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

Purchase options

Limited Offer

Save 50% on book bundles

Immediately download your ebook while waiting for your print delivery. No promo code is needed.

Institutional subscription on ScienceDirect

Request a sales quote

Resources

Textbook support for instructors(opens in new tab/window)

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 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.

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

Seifedine Kadry

Dr Seifedine Kadry has a Bachelor degree in 1999 from Lebanese University, MS degree in 2002 from Reims University (France) and EPFL (Lausanne), PhD in 2007 from Blaise Pascal University (France), HDR degree in 2017 from Rouen University. At present his research focuses on Data Science, education using technology, system prognostics, stochastic systems, and applied mathematics. He is an ABET program evaluator for computing, and ABET program evaluator for Engineering Tech. He is a Fellow of IET, Fellow of IETE, and Fellow of IACSIT. He is a distinguish speaker of IEEE Computer Society.

Affiliations and expertise

Professor, Noroff University College, Norway

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), India

Rajesh Kumar Dhanaraj

Dr. Dhanaraj holds a PhD in Information and Communication Engineering by Anna University, Chennai, 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 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 Institute of Computer Studies and Research (SICSR), Symbiosis International (Deemed University), Pune, Maharashtra, India

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, India

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, India