Overview

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.

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9781032817460

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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.

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