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.
This book gives a detailed description about integration of blockchain technology and digital twin solutions in modern farming. This book covers the core principles, practical applications, and some real-world case studies, with offering a practical roadmap for implementing smart agriculture practices.
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. The Role of Artificial Intelligence and IoT to Enhance Agriculture Production and Sustainability
6. A Blockchain-Enabled Framework to Enhance Cybersecurity Challenges in Smart Agriculture
7. Transformative Potential: IoT, Cloud, Big Data, AI-Driven Blockchain Digital Twins in Agriculture
8. Blockchain Implementation in IoT-Digital Twin-Based Crop Production
9. Digital Twin Applications in the Agriculture and Farming Domain
10. A Blockchain-Based Security Framework for Smart Agriculture: Harvesting Smart Agriculture Supply Chains Transformed by Blockchain Efficiency
11. A Systematic Review of Plant Leaf Disease Identification Using Image Search Engine
12. Precision Agriculture: Navigating the Future of Farming with Artificial Intelligence
13. Smarter Yields, Sustainable Practices: Predictive Modelling for Informed Decision Making in Smart Farming
14. Tech-Driven Harvest: Blockchain's Role in Sustainable Food Systems and a Waste-Free Future
15. Digital Twin for the Agriculture Sector
16. Smart Farming with Blockchain: Revolutionizing Medicinal Plant Cultivation
17. Insights into Coral Reefs of Western Indian Ocean: A Comprehensive Analysis of Habitat Classification, Forecasting Taxa Changes and AI-Driven Health Assessment
18. Farm Easy: Smart Farming Practices for Sustainable Agriculture
19. Block Chain and Digital Twin Applications in Precision Agriculture: A Comprehensive Approach
20. Innovative Applications of Digital Twin and Blockchain in Agriculture 4.0
21. Blockchain and Digital Twin Applications in Smart Agriculture
22. Enabling Blockchain in Digital Twins for Decentralized Supply Chain Traceability and Crop Growth Optimization
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.
