Artificial Intelligence and Biodiversity

Umesh Kumar Lilhore, Sarita Simaiya, Surjeet Dalal, Martin Margala

• Publisher: Wiley-Scrivener
• Year: 2026
• Print ISBN: 9781394384952
• eBook ISBN: 9781394384969
• Edition: 1
• Topic: Computer Science
• Subtopic: Artificial Intelligence (AI) & Semantics
• Index: Computer Science

Harness the power of the digital frontier to save our planet with this essential guide, which demonstrates how deep learning, genetic engineering, and AI-based robotics can be integrated to track biodiversity, restore genetic diversity, and rebuild fragile ecosystems with unprecedented precision.

From satellite imagery to genetic sequencing, AI is helping researchers track biodiversity, predict ecosystem changes, and monitor endangered species with unprecedented precision. This book delves into the exciting ways that artificial intelligence (AI), particularly deep learning, is being used to analyze complex ecological data. It offers an in-depth look at how these AI-driven technologies are transforming how we approach biodiversity conservation on a global scale, examining the role of genetic engineering, guided by AI, in restoring genetic diversity and helping species adapt to rapidly changing environments. Additionally, the book highlights how AI is revolutionizing ecosystem restoration, using AI-based robotics and reinforcement learning to restore habitats such as forests, wetlands, and coral reefs. It looks at real-world applications where AI systems are actively being used to rebuild damaged ecosystems, suggesting new ways to restore balance to nature. Through a combination of practical case studies and theoretical insights, this guide serves as an essential resource for anyone interested in the future of conservation, whether you are an AI specialist, an environmental scientist, or simply someone passionate about protecting the planet. By blending the latest in AI research with real-world biodiversity challenges, this book paints a picture of a future where technology and nature work hand in hand to safeguard life on Earth.

Table of contents

• Cover
• Table of Contents
• Series Page
• Title Page
• Copyright Page
• Preface
• 1 Harnessing Artificial Intelligence to Address Global Environmental Challenges: A Cross-Domain Review
• 2 Innovative AI Paradigms for Achieving Environmental Sustainability: From Concept to Practice
• 3 Deep Learning Approaches for Real-Time Climate Monitoring and Anomaly Detection in Meteorological Systems
• 4 Federated Learning for Privacy-Preserving Environmental Monitoring Across Distributed Sensor Networks
• 5 Quantum AI in Environmental Modeling: Opportunities for Accelerating Ecosystem Simulations
• 6 Developing Digital Twin Ecosystems for Dynamic Environmental Analysis and Predictive Sustainability Planning
• 7 AI-Driven Optimization of Renewable Energy Systems: Forecasting, Load Balancing, and Grid Efficiency
• 8 Intelligent Systems for Pollution Detection and Control: Integrating AI in Urban and Industrial Environments
• 9 Smart Agriculture Using AI: Enhancing Crop Yield, Soil Health, and Resource Efficiency
• 10 Preserving Biodiversity through AI: Automated Species Monitoring and Habitat Conservation Strategies
• 11 Explainable AI in Environmental Decision-Making: Enhancing Trust and Transparency in Sustainability Models
• 12 Ethical Implications of AI in Environmental Policy Formulation: Balancing Innovation and Responsibility
• 13 Shaping a Sustainable Future: The Role of AI in Driving Green Innovation and Environmental Equity
• 14 Decades of Transformation: Predictive Analysis of Land Use Changes in Dhanbad Using Deep Learning and Remote Sensing
• 15 AI for Biodiversity and Ecosystem Conservation
• 16 Pneumonia Detection in Chest Based on Respiratory Variability Using Deep Learning
• 17 Integrated Optimization Strategies for High-Efficiency Solar PV Plants: From AI to Bifacial Technologies
• Index
• Also of Interest
• End User License Agreement