Hyperautomation in Precision Agriculture

Auteur
Dr. Singh received his B.Tech., M.Tech., and PhD degrees in Electronics and Communication Engineering from I. K. Gujral Punjab Technical University, India, in 2009, 2011, and 2018, respectively. Since 2012, he has been an Associate Professor with the Dept. of Electronics and Communication Engineering. He is also a visiting researcher (Teacher Associateship for Research Excellence Fellowship awardee, Science and Engineering Research Board) with the Department of Civil Engineering, Indian Institute of Technology (IIT) Ropar, Rupnagar, India. His research interests include electronic sensors; remote sensing of agriculture and digital image processing, in particular, scatterometer, classification, and data fusion. Dr. Sood received her B.Tech. degree in Electronics and Communication Engineering from Himachal Pradesh University, Shimla, India, in 2008; M.Tech. degree from I. K. Gujral Punjab Technical University, India, in 2011; and a PhD degree in Electronics and Communication Engineering from Chitkara University, India, in 2020. Her research interests include deep learning, machine learning, and digital image processing.Dr. Arun Lal Srivastav is working as an Associate Professor at Chitkara University, Himachal Pradesh in India. He is currently involved in the teaching of environmental science, environmental engineering, and disaster management to the undergraduate engineering students. His research interests include water treatment, river ecosystem, climate change, soil health maintenance, phytoremediation, and waste management. He has published around 91 research publication (as per SCOPUS) in various peer-reviewed, national and international journals, conferences, and books. He has also filed 25 patents on multidisciplinary topics. He is also working on four government-sponsored projects (worth ~16 million INR) on phytoremediation, adsorption, capacity building, organic farming, leachate treatment, agro-waste management, and so on.

Associate Professor Yiannis Ampatzidis
works in the Southwest Florida Research and Education Center (SWFREC), Immokalee, Florida, USA.

Texte du rabat
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).

Contenu

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