Hyperautomation in Precision Agriculture
Advancements and Opportunities for Sustainable Farming
- 1st Edition - November 27, 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
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).
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).
- Provides a comprehensive overview of the current state-of-the-art of automation in agriculture
- Enables improved productivity and resource optimization
- Presents advanced monitoring/mapping methods in soil properties, nutrients, crop growth, and yield
Those implementing technology in agricultural fields both in academia and industry for the furthering of research as well as production. Researchers and advanced students in sustainable agriculture and/or agricultural engineering
Section I: Fundamentals of Hyperautomation technology for sustainable agriculture
1. A global overview and the fundamentals of sustainable agriculture
2. Smart Contracts for Efficient Resource Allocation and Management in Hyperautomated Agriculture Information Systems
3. Towards Smart Farming: Applications of Artificial Intelligence and Internet of Things in Precision Agriculture
4. Hyperautomation in agriculture sector by technological devices towards irrigation, crop harvest and storage
5. AI-Powered Agriculture and Sustainable Practices in Developing Countries
Section II: Smart agriculture automation using advanced technologies
6. A light-weight Deep Learning model for plant disease detection in hyperautomation
7. Mapping and Retrieval of Agricultural Parameters using Artificial Intelligence
8. Sustainable Plant Disease Protection Using Machine Learning and Deep Learning
9. Cereal crop yield prediction using machine learning techniques
10. Estimation of soil properties for sustainable crop production using multisource data fusion
Section III: Advances in remote sensing for precision crop production
11. Detecting the stages of Ragi crop diseases using satellite data in villages of Nanjangud taluk
12. Soil and field analysis using unmanned aerial vehicles (UAV) for smart and sustainable farming
13. Crop Land Assessment with Deep Neural Network using Hyperspectral Satellite Dataset
14. Development of Soil moisture maps using image fusion of MODIS and optical dataset
15. Advance remote sensing technologies for crop disease and pest detection
16. Estimating Soil Moisture in Semi-Arid Areas for Winter Wheat Using Sentinel-1 and Support Vector Algorithms
Section IV: Robotic/Digital Process Automation (RPA/DPA) in agriculture and field applications
17. Autonomous Robotic Leaf Retrieval
18. Robotics-assisted precision and sustainable irrigation, harvesting and fertilizing processes
19. Computer Vision Technology for Weed Detection
20. LiDAR/RADAR robots in monitoring and mapping crop growth for sustainable crop production
Section V: Emerging trends and case studies in Hyperautomation of Sustainable Agriculture
21. Is Hyper-automation is playing a significant role in Smart Agriculture?
22. Predictive Irrigation: Current practice and Future Prospects
23. Design and fabrication of quad copter for agriculture seeding
24. hallenges and future trends in the Hyperautomation of Sustainable Agriculture
25. Techniques and applications of deep learning in smart agriculture systems
26. Investigation of Automated Plant disease detection Framework using Machine Learning Classifier with novel Segmentation and Feature Extraction Strategy
27. Hyperautomation in precision agriculture using different unmanned aerial vehicles (UAV)
28. Emerging Trends of hyperautomation in decision-making process & sustainable crop production
29. Remote sensors for hyper-automation in agriculture
1. A global overview and the fundamentals of sustainable agriculture
2. Smart Contracts for Efficient Resource Allocation and Management in Hyperautomated Agriculture Information Systems
3. Towards Smart Farming: Applications of Artificial Intelligence and Internet of Things in Precision Agriculture
4. Hyperautomation in agriculture sector by technological devices towards irrigation, crop harvest and storage
5. AI-Powered Agriculture and Sustainable Practices in Developing Countries
Section II: Smart agriculture automation using advanced technologies
6. A light-weight Deep Learning model for plant disease detection in hyperautomation
7. Mapping and Retrieval of Agricultural Parameters using Artificial Intelligence
8. Sustainable Plant Disease Protection Using Machine Learning and Deep Learning
9. Cereal crop yield prediction using machine learning techniques
10. Estimation of soil properties for sustainable crop production using multisource data fusion
Section III: Advances in remote sensing for precision crop production
11. Detecting the stages of Ragi crop diseases using satellite data in villages of Nanjangud taluk
12. Soil and field analysis using unmanned aerial vehicles (UAV) for smart and sustainable farming
13. Crop Land Assessment with Deep Neural Network using Hyperspectral Satellite Dataset
14. Development of Soil moisture maps using image fusion of MODIS and optical dataset
15. Advance remote sensing technologies for crop disease and pest detection
16. Estimating Soil Moisture in Semi-Arid Areas for Winter Wheat Using Sentinel-1 and Support Vector Algorithms
Section IV: Robotic/Digital Process Automation (RPA/DPA) in agriculture and field applications
17. Autonomous Robotic Leaf Retrieval
18. Robotics-assisted precision and sustainable irrigation, harvesting and fertilizing processes
19. Computer Vision Technology for Weed Detection
20. LiDAR/RADAR robots in monitoring and mapping crop growth for sustainable crop production
Section V: Emerging trends and case studies in Hyperautomation of Sustainable Agriculture
21. Is Hyper-automation is playing a significant role in Smart Agriculture?
22. Predictive Irrigation: Current practice and Future Prospects
23. Design and fabrication of quad copter for agriculture seeding
24. hallenges and future trends in the Hyperautomation of Sustainable Agriculture
25. Techniques and applications of deep learning in smart agriculture systems
26. Investigation of Automated Plant disease detection Framework using Machine Learning Classifier with novel Segmentation and Feature Extraction Strategy
27. Hyperautomation in precision agriculture using different unmanned aerial vehicles (UAV)
28. Emerging Trends of hyperautomation in decision-making process & sustainable crop production
29. Remote sensors for hyper-automation in agriculture
- Edition: 1
- Published: November 27, 2024
- Language: English
SS
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, IndiaVS
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, IndiaAS
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, IndiaYA
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.Read Hyperautomation in Precision Agriculture on ScienceDirect