Deep Learning for Terrestrial Ecosystem Sustainability

1. Auflage 2027
ISBN: 978-1-041-27402-5
Verlag: Taylor & Francis Ltd

Buch, Englisch, 264 Seiten, Format (B × H): 156 mm x 234 mm

Reihe: Computational Intelligence in Sustainable Agriculture

This book discusses how artificial intelligence can help monitor, understand, and protect terrestrial ecosystems. It presents applications of CNNs, RNNs, GANs, Vision Transformers, and Graph Neural Networks in vegetation classification, forest health, climate forecasting, deforestation detection, and biodiversity tracking. Combining case studies with responsible AI practices, the book shows how data-driven technologies can support conservation, informed decision-making, and sustainable development. This book is for academicians and professional readers seeking practical applications of deep learning, explainable AI, IoT, computer vision, and data-driven technologies for ecosystem monitoring, biodiversity protection, climate resilience, and responsible environmental decision-making.

Key Features

- Explore deep learning models including CNNs, RNNs, GANs, Vision Transformers, and Graph Neural Networks for ecosystem applications.

- Examine AI applications in vegetation classification, forest health, climate forecasting, deforestation, and biodiversity monitoring.

- Apply explainable AI and responsible-AI principles to environmental monitoring and conservation.

- Integrate IoT, multimodal data fusion, and intelligent systems for smart waste management and sustainable ecosystems.

- Investigate drone-assisted precision farming and AI-enabled approaches to sustainable resource management.

- Assess cybersecurity challenges and secure AI frameworks for smart cities and ecosystem management.

- Analyse real-world case studies and methodologies aligned with the United Nations Sustainable Development Goals.

Zielgruppe

Academic

Autoren/Hrsg.

Zafar, Sherin

Kumar, S.N.

Wani, Niyaz Ahmad

Shakil, Kashish Ara

Shaheen, Momina

Wani, Mudasir Ahmad

Fachgebiete

• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Maschinelles Lernen
• Naturwissenschaften Agrarwissenschaften Ackerbaukunde, Pflanzenbau
• Mathematik | Informatik EDV | Informatik Informatik Theoretische Informatik
• Mathematik | Informatik EDV | Informatik Daten / Datenbanken
• Naturwissenschaften Agrarwissenschaften Agrarwissenschaften Nachhaltige Landwirtschaft

Inhaltsverzeichnis

1. Responsible AI for Sustainable Development: Legal and Ethical Foundations. 2. TES-XAI Framework: Explainable Artificial Intelligence for Terrestrial Ecosystem Sustainability and Ecological Accountability. 3. Signal Processing Framework for IoT-Enabled Smart Waste Bins Using Multi-Sensor Data Fusion and Route Optimization Algorithms. 4. Human Induced Bias in Sustainable Agriculture AI Systems and Its Mitigation Using Contrastive Learning. 5. Deep Learning and Data Mining for Terrestrial Ecosystem Sustainability. 6. Forest Boundary Modelling with Deep Learning and Mathematical Approaches for Ecosystem Sustainability. 7. Data-Driven NLARX Modeling of Piezoelectric Actuator Hysteresis Using GP and Deep Learning for Sustainable Environmental Sensing. 8. Cyber-Secure Deep Learning Approaches for Terrestrial Ecosystem Management in Smart Cities.9 Internet of Things in Sustainable Agriculture. 10. Connected Agriculture: IoT and Intelligent Decision Support for Sustainable Farming. 11. Deep Learning–Driven Low-Carbon Agriculture for Higher Yields and Reduced Environmental Footprints. 12. From Sky to Field: Using Drones and Deep Learning for Smart Rice Farming. 13. Variants of GAN and Transformer Architecture for Underwater Image Enhancement. Index.

Über Autor(innen)

Dr. Sherin Zafar is Assistant Professor (Grade 2) Computer Science & Engineering in School of Engineering Sciences & Technology, Jamia Hamdard with a decade of successful experience in teaching and research management. She specializes in Wireless Networks, Soft Computing, and Network Security and has great profile in Scopus, Mendeley, Google Scholar, Research Gate and Publons. With about half century of papers published in Scopus, SCI and peer reviewed journals double century of papers reviewed and also Editorial Board and Editor in Chief of many reputed and Scopus indexed journals. Has published 6 books and has 5 patents on AI, ML and Block Chain technology, Co-Pi for FIST project of DST and Pi for 2 completed Unnat Bharat project.

Dr. S. N. Kumar is a Professor in the Department of Electrical and Electronics Engineering at Amal Jyothi College of Engineering, Kanjirappally, Kerala, India. He received his B.E. degree in Electrical and Electronics Engineering in 2007 and his M.E. degree in Applied Electronics (First Rank) in 2011 from Anna University, Chennai. He earned his Ph.D. in Electronics and Communication Engineering from Sathyabama Institute of Science and Technology under a DST-funded research project. He also holds an M.Sc. in Yoga for Human Excellence from Bharathidasan University and a Sanskrit Kovidha certification from Samskrita Bharathi, Kerala. With over 17 years of teaching and research experience, his interests include medical image processing, artificial intelligence, embedded systems, and telemedicine applications. He has published 45 journal articles, 90 book chapters, 12 textbooks, edited six books, and holds 16 utility patent publications and three granted design patents. He is a life member of BMESI, ISTE, ISRD, and IAENG.

Dr. Niyaz Ahmad Wani is an Assistant Professor in the Department of Computer Applications at Manipal University Jaipur, India. He received his Ph.D. in Computer Science and Engineering from Thapar Institute of Engineering and Technology, Patiala. His research focuses on Explainable Artificial Intelligence (XAI), Machine Learning, Deep Learning, Federated Learning, Medical Image Analysis, and Smart Healthcare Systems. He has authored more than 45 research articles in leading SCI-indexed journals, including Information Fusion, Engineering Applications of Artificial Intelligence, Computer Methods and Programs in Biomedicine, IEEE Transactions on Consumer Electronics, IEEE Journal of Biomedical and Health Informatics, and Scientific Reports. He is a Professional Member of IEEE and ACM, with research dedicated to developing trustworthy and interpretable AI solutions for real-world healthcare applications.

Dr. Kashish Ara Shakil is an Assistant Professor in the Department of Computer Science at Princess Nourah Bint AbdulRehman University, Riyadh, Saudi Arabia, with over five years of academic research experience and more than a year in the IT industry. She holds a Ph.D. in Cloud Computing from Jamia Millia Islamia and specializes in big data analytics, cloud computing, and machine learning. With a research portfolio that includes numerous publications in Scopus and SCIE-indexed journals, her work spans resource allocation, educational cloud systems, and biomedical data analysis. She has delivered multiple invited talks on cloud and big data for the Government of India and served as a visiting researcher at the University of Melbourne. Dr. Shakil is also an active reviewer for top-tier journals, member of professional organizations like IEEE and MIR Labs, and has mentored several postgraduate projects in data science and bioinformatics.

Dr. Momina Shaheen is a Lecturer in Computer Science and Programme Leader for BSc Web Design and Development at the University of Roehampton, UK, with over seven years of experience in teaching and research. Her expertise spans Edge Computing, IoT, Digital Twin, Federated Learning, and Cybersecurity, focusing on their applications in the Metaverse, Smart Learning, FinTech, Healthcare, and Industry 4.0. She has published over 28 high-impact research articles in prestigious Q1 journals, serves as a book editor and international session chair, and is actively involved in leading research collaborations and academic partnerships worldwide. Currently pursuing her Ph.D. in Computer Science, she also supervises dissertations, leads modules in AI and Software Engineering, and contributes as a peer reviewer for reputed journals like IEEE Access and ACM TAAS.

Dr. Mudasir Ahmad Wani is a seasoned researcher and academic currently serving as a Postdoctoral Researcher and Assistant Professor at Prince Sultan University, Saudi Arabia, with affiliations at the Norwegian University of Science and Technology (NTNU), Norway. With over 8 years of research experience, his expertise lies in Generative Artificial Intelligence, Natural Language Processing (NLP), Deep Learning, and Computational Linguistics, especially in the context of healthcare, cybersecurity, and social media analytics. He holds a Ph.D. in Computer Science from Jamia Millia Islamia, India, where his research focused on fake profile detection in online social networks. Dr. Wani has secured substantial research funding, authored impactful publications in IEEE and Springer journals, and contributed to high-profile international projects. He has delivered invited talks globally, organized major international conferences, and supervised over 30 master’s theses. His technical acumen spans Python, MATLAB, NLP frameworks, and he is also the editor of several academic books on AI and cybersecurity.