Agri 4.0 and the Future of Cyber-Physical Agricultural Systems
- 1st Edition - April 16, 2024
- Editors: Seifedine Kadry, Vandana Sharma, Rajesh Kumar Dhanaraj, Rutvij H. Jhaveri, Gandhiya Vendhan
- Language: English
- Paperback ISBN:9 7 8 - 0 - 4 4 3 - 1 3 1 8 5 - 1
- eBook ISBN:9 7 8 - 0 - 4 4 3 - 1 3 1 8 6 - 8
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 a focus on technologies that enab… Read more
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Request a sales quoteAgri 4.0 and the Future of Cyber-Physical Agricultural Systems is the first book to explore the potential use of technology in agriculture with a focus on technologies that enable 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. Specific sections explore ways to implement CPS effectively and appropriately and cover digitalization of agriculture, digital computers to assist the processes of agriculture with digitized data and allied technologies, including AI, Computer Vision, Big data, Block chain, and IoT. Other sections cover Agri 4.0 and how it can digitalize, estimate, plan, predict, and produce the optimum agricultural inputs and outputs required for commercial purposes. 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
Researchers in cyber-physical and AI focused on agricultural applications Professionals seeking agricultural solutions using cyber-physical systems. Advanced level students in related courses
- Cover image
- Title page
- Table of Contents
- Copyright
- List of contributors
- About the editors
- Preface
- Acknowledgments
- Chapter 1. Journey to cyber-physical agricultural systems digitalization and technological evolution
- Abstract
- 1.1 Introduction of agricultural cyber-physical system
- 1.2 Digitalization
- 1.3 Technological evolution
- 1.4 Internet of Things
- 1.5 Digital farming
- 1.6 Artificial intelligence and machine learning
- 1.7 Applications of cyber-physical agricultural systems
- 1.8 More about agricultural cyber-physical system
- 1.9 Tools
- 1.10 Cyber-physical farming platform
- 1.11 Real-time modeling, sensing, and optimization for data-driven decision-making
- 1.12 Crop productivity and soil nutrient analysis
- 1.13 Simulated case study and validation of theory
- 1.14 Potential paths for future studies
- 1.15 Future scope
- 1.16 Apps
- 1.17 Conclusion
- Acknowledgment
- References
- Further reading
- Chapter 2. Agricultural cyber-physical systems: evolution, basic, and fundamental concepts
- Abstract
- 2.1 Introduction
- 2.2 What is machine learning
- 2.3 Machine learning–based cyber-physical system
- 2.4 Smart grids
- 2.5 Reinforcement learning–based cyber-physical system
- 2.6 Integration of machine learning–based deep learning
- 2.7 Machine learning–based cyber-physical system detection rate
- 2.8 Techniques for machine learning
- 2.9 Some technological issues
- 2.10 Conclusion
- References
- Chapter 3. Tools and framework for cyber-physical agricultural systems
- Abstract
- 3.1 Introduction
- 3.2 Sensors for cyber-physical agricultural system
- 3.3 Communication protocols for cyber-physical agricultural system
- 3.4 Decision support systems for cyber-physical agricultural system
- 3.5 Machine learning and artificial intelligence for cyber-physical agricultural system
- 3.6 Case studies of cyber-physical agricultural system implementation
- 3.7 Conclusion
- References
- Chapter 4. Convergence of Internet of things, machine learning, blockchain, big data, cloud, 5G for building the ecosystem for cyber-physical agricultural systems
- Abstract
- 4.1 Introduction
- 4.2 Blockchain in agriculture
- 4.3 5G in agriculture
- 4.4 Internet of things in agriculture
- 4.5 Cloud computing in agriculture
- 4.6 Machine learning and big data in agriculture
- 4.7 Summary
- References
- Chapter 5. Issues and research challenges for implementing cyber-physical agricultural supply chains
- Abstract
- 5.1 Introduction
- 5.2 Different technologies in Agri 4.0
- 5.3 The effects of Internet of things on supply chain
- 5.4 Application of agricultural Internet of things
- 5.5 Cyber-physical system
- 5.6 Conclusion
- References
- Chapter 6. Economic, social, and environmental challenges in Agri 4.0
- Abstract
- 6.1 Introduction
- 6.2 Problem statement
- 6.3 Related work
- 6.4 Some general findings from the literature review
- 6.5 Objective of study
- 6.6 Methods applied to study
- 6.7 World Trade Organization
- 6.8 Dimensions of India’s agricultural exports
- 6.9 Agricultural export zone
- 6.10 Conclusion
- 6.11 Guidelines consequences
- Abbreviation
- References
- Chapter 7. Smart multilayer architecture for cyber-physical agricultural systems with Intel oneAPI
- Abstract
- 7.1 Introduction
- 7.2 Motivation
- 7.3 Problem statement
- 7.4 Intel oneAPI—a software stack for all domains, explored for agriculture
- 7.5 Proposed architecture and technicalities
- 7.6 Results and observations
- 7.7 Conclusion
- References
- Chapter 8. Blockchain-based smart supply chain and transportation for Agri 4.0
- Abstract
- 8.1 Introduction
- 8.2 Literature survey
- 8.3 Methodology
- 8.4 Experiments and performance analysis
- 8.5 Conclusion
- References
- Chapter 9. Toward precision agriculture in Cyber-Physical Agricultural System
- Abstract
- 9.1 Introduction
- 9.2 Benefits of cyber-physical system
- 9.3 Challenges faced by cyber-physical system
- 9.4 Model of Agriculture 4.0
- 9.5 Aspects of Agriculture 4.0 and its applications
- 9.6 Challenges in Agriculture 4.0
- 9.7 Developments in Agriculture 4.0
- 9.8 Use cases of cyber-physical agricultural system
- 9.9 Conclusion
- References
- Chapter 10. Fully convolutional network for edge devices—FPGA implementation and analysis for agriculture technology
- Abstract
- 10.1 Introduction
- 10.2 Background
- 10.3 Network structure and MATLAB implementation—convolutional neural network and fully convolutional network
- 10.4 Hardware architecture—FPGA implementation and validation—convolutional neural network and fully convolutional network
- 10.5 Hardware validation—results and analysis
- 10.6 Conclusion
- Acknowledgment
- Data availability statement
- References
- Chapter 11. Smart production monitoring using drones in cyber-physical agricultural systems
- Abstract
- 11.1 Introduction
- 11.2 Historical timeline of drones
- 11.3 Literature review
- 11.4 Flowchart of decision-making algorithm
- 11.5 Drone architecture
- 11.6 Working principle of drone
- 11.7 Working process of drone
- 11.8 Hardware work process
- 11.9 Software/logical working of drone
- 11.10 Application areas/potential sectors of drone technology
- 11.11 Cyber-physical agriculture system in agriculture
- 11.12 Revolution in agriculture or Agri 4.0
- 11.13 Application of drones in smart production
- 11.14 Implementation of drone in smart production and farming
- 11.15 Key points of drone employed in agriculture
- 11.16 Conclusion
- References
- Further reading
- Chapter 12. Role of recent innovations in smart agriculture systems
- Abstract
- 12.1 Introduction
- 12.2 Conceptual framework of climate-smart agriculture and climate-smart village
- 12.3 Field-based evidence from India on the agronomic, financial, and environmental benefits
- 12.4 Use of cyber-physical system in smart agriculture
- 12.5 Use of Internet of things in smart farming
- 12.6 Use of blockchain in smart agriculture
- 12.7 Use of drones in smart agriculture
- 12.8 Role of smart technology in agricultural governance
- 12.9 Conclusion
- References
- Chapter 13. AI-based pest detection and recovery model for cyber-physical agricultural systems
- Abstract
- 13.1 Introduction to the fruit detector challenges
- 13.2 Review and analysis of an experimental study
- 13.3 Methodology of the proposed work
- 13.4 Requirements for Architecture and Software
- 13.5 Results
- 13.6 Summary
- References
- Chapter 14. Automated diagnosis of disease in grape leaves using deep neural networks
- Abstract
- 14.1 Introduction
- 14.2 Related work
- 14.3 Proposed model
- 14.4 GLCM
- 14.5 Pseudo-code for GLCM
- 14.6 K-means Clustering
- 14.7 Pseudo-code for K-means clustering algorithm
- 14.8 Reading the input leaf image
- 14.9 K-means on image compression
- 14.10 Multisupport vector machine
- 14.11 Pseudo-code for multisupport vector machine
- 14.12 Results and discussions
- 14.13 Conclusion
- References
- Chapter 15. Automated crop cultivation and pesticide scheduling: a case study
- Abstract
- 15.1 Introduction
- 15.2 Artificial intelligence
- 15.3 Automated crop cultivation
- 15.4 Case studies
- 15.5 Implementation challenges
- 15.6 Percentage of respondents
- 15.7 Conclusion and future prospects
- References
- Index
- No. of pages: 330
- Language: English
- Edition: 1
- Published: April 16, 2024
- Imprint: Academic Press
- Paperback ISBN: 9780443131851
- eBook ISBN: 9780443131868
SK
Seifedine Kadry
Prof. Seifedine Kadry’s research focuses on data science, education using technology, system prognostics, stochastic systems, and applied mathematics. He is an ABET (Accreditation Board for Engineering and Technology) Program Evaluator for computing and engineering technology. He is a Fellow of IET, IETE, and IACSIT. He is a distinguished speaker for the IEEE Computer Society.
Affiliations and expertise
Department of Computer Science and Mathematics, Lebanese American University, Beirut, Lebanon; Department of Applied Data Science, Noroff University College, Kristiansand, NorwayVS
Vandana Sharma
Dr. Vandana Sharma is an Associate Professor at CHRIST (Deemed to be University), Delhi NCR, India. She is a Senior Member of IEEE, member of Women in Engineering Society and member of IEEE Consumer Technology Society popularly known as CTSoc. As a keen researcher, she has published 50+ research papers in SCI and Scopus-indexed international journals and conferences. Dr. Sharma has contributed voluntarily as a keynote speaker, session chair, Reviewer and Technical Program Committee (TPC) member for reputed International Journals and IEEE Conferences and has presented her work across India and abroad. Her primary areas of interest include Artificial Intelligence, Blockchain Technology and the Internet of Things (IoT).
Affiliations and expertise
Associate Professor, CHRIST (Deemed to be University), IndiaRD
Rajesh Kumar Dhanaraj
Dr. Rajesh Kumar Dhanaraj is a professor at the Symbiosis International (Deemed University) in Pune, India. His research and publication interests include cyber-physical systems, wireless sensor networks, and cloud computing. He is a senior member of the Institute of Electrical and Electronics Engineers (IEEE), a member of the Computer Science Teacher Association (CSTA) and member of the International Association of Engineers (IAENG). He is an expert advisory panel member of Texas Instruments Inc. (USA), and an associate editor of International Journal of Pervasive Computing and Communications (Emerald Publishing).
Affiliations and expertise
Professor, Symbiosis International (Deemed University), Pune, IndiaRJ
Rutvij H. Jhaveri
Dr. Rutvij H. Jhaveri is an experienced researcher working in the Department of Computer Science & Engineering, Pandit Deendayal Energy University (PDEU/PDPU), Gandhinagar, India He was among top 2% of scientists around the world as per the 2021 statistics provided by Stanford University and Elsevier. He serves as an editor/ guest editor in various journals of repute, he also serves as a reviewer in several international journals and also as a program committee member/reviewer in renowned international conferences. He authored several papers/book-chapters published by prominent publishing houses such as IEEE, Springer, Elsevier, Wiley, Hindawi, ACM, IET, INTECH and others. Apart from Senior Member of IEEE (SMIEEE), he possesses memberships in technical societies CSI, ISTE, IAENG, IDES and others. He is a member of the Research Advisory Board in Symbiosis Institute of Digital and Telecom Management since 2021. In 2017, he was awarded "Pedagogical Innovation Award" by Gujarat Technological University.
Affiliations and expertise
Department of Computer Science and Engineering, School of Technology, Pandit Deendayal Energy University, IndiaGV
Gandhiya Vendhan
Dr. S. Gandhiya Vendhan has been awarded Post-Doctoral Fellow from Indian Council of Medical Research (ICMR) New Delhi, IIT, India, Ph.D., M.Tech in Information Technology from Anna University and Six Sigma Black Belt from Indian Statistical Institute (ISI) and also Data Science, Big Data & IoT from SAS. He completed his M.Sc, M.Phil in Statistics from Bharathiar University, Coimbatore. He is having 8 years of teaching experience and 10 years of research experience. He received Independent Scientist Awards 2020 from You Tube. He has published 30+ papers in top journals.
Affiliations and expertise
Professor, Bharathiar University, IndiaRead Agri 4.0 and the Future of Cyber-Physical Agricultural Systems on ScienceDirect