Agri 4.0 and the Future of Cyber-Physical Agricultural Systems

Seifedine Kadry, Vandana Sharma, Rajesh Kumar Dhanaraj, Rutvij H. Jhaveri, Gandhiya Vendhan, Seifedine Kadry, Vandana Sharma, Rajesh Kumar Dhanaraj, Rutvij H. Jhaveri, Gandhiya Vendhan

• Publisher: Academic Press
• Year: 2024
• Print ISBN:
  9780443131851
• eBook ISBN:
  9780443131868
• Topic: Technology & Engineering
• Subtopic: Agribusiness
• Index: Technology & Engineering

Agri 4.0 and the Future of Cyber-Physical Agricultural Systems is the first book to explore the potential use of technology in agriculture with the focus on the technologies, enabling the reader to better comprehend the full range of CPS opportunities. From planning to distribution, CPS technologies are available to impact agricultural output, delivery and consumption. The impact for food security may be significant and this book explores ways to implement CPS effectively and appropriately. Technology, especially computing technology, can play a significant in the field of agriculture by processing digitized data to solve the complex agronomic, agricultural demand and supply issues that impact the food supply chain, and ultimately food security. In Agri 4.0, the cyber physical system synchronously interacts with agricultural systems to control and execute the operation autonomously. Digitalization of agriculture integrates digital computers to assist the processes of agriculture with its digitized data and its allied technology including AI, Computer Vision, Big data, Block chain and IoT. Agri 4.0 digitalizes, estimate, plan, predict, and produce the optimum agricultural inputs and outputs for the required for commercial purposes. It can be used to get a fair, transparent and accountable process to serve the stakeholders. The convergence of IoT, ML, Big data and 5G networks have opened new possibilities to explore and exploit the cyber physical agricultural systems. The management and practices of smart multi-layer architecture and smart supply chain are one of the key application areas in Agri 4.0. The global team of authors also presents important insights into promising areas of precision agriculture, autonomous systems, smart farming environment, smart production monitoring, pest detection and recovery, sustainable industrial practices and government policies in Agri 4.0. - Addresses one of the most complex applications of CPS - Describes various technologies, covering CPS in agriculture from precision agriculture to smart supply chain management - Focuses on the digital framework, tools, and systems capable of supporting Agri 4.0

Table of contents

• Cover
• Title page
• Table of Contents
• Copyright
• Contents
• List of contributors
• About the editors
• Preface
• Acknowledgments
• List of Illustrations
• List of Tables
• Chapter 1 : Journey to cyber-physical agricultural systems digitalization and technological evolution
• Chapter 2 : Agricultural cyber-physical systems: evolution, basic, and fundamental concepts
• Chapter 3 : Tools and framework for cyber-physical agricultural systems
• Chapter 4 : Convergence of Internet of things, machine learning, blockchain, big data, cloud, 5G for building the ecosystem for cyber-physical agricultural systems
• Chapter 5 : Issues and research challenges for implementing cyber-physical agricultural supply chains
• Chapter 6 : Economic, social, and environmental challenges in Agri 4.0
• Chapter 7 : Smart multilayer architecture for cyber-physical agricultural systems with Intel oneAPI
• Chapter 8 : Blockchain-based smart supply chain and transportation for Agri 4.0
• Chapter 9 : Toward precision agriculture in Cyber-Physical Agricultural System
• Chapter 10 : Fully convolutional network for edge devices—FPGA implementation and analysis for agriculture technology
• Chapter 11 : Smart production monitoring using drones in cyber-physical agricultural systems
• Chapter 12 : Role of recent innovations in smart agriculture systems
• Chapter 13 : AI-based pest detection and recovery model for cyber-physical agricultural systems
• Chapter 14 : Automated diagnosis of disease in grape leaves using deep neural networks
• Chapter 15 : Automated crop cultivation and pesticide scheduling: a case study
• Index
• A