satellite Data Analysis:
• AI processes satellite imagery to monitor
changes in land cover, deforestation, and
habitat loss.
• This information is crucial for assessing the
health of ecosystems and implementing
conservation measures.
• Drones and Aerial Surveys: AI aids in
analyzing data collected from drones and
aerial surveys, providing high-resolution
images for habitat monitoring, anti-poaching
efforts, and environmental impact
assessments.
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6. Anti-Poaching and Illegal Activity Detection
• Surveillance Systems: AI-powered
surveillance systems use cameras and
sensors to detect and alert authorities to
illegal activities, such as poaching or
logging.
• This helps in real-time intervention and
enforcement.
• Pattern Recognition: AI algorithms analyze
patterns in data to predict and identify
potential illegal activities, enhancing the
effectiveness of conservation law
enforcement.
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7. Conservation Planning
• Land Use Optimization:
• AI assists in optimizing land use planning by
analyzing ecological and environmental data.
• This helps identify priority areas for conservation,
habitat restoration, and sustainable land
management.
• Climate Change Impact Assessment: AI models
assess the potential impact of climate change on
ecosystems, guiding conservation strategies to
increase resilience and adaptive capacity.
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8. Citizen Science and Public Engagement
• Mobile Apps and Chatbots:
• AI-powered mobile applications and chatbots
engage the public in citizen science projects,
enabling users to contribute to data
collection, species identification, and
conservation awareness.
• Education Platforms:
• AI is used to develop interactive educational
platforms that raise awareness about
conservation issues, biodiversity, and
sustainable practices.
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9. Restoration and Rewilding
• Ecological Restoration Planning:
• AI analyzes data on soil health, climate, and
vegetation to optimize ecological restoration
projects.
• This includes reforestation, wetland restoration,
and other initiatives to rehabilitate degraded
ecosystems.
• Rewilding Strategies:
• AI contributes to developing rewilding strategies
by identifying suitable areas for reintroducing
species, considering ecological connectivity and
potential impacts on local ecosystems.
www.indopraba.blogspot.com

10. Challenges and Considerations
• Data Quality and Bias:
• Ensuring that AI models are trained on diverse and
unbiased datasets is crucial for accurate species
identification and monitoring.
• Ethical Use of AI:
• Ethical considerations, including transparency and
privacy, are important when applying AI in conservation,
especially in areas where human and environmental
interests intersect.
• Capacity Building:
• Building local capacity and expertise in AI applications for
conservation is essential to ensure effective
implementation and long-term success.
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11. Future Trends
• Advancements in Sensor Technologies: Continued
improvements in sensor technologies, including remote
sensing and wildlife tracking devices, will enhance the
quality and quantity of data available for AI applications.
• Integration with Traditional Ecological Knowledge:
Combining AI-driven insights with traditional ecological
knowledge enhances the effectiveness and cultural
relevance of conservation efforts.
• Global Collaboration: Increased collaboration
between conservation organizations, governments, and
technology companies to share data and expertise,
fostering a more comprehensive and coordinated
approach.
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