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).
1. Hyperautomation in Precision Agriculture - p. i 2. Cover - p. i 3. Title page - p. i 4. Copyright Page - p. iv 5. Table of Contents - p. iv 6. List of contributors - p. xiii 7. Chapter 1 A global overview and the fundamentals of sustainable agriculture - p. 3 8. Chapter 2 Smart contracts for efficient resource allocation and management in hyperautomated agricultural information systems - p. 15 9. Chapter 3 Towards smart farming: applications of artificial intelligence and internet of things in precision agriculture - p. 27 10. Chapter 4 Hyperautomation in agriculture sector by technological devices toward irrigation, crop harvest, and storage - p. 39 11. Chapter 5 Artificial intelligence-powered agriculture and sustainable practices in developing countries - p. 49 12. Chapter 6 A light-weight deep learning model for plant disease detection in hyperautomation - p. 65 13. Chapter 7 Mapping and retrieval of agricultural parameters using artificial intelligence - p. 71 14. Chapter 8 Sustainable plant disease protection using machine learning and deep learning - p. 81 15. Chapter 9 Cereal crop yield prediction using machine learning techniques - p. 97 16. Chapter 10 Estimation of soil properties for sustainable crop production using multisource data fusion - p. 113 17. Chapter 11 Detecting the stages of ragi crop diseases using satellite data in villages of Nanjangud Taluk - p. 131 18. Chapter 12 Soil and field analysis using unmanned aerial vehicles for smart and sustainable farming - p. 147 19. Chapter 13 Crop land assessment with deep neural network using hyperspectral satellite dataset - p. 159 20. Chapter 14 Development of soil moisture maps using image fusion of SCATSAT-1 and MODIS Dataset - p. 169 21. Chapter 15 Advance remote sensing technologies for crop disease and pest detection - p. 181 22. Chapter 16 Estimating soil moisture in semiarid areas for winter wheat using Sentinel-1 and support vector algorithms - p. 191 23. Chapter 17 Design, integration, and field evaluation of a robotic leaf cutting and retrieving system - p. 203 24. Chapter 18 Robotics-assisted precision and sustainable irrigation, harvesting, and fertilizing processes - p. 217 25. Chapter 19 Computer vision technology for weed detection - p. 227 26. Chapter 20 Light detection and ranging/radio detection and ranging robots in monitoring and mapping crop growth for sustainable crop production - p. 237 27. Chapter 21 Is hyperautomation playing a significant role in smart agriculture? - p. 251 28. Chapter 22 Predictive irrigation: current practice and future prospects - p. 259 29. Chapter 23 Design and development of a quadcopter for agricultural seeding - p. 277 30. Chapter 24 Challenges and future trends in the hyperautomation of sustainable agriculture - p. 289 31. Chapter 25 Techniques and applications of deep learning in smart agriculture systems - p. 299 32. Chapter 26 Investigation of automated plant disease detection framework using machine learning classifier with novel segmentation and feature extraction strategy - p. 313 33. Chapter 27 Hyperautomation in precision agriculture using different unmanned aerial vehicles - p. 323 34. Chapter 28 Emerging trends of hyperautomation in decision making process and sustainable Crop Production - p. 331 35. Chapter 29 Remote sensors for hyperautomation in agriculture - p. 341 36. Index - p. 351 37. Index - Continued - p. 360
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).
1. Hyperautomation in Precision Agriculture - p. i 2. Cover - p. i 3. Title page - p. i 4. Copyright Page - p. iv 5. Table of Contents - p. iv 6. List of contributors - p. xiii 7. Chapter 1 A global overview and the fundamentals of sustainable agriculture - p. 3 8. Chapter 2 Smart contracts for efficient resource allocation and management in hyperautomated agricultural information systems - p. 15 9. Chapter 3 Towards smart farming: applications of artificial intelligence and internet of things in precision agriculture - p. 27 10. Chapter 4 Hyperautomation in agriculture sector by technological devices toward irrigation, crop harvest, and storage - p. 39 11. Chapter 5 Artificial intelligence-powered agriculture and sustainable practices in developing countries - p. 49 12. Chapter 6 A light-weight deep learning model for plant disease detection in hyperautomation - p. 65 13. Chapter 7 Mapping and retrieval of agricultural parameters using artificial intelligence - p. 71 14. Chapter 8 Sustainable plant disease protection using machine learning and deep learning - p. 81 15. Chapter 9 Cereal crop yield prediction using machine learning techniques - p. 97 16. Chapter 10 Estimation of soil properties for sustainable crop production using multisource data fusion - p. 113 17. Chapter 11 Detecting the stages of ragi crop diseases using satellite data in villages of Nanjangud Taluk - p. 131 18. Chapter 12 Soil and field analysis using unmanned aerial vehicles for smart and sustainable farming - p. 147 19. Chapter 13 Crop land assessment with deep neural network using hyperspectral satellite dataset - p. 159 20. Chapter 14 Development of soil moisture maps using image fusion of SCATSAT-1 and MODIS Dataset - p. 169 21. Chapter 15 Advance remote sensing technologies for crop disease and pest detection - p. 181 22. Chapter 16 Estimating soil moisture in semiarid areas for winter wheat using Sentinel-1 and support vector algorithms - p. 191 23. Chapter 17 Design, integration, and field evaluation of a robotic leaf cutting and retrieving system - p. 203 24. Chapter 18 Robotics-assisted precision and sustainable irrigation, harvesting, and fertilizing processes - p. 217 25. Chapter 19 Computer vision technology for weed detection - p. 227 26. Chapter 20 Light detection and ranging/radio detection and ranging robots in monitoring and mapping crop growth for sustainable crop production - p. 237 27. Chapter 21 Is hyperautomation playing a significant role in smart agriculture? - p. 251 28. Chapter 22 Predictive irrigation: current practice and future prospects - p. 259 29. Chapter 23 Design and development of a quadcopter for agricultural seeding - p. 277 30. Chapter 24 Challenges and future trends in the hyperautomation of sustainable agriculture - p. 289 31. Chapter 25 Techniques and applications of deep learning in smart agriculture systems - p. 299 32. Chapter 26 Investigation of automated plant disease detection framework using machine learning classifier with novel segmentation and feature extraction strategy - p. 313 33. Chapter 27 Hyperautomation in precision agriculture using different unmanned aerial vehicles - p. 323 34. Chapter 28 Emerging trends of hyperautomation in decision making process and sustainable Crop Production - p. 331 35. Chapter 29 Remote sensors for hyperautomation in agriculture - p. 341 36. Index - p. 351 37. Index - Continued - p. 360
