Hyperautomation in Precision Agriculture

Advancements and Opportunities for Sustainable Farming

1st Edition - November 22, 2024
Editors: Sartajvir Singh, Vishakha Sood, Arun Lal Srivastav, Yiannis Ampatzidis
Language: English
Paperback ISBN:
9 7 8 - 0 - 4 4 3 - 2 4 1 3 9 - 0
eBook ISBN:
9 7 8 - 0 - 4 4 3 - 2 4 1 4 0 - 6

Hyperautomation in Precision Agriculture: Advancements and Opportunities for Sustainable Farming is the first book to focus on the integration of multiple techniques and technolog… Read more

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Hyperautomation in Precision Agriculture: Advancements and Opportunities for Sustainable Farming is the first book to focus on the integration of multiple techniques and technologies to create an ecosystem sustaining approach that doesn’t compromise soil health or environmental safety as it increases crop yield. The book highlights the integration of state-of-the-art tools and working models to address the various challenges in the field of agriculture. It also identifies and discusses the potential and challenges of hyperautomation in sustainable agriculture with respect to efficiency improvement and human enhancement of automated operations.

Hyperautomation is a true digital transformation in sustainable agriculture utilizing advanced techniques such as robotic process automation (RPA), digital process automation (DPA), unmanned aerial vehicle (UAV), controlled-environment agriculture (CEA), remote sensing, internet of things (IoT), crop modeling, precision farming, sustainable yield, image analysis, data fusion, artificial intelligence (AI), machine learning (ML), and deep learning (DL).

Title of Book
Cover image
Title page
Table of Contents
Copyright
List of contributors
Section I: Fundamentals of hyperautomation technology for sustainable agriculture
Chapter 1. A global overview and the fundamentals of sustainable agriculture
Abstract
1.1 Introduction
1.2 Why sustainable agriculture is important in the global context
1.3 Global perspectives on sustainable agriculture
1.4 Barriers and challenges to implementing sustainable agriculture globally
1.5 Recent advances and future directions in sustainable agriculture: global scenario
1.6 Institutions promoting sustainable agriculture and its importance
1.7 Summary
References
Chapter 2. Smart contracts for efficient resource allocation and management in hyperautomated agricultural information systems
Abstract
2.1 Introduction
2.2 Theoretical framework
2.3 Case studies
2.4 Challenges
2.5 Policy implications
2.6 Conclusion
References
Chapter 3. Towards smart farming: applications of artificial intelligence and internet of things in precision agriculture
Abstract
3.1 Introduction
3.2 Importance and applications of artificial intelligence and internet of things in smart farming
3.3 Challenges of artificial intelligence and internet of things in smart farming
3.4 Balancing technology adoption with traditional farming practices
3.5 Future directions and potential developments
3.6 Conclusions
References
Chapter 4. Hyperautomation in agriculture sector by technological devices toward irrigation, crop harvest, and storage
Abstract
4.1 Introduction
4.2 Experimental setup
4.3 Results and discussion
4.4 Conclusion
References
Further reading
Chapter 5. Artificial intelligence-powered agriculture and sustainable practices in developing countries
Abstract
5.1 Introduction
5.2 Artificial intelligence in agriculture for developing countries
5.3 Sustainable agriculture and the use of artificial intelligence
5.4 Challenges in artificial intelligence-powered agriculture in developing countries
5.5 Successful artificial intelligence-powered agriculture in developing countries
5.6 Future of artificial intelligence in agriculture in developing countries
5.7 Conclusion
References
Section II: Smart agriculture automation using advanced technologies
Chapter 6. A light-weight deep learning model for plant disease detection in hyperautomation
Abstract
6.1 Introduction
6.2 Existing literature
6.3 Proposed approach
6.4 Experimental study
6.5 Conclusions and future work
Acknowledgments
References
Chapter 7. Mapping and retrieval of agricultural parameters using artificial intelligence
Abstract
7.1 Introduction
7.2 Satellite dataset and researcher area
7.3 Methodology
7.4 Analysis and discussion of the result
7.5 Conclusion
References
Chapter 8. Sustainable plant disease protection using machine learning and deep learning
Abstract
8.1 Introduction
8.2 Plant disease protection and technological advancements
8.3 Traditional and deep learning techniques used in plant disease protection for model development
8.4 Performance measures for evaluating machine learning and deep learning models used in plant disease protection
8.5 Application of machine learning and deep learning in plant disease protection
8.6 Precision agriculture and Agriculture 5.0
8.7 Conclusion
References
Chapter 9. Cereal crop yield prediction using machine learning techniques
Abstract
9.1 Introduction
9.2 Proposed model
9.3 Experiment and result
9.4 Conclusion
References
Chapter 10. Estimation of soil properties for sustainable crop production using multisource data fusion
Abstract
10.1 Introduction
10.2 Multisource data for soil property estimation
10.3 Data fusion techniques for soil property estimation
10.4 Practical applications in precision agriculture
10.5 Future directions and conclusion
References
Section III: Advances in remote sensing for precision crop production
Chapter 11. Detecting the stages of ragi crop diseases using satellite data in villages of Nanjangud Taluk
Abstract
11.1 Introduction
11.2 Methodology
11.3 Proposed methodology
11.4 Results and discussion
11.5 Conclusion and future work
Acknowledgments
Fundings
References
Chapter 12. Soil and field analysis using unmanned aerial vehicles for smart and sustainable farming
Abstract
12.1 Introduction
12.2 Types of unmanned aerial vehicle
12.3 unmanned aerial vehicle system
12.4 Types of sensors used in agriculture
12.5 Wireless communication technology used in smart and sustainable farming
12.6 Unmanned aerial vehicle data acquisition
12.7 Photogrammetry
12.8 Vegetation indices
12.9 Applications of unmanned aerial vehicles in smart and sustainable farming systems
12.10 Conclusion
Acknowledgment
References
Chapter 13. Crop land assessment with deep neural network using hyperspectral satellite dataset
Abstract
13.1 Introduction
13.2 Study area and dataset
13.3 Methodology
13.4 Results and discussion
13.5 Conclusion
References
Chapter 14. Development of soil moisture maps using image fusion of SCATSAT-1 and MODIS Dataset
Abstract
14.1 Introduction
14.2 Study area and satellite data
14.3 Methodology
14.4 Results and discussion
14.5 Conclusion
Acknowledgments
Disclosure statement
Funding
References
Chapter 15. Advance remote sensing technologies for crop disease and pest detection
Abstract
15.1 Introduction
15.2 The utilization of remote sensing devices in agricultural methodologies
15.3 The remote sensing techniques used for monitoring plant diseases and pests
15.4 The monitoring of plant disease and pests presents many challenges and exhibits notable patterns
15.5 Conclusion
References
Chapter 16. Estimating soil moisture in semiarid areas for winter wheat using Sentinel-1 and support vector algorithms
Abstract
16.1 Introduction
16.2 Data description
16.3 Support vector regression method
16.4 Results and discussion
16.5 Conclusion
References
Section IV: Robotic/Digital process automation (RPA/DPA) in agriculture and field applications
Chapter 17. Design, integration, and field evaluation of a robotic leaf cutting and retrieving system
Abstract
17.1 Introduction
17.2 Related works
17.3 System description
17.4 Experimental testing and results
17.5 Conclusions
Acknowledgments
Funding
References
Chapter 18. Robotics-assisted precision and sustainable irrigation, harvesting, and fertilizing processes
Abstract
18.1 Introduction
18.2 Literature review
18.3 Robotics in agriculture operation lifecycle
18.4 Robotics in precision irrigation
18.5 Robotics in harvesting processes
18.6 Robotics in fertilization processes
18.7 Future prospects and new trends in agriculture with robotic assistance
18.8 Conclusion
References
Chapter 19. Computer vision technology for weed detection
Abstract
19.1 Introduction
19.2 Weed detection based on different modal images
19.3 Weed detection algorithms based on machine learning
19.4 An instance of weed detection in the wheat field based on multimodal image fusion
19.5 Conclusion and prospect of weed detection based on computer vision
References
Chapter 20. Light detection and ranging/radio detection and ranging robots in monitoring and mapping crop growth for sustainable crop production
Abstract
20.1 Introduction
20.2 Light detection and ranging and radio detection and ranging applications in precision agriculture
20.3 Case study of agricultural models
20.4 Conclusion
References
Further reading
Section V: Emerging trends and case studies in hyperautomation of sustainable agriculture
Chapter 21. Is hyperautomation playing a significant role in smart agriculture?
Abstract
21.1 Introduction
21.2 Academic literature
21.3 Smart agriculture
21.4 Smart agriculture – hyperautomation
21.5 A case study
21.6 Discussions and conclusions
References
Chapter 22. Predictive irrigation: current practice and future prospects
Abstract
22.1 Introduction
22.2 Current irrigation practices
22.3 Requirement of predictive irrigation
22.4 Case study of India
22.5 Current progress in predictive irrigation
22.6 Challenges
22.7 Future research
22.8 Conclusion
Funding
References
Chapter 23. Design and development of a quadcopter for agricultural seeding
Abstract
23.1 Introduction
23.2 Parts list of quadcopter
23.3 Various applications of drones in agriculture
23.4 Types of agrobots
23.5 Seed dispersing system
23.6 Design of drone
23.7 Analysis load data
23.8 Working of seeding system
23.9 Conclusions
References
Chapter 24. Challenges and future trends in the hyperautomation of sustainable agriculture
Abstract
24.1 Introduction
24.2 Hyperautomation and sustainable agriculture
24.3 Technologies employed in the agricultural automation
24.4 Challenges and future trends
24.5 Conclusions
References
Chapter 25. Techniques and applications of deep learning in smart agriculture systems
Abstract
25.1 Introduction
25.2 Survey on deep learning methods
25.3 Survey on deep learning in agricultural application
25.4 Survey on agriculture deep learning techniques
25.5 Discussion
25.6 Conclusion
References
Chapter 26. Investigation of automated plant disease detection framework using machine learning classifier with novel segmentation and feature extraction strategy
Abstract
26.1 Introduction
26.2 Literature survey
26.3 Methodology
26.4 Result and discussion
26.5 Conclusion
References
Chapter 27. Hyperautomation in precision agriculture using different unmanned aerial vehicles
Abstract
27.1 Introduction
27.2 Types of unmanned aerial vehicle used in the hyperautomation of agriculture
27.3 Soil analysis using unmanned aerial vehicle
27.4 Field analysis using unmanned aerial vehicle
References
Chapter 28. Emerging trends of hyperautomation in decision making process and sustainable Crop Production
Abstract
28.1 Introduction
28.2 IoT and machine learning in smart farming – enhancing agricultural efficiency
28.3 Cosine algorithm for crop and yield prediction
28.4 Proposed work
28.5 Conclusion
References
Chapter 29. Remote sensors for hyperautomation in agriculture
Abstract
29.1 Introduction
29.2 Types of sensors used for agriculture
29.3 Materials used for remote sensors
29.4 Features/parameters for remote sensing
29.5 Models and techniques for predicting plant diseases
29.6 IoT based agriculture plant monitoring
29.7 Challenges in hyperautomation in agriculture
29.8 Conclusion
References
Index

Sartajvir Singh

He is a digital image analyst with a passion for remote sensing. Presently, he is working as a Professor and Associate Director (University Institute of Engineering) at Chandigarh University, Punjab, India. He is also practice as an Indian Patent Agent (IN/PA 5806). He received his PhD (Electronics and Communication Engineering - ECE) from I.K. Gujral Punjab Technical University, Punjab, India in 2018. He received his M.Tech (ECE) as a Gold Medalist, and B.Tech (ECE) with Distinction, from Punjab Technical University in 2011 and 2009, respectively. His research interests include electronics, remote sensing, and digital image processing.

Affiliations and expertise

Professor and Associate Director, University Institute of Engineering, Chandigarh University, Gharuan, Punjab, India

Vishakha Sood

Dr. Sood is working as Scientist at Indian Institute of Technology (IIT), Ropar, India, under Women Scientist Scheme (WOS) by Department of Science & Technology (DST), Govt. of India. She is also founder of a company named as Aiotronics Automation Pvt.Ltd. supported under Himachal Pradesh CM Startup Scheme. She has more than 10 years of experience in the field of academics and research. She received her PhD in Electronics and Communication Engineering from Chitkara University, Punjab in 2020. She has done B. Tech from Himachal Pradesh University (HPU) Shimla, 2008 and M. Tech from Punjab Technical University (PTU) in Electronics and Communication Engineering,2011. She has also done MBA in Human Resource (HR) ,2010. She has authored more than 25 SCI-indexed articles (IEEE, T&F, ELSEVIER, and SPRINGER), SCOPUS indexed book chapters and holds many inventions. Her research interests include satellite sensors, remote sensing, scatterometer and digital image analysis.

Affiliations and expertise

Scientist (Women Scientist Scheme-A, Department of Science and Technology) Civil Engineering Department at the Indian Institute of Technology (IIT), Ropar, India

Arun Lal Srivastav

Dr. Arun Lal Srivastav is an Associate Professor in the Department of Applied Sciences at Chitkara University, Himachal Pradesh, India.

Affiliations and expertise

Chitkara University, Himachal Pradesh, Solan, India

Yiannis Ampatzidis

Dr. Ampatzidis is an Associate Professor leading a research and extension precision agriculture engineering program at the Southwest Florida Research and Education Center (SWFREC) in Immokalee, Florida. He received his B.S. in Agriculture Science in 2002 from the Aristotle University of Thessaloniki (AUTH) in Thessaloniki, Greece, with a major in row crops and ecology. After completing his bachelor’s degree, he enrolled in the Agricultural Engineering department and completed his M.S. in 2005 with an emphasis on precision agriculture and robotics. At the same time, he completed his second bachelor’s degree in Hydraulics, Soil Science and Agricultural Engineering in 2008 (AUTH). He received his PhD in Agricultural Engineering from AUTH in 2010 with an emphasis on precision agriculture and automation for specialty crops. His current research focuses on the mechanical harvesting of specialty crops, mechatronics, artificial intelligence, machine vision, precision agriculture, smart machines (e.g., smart spraying technologies), UAVs, and machine systems.

Affiliations and expertise

Department of Agricultural and Biological Engineering, University of Florida, Gainesville, FL, USA.

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Hyperautomation in Precision Agriculture

Advancements and Opportunities for Sustainable Farming

Purchase options

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Institutional subscription on ScienceDirect

Sartajvir Singh

Vishakha Sood

Arun Lal Srivastav

Yiannis Ampatzidis

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