Blockchain and Digital Twin Applications in Smart Agriculture

1. Auflage 2025
ISBN: 978-1-032-81746-0
Verlag: Taylor & Francis Ltd

Buch, Englisch, 375 Seiten, Format (B × H): 178 mm x 254 mm

Blockchain and Digital Twin Applications in Smart Agriculture presents scenarios, solutions, applications to optimize crop management, improve resource allocation, and enhance sustainability in modern farming practices. It examines how blockchain and digital twin technology could enable farmers and stakeholders to create virtual, data-driven replicas of their agricultural operations while leveraging the security and transparency of blockchain technology. Highlights of the book include:

- An immutable blockchain ledger for storing and sharing agricultural data securely

- Decentralized data management to share data among such stakeholders as farmers, suppliers, distributors, and consumers

- Supply chain transparency to ensure product authenticity, reduce food fraud, and protect consumers

- Solutions to improve the allocation of such resources as water, fertilizer, and labor to reduce costs and improve productivity.

The book helps researchers and academics develop solutions that can transition the agricultural industry into a more data-driven, sustainable, and transparent future. It explains how creating digital twins of agricultural operations and securing data through blockchain can enhance the ability to make informed decisions, reduce waste, and ensure that agriculture practices are more efficient and environmentally responsible. Featuring practical applications and case studies, the book provides a roadmap to farming that is smart, data-driven, and more.

Zielgruppe

Postgraduate

Autoren/Hrsg.

Kumar Tyagi, Amit

Tiwari, Shrikant

Fachgebiete

• Naturwissenschaften Agrarwissenschaften Ackerbaukunde, Pflanzenbau
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz

Inhaltsverzeichnis

1. Introduction of Blockchain in Smart Agriculture 2. Smart Agriculture and Digital Twins: A Vision of Sustainability 3. Digital Twin for the Agriculture Sector 4. AI-Enabled Blockchain for Livestock Tracking and Health Management in Smart Agriculture 5. A Blockchain-Enabled Framework to Enhance Cybersecurity Challenges in Smart Agriculture 6. Transformative Potential: IoT, Cloud, Big Data, AI-Driven Blockchain Digital Twins in Agriculture 7. Blockchain Implementation in IoT-Digital Twin-Based Crop Production 8. Digital Twin Applications in the Agriculture and Farming Domain 9. A Blockchain-Enabled Security Framework for Smart Agriculture: Harvesting Smart Agriculture Supply Chains Transformed by Blockchain Efficiency 10. A Systematic Review of Plant Leaf Disease Identification Using Image Search Engine 11. Precision Agriculture: Navigating the Future of Farming with Artificial Intelligence 12. Smarter Yields, Sustainable Practices: Predictive Modelling for Informed Decision Making in Smart Farming 13. Tech-Driven Harvest: Blockchain's Role in Sustainable Food Systems and a Waste-Free Future 15. Digital Twin for the Agriculture Sector 15. Smart Farming with Blockchain: Revolutionizing Medicinal Plant Cultivation 16. Insights into Coral Reefs of Western Indian Ocean: A Comprehensive Analysis of Habitat Classification, Forecasting Taxa Changes and AI-Driven Health Assessment 17. Farm Easy: Smart Farming Practices for Sustainable Agriculture 18. Blockchain and Digital Twin Applications in Precision Agriculture: A Comprehensive Approach 19. Effective Deep Learning-Based Agricultiral Models for Classifications of Plant Diseases 20. Enabling Blockchain in Digital Twins for Decentralized Supply Chain Traceability and Crop Growth Optimization

Über Autor(innen)

Amit Kumar Tyagi is an assistant professor, Department of Fashion Technology, National Institute of Fashion Technology, New Delhi. He earned a Ph.D. from Pondicherry Central University, India. He has worked as assistant professor and senior researcher at the School of Computer Science and Engineering, Vellore Institute of Technology, Chennai, India.

Shrikant Tiwari received his Ph.D. from the Department of Computer Science & Engineering at the Indian Institute of Technology (Banaras Hindu University), Varanasi, India. Currently, he is an associate professor in the School of Computing Science and Engineering (SCSE), Galgotias University, Greater Noida, India. He has authored or co-authored more than 50 national and international journal publications, book chapters, and conference articles. He has five patents filed to his credit. His research interests include machine learning, deep learning, computer vision, medical image analysis, pattern recognition, and biometrics. Dr. Tiwari is a FIETE, a senior member of the IEEE, and member of ACM, IET, CSI, ISTE, IAENG, and SCIEI.