Precision Technologies for Digital Agriculture investigates the growing role of computational and connected approaches in contemporary agricultural environments, bringing together recent findings in artificial intelligence, sensing infrastructures, agroecosystem modeling, and data analytics to illustrate decisive shifts toward more accurate, forward-looking, and responsive decision-making.
Early chapters set out the theoretical and technical underpinnings of digital and precision agriculture, addressing automation, data stewardship, privacy, and the development of dependable AI‑based tools. These foundations support a detailed overview of key technological building blocks, including remote and proximal sensing, geospatial platforms, machine vision, digital twins, and the Internet of Living Things, with emphasis on their contribution to integrated system management and optimized resource allocation.
Later sections concentrate on applied methods across field operations. Topics include climate‑adaptive, smart irrigation, soil and microbial diagnostics, UAV‑enabled monitoring, and algorithmic frameworks for yield estimation and nutrient regulation. Across these chapters, attention is given to both implementation constraints and enabling factors, including interoperability, data security, and scalability.
The volume concludes with regionally grounded case studies and economic assessments that explore adoption strategies, governance arrangements, and cost–benefit considerations across diverse crops and production settings. Together, these cutting-edge analyses provide a structured assessment of how digital technologies are being translated into transformative agricultural practice, and of the contextual conditions under which their effective use can be fully realized.
1. Title of Book - p. i 2. Cover Image - p. i 3. Title Page - p. ii 4. Table of Contents - p. iii 5. Copyright - p. iv 6. Contributors - p. xiii 7. Section I: Smart precision technologies for digital agriculture - p. S1 8. 1 Smart agricultural technologies: Challenges and opportunities from today’s fields to the future - p. 3 9. 2 Advancements in agricultural automation: Smart greenhouses and controlled environment agriculture - p. 31 10. Chapter 3 Data sharing, privacy management strategies, and security protocols for smart agricultural systems - p. 59 11. 4 Trustworthy artificial intelligence for digital agriculture - p. 75 12. 5 Precision farming with the Internet of Everything - p. 95 13. Section II: Computer vision, digital tools, and sensing technologies for modern agriculture - p. S2 14. 6 Harnessing remote sensing and geographic information systems in precision and digital agriculture - p. 117 15. 7 Integration of computer vision for smart agriculture: Fundamentals and applications - p. 141 16. 8 Emergence and application of the Internet of Living Things in precision farming - p. 165 17. 9 Enhancing crop management with digital twin technology - p. 173 18. 10 Predicting crop yield and nutrient needs using explainable artificial intelligence - p. 189 19. Section III: Deployment of smart agricultural technologies for informed decision-making - p. S3 20. 11 Physics-based crop modeling for climate- and water-smart irrigation management - p. 207 21. 12 Metagenomics: A next-generation tool for monitoring soil microbial communities and managing soil health - p. 231 22. 13 Next-gen data-driven precision irrigation: Application of artificial intelligence with in-situ and remote sensing measurements for management zones and variable-rate irrigation technologies - p. 261 23. 14 Optimizing unmanned aerial vehicle spray application for pesticide deposition in watermelon production systems in the Mid-Atlantic - p. 279 24. Chapter 15 Real-world applications of smart technologies in protected farming - p. 295 25. Section IV: Case studies and economic analysis of smart agricultural solutions - p. S4 26. 16 Integrating climate-informed crop modeling to optimize planting dates for sustainable productivity of mustard (Brassica juncea) - p. 321 27. 17 Assessment of the benefits, costs, and factors affecting the adoption of traceability systems in rice - p. 337 28. 18 Smart agriculture technologies in Bangladesh: Current status, adoption, and policy implications - p. 355 29. 19 Prioritization of erosion-prone areas for conservation treatments using the sediment yield index: A case study of the Lower Subansiri Dam project - p. 373 30. 20 Leveraging drone technology for agrichemical spraying and locust control: Policy insights from the Vidarbha region of Maharashtra, India - p. 391 31. Index - p. 409
Precision Technologies for Digital Agriculture investigates the growing role of computational and connected approaches in contemporary agricultural environments, bringing together recent findings in artificial intelligence, sensing infrastructures, agroecosystem modeling, and data analytics to illustrate decisive shifts toward more accurate, forward-looking, and responsive decision-making.
Early chapters set out the theoretical and technical underpinnings of digital and precision agriculture, addressing automation, data stewardship, privacy, and the development of dependable AI‑based tools. These foundations support a detailed overview of key technological building blocks, including remote and proximal sensing, geospatial platforms, machine vision, digital twins, and the Internet of Living Things, with emphasis on their contribution to integrated system management and optimized resource allocation.
Later sections concentrate on applied methods across field operations. Topics include climate‑adaptive, smart irrigation, soil and microbial diagnostics, UAV‑enabled monitoring, and algorithmic frameworks for yield estimation and nutrient regulation. Across these chapters, attention is given to both implementation constraints and enabling factors, including interoperability, data security, and scalability.
The volume concludes with regionally grounded case studies and economic assessments that explore adoption strategies, governance arrangements, and cost–benefit considerations across diverse crops and production settings. Together, these cutting-edge analyses provide a structured assessment of how digital technologies are being translated into transformative agricultural practice, and of the contextual conditions under which their effective use can be fully realized.
1. Title of Book - p. i 2. Cover Image - p. i 3. Title Page - p. ii 4. Table of Contents - p. iii 5. Copyright - p. iv 6. Contributors - p. xiii 7. Section I: Smart precision technologies for digital agriculture - p. S1 8. 1 Smart agricultural technologies: Challenges and opportunities from today’s fields to the future - p. 3 9. 2 Advancements in agricultural automation: Smart greenhouses and controlled environment agriculture - p. 31 10. Chapter 3 Data sharing, privacy management strategies, and security protocols for smart agricultural systems - p. 59 11. 4 Trustworthy artificial intelligence for digital agriculture - p. 75 12. 5 Precision farming with the Internet of Everything - p. 95 13. Section II: Computer vision, digital tools, and sensing technologies for modern agriculture - p. S2 14. 6 Harnessing remote sensing and geographic information systems in precision and digital agriculture - p. 117 15. 7 Integration of computer vision for smart agriculture: Fundamentals and applications - p. 141 16. 8 Emergence and application of the Internet of Living Things in precision farming - p. 165 17. 9 Enhancing crop management with digital twin technology - p. 173 18. 10 Predicting crop yield and nutrient needs using explainable artificial intelligence - p. 189 19. Section III: Deployment of smart agricultural technologies for informed decision-making - p. S3 20. 11 Physics-based crop modeling for climate- and water-smart irrigation management - p. 207 21. 12 Metagenomics: A next-generation tool for monitoring soil microbial communities and managing soil health - p. 231 22. 13 Next-gen data-driven precision irrigation: Application of artificial intelligence with in-situ and remote sensing measurements for management zones and variable-rate irrigation technologies - p. 261 23. 14 Optimizing unmanned aerial vehicle spray application for pesticide deposition in watermelon production systems in the Mid-Atlantic - p. 279 24. Chapter 15 Real-world applications of smart technologies in protected farming - p. 295 25. Section IV: Case studies and economic analysis of smart agricultural solutions - p. S4 26. 16 Integrating climate-informed crop modeling to optimize planting dates for sustainable productivity of mustard (Brassica juncea) - p. 321 27. 17 Assessment of the benefits, costs, and factors affecting the adoption of traceability systems in rice - p. 337 28. 18 Smart agriculture technologies in Bangladesh: Current status, adoption, and policy implications - p. 355 29. 19 Prioritization of erosion-prone areas for conservation treatments using the sediment yield index: A case study of the Lower Subansiri Dam project - p. 373 30. 20 Leveraging drone technology for agrichemical spraying and locust control: Policy insights from the Vidarbha region of Maharashtra, India - p. 391 31. Index - p. 409
