Precision Technologies for Digital Agriculture

Harnessing Iot, Big Data, Crop Modeling, and AI for Agricultural Production
Erscheinungsjahr 2026
ISBN: 978-0-443-40513-6
Verlag: Elsevier Science

Buch, Englisch, 400 Seiten, Format (B × H): 152 mm x 229 mm, Gewicht: 449 g

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 to 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.

Autoren/Hrsg.

Himanshu, Sushil Kumar

Kumar, Hemendra

Gupta, Pankaj Kumar

Fachgebiete

• Naturwissenschaften Agrarwissenschaften Agrarwissenschaften
• Mathematik | Informatik EDV | Informatik EDV & Informatik Allgemein
• Technische Wissenschaften Technik Allgemein Technik: Allgemeines
• Wirtschaftswissenschaften Wirtschaftssektoren & Branchen Primärer Sektor

Inhaltsverzeichnis

Section I: Introduction to Smart Precision Technologies for Digital Agriculture
1. Smart Agricultural Technologies: Challenges and Opportunities from Fields to the Future
2. Advancements in Agricultural Automation: Smart Greenhouses and Controlled Environment Agriculture
3. Data Sharing, Privacy, and Management Strategies and Security Protocols for Smart Agricultural Systems
4. Trustworthy Artificial Intelligence for Digital Agriculture
5. Precision Farming with the Internet of Everything

Section II: Computer Vision of Digital Tools and Sensing Technologies for Modern Agriculture
6. Harnessing Remote Sensing and GIS in Precision and Digital Agriculture
7. Integration of Computer Vision for Smart Agriculture: Fundamentals and Applications
8. Emergence and Application of Internet of Living Things (IoLT) in Precision Farming
9. Enhancing Crop Management with Digital Twin Technology
10. Predicting Crop Yield and Nutrient Needs Using Explainable AI for Precision Agriculture

Section III: Implementation and Deployment of Smart Agricultural Technologies for Informed Decision-Making
11. Physical-Based Crop Modelling for Climate- and Water-Smart Irrigation Management
12. Metagenomics: The Next-Generation Tools for Monitoring Soil Microbial Communities and Managing Soil Health
13. Next-Gen Data-Driven Precision Irrigation: Application of Artificial Intelligence with In-Situ and Remote Sensing Measurements for Management Zones and VRI Technologies
14. Optimizing UAV Spray Application for Pesticide Deposition in Watermelon Production Systems in the Mid-Atlantic
15. Real-World Applications of Smart Technologies in Farming

Section IV: Case Studies and Economic Analysis of Smart Agricultural Solutions
16. Integrating Crop Modeling-Based Climate Predictions to Optimize Planting Dates for Sustainable Crop Productivity of Mustard (Brassica juncea L.)
17. Assessment of the Benefits, Costs, and Factors Affecting the Adoption of Traceability System in Rice
18. Smart Agriculture Technologies in Bangladesh: Current Status, Adoption and Policy Implications
19. Prioritization of Erosion-Prone Areas for Conservation Treatments Using Sediment Yield Index: A Case Study of the Lower Subansiri Dam Project
20. Leveraging Drone Technology for Agri-Chemical Spraying and Locust Control: Policy Insights from the Vidarbha Region of Maharashtra, India

Über Autor(innen)

Gupta, Pankaj Kumar
Dr. Pankaj Kumar Gupta is a Ramanujan fellow at the Indian Institute of Technology (I.I.T.) Delhi, India and post-doctoral fellow in the faculty of environment, University of Waterloo, Canada. His current research focuses on investigating the behavior of pollutants in peatlands (Canada) and mineral aquifers (India) under dynamically fluctuating groundwater table conditions. Majority of his works focus on two areas: (1) understanding the occurrence of bio-geo-chemical interactions when pollutants migrate into groundwater systems; and (2) developing remediation strategies. Dr. Gupta hsa in-depth experience in incorporating novel technologies to map soil-water systems in more than 30sites in India. He is passionate about interdisciplinary research and teaching to understand multi-scale interactions between different components of the subsurface environment, especially the soil- groundwater-pollutant-microbes system.

Himanshu, Sushil Kumar
Dr. Sushil Kumar Himanshu is an Associate Professor and Deputy Dean at the Faculty of Food, Agriculture, and Natural Resources, Asian Institute of Technology (AIT), Thailand. He obtained his M.Tech in Hydrology and PhD in Water Resources Development and Management from the Indian Institute of Technology (IIT) Roorkee and conducted postdoctoral research at Texas A&M University. Dr. Himanshu’s research focuses on the applications of generative AI, remote sensing, and modeling applications for climate-smart water management. His research has been funded by ADB, World Bank, UN-FAO, USDA, and other agencies to support agricultural and aquacultural sustainability, food security, and water security. He is a co-editor of the Elsevier book Advances in Remediation Techniques for Polluted Soils and Groundwater.

Kumar, Hemendra
Dr. Hemendra Kumar is a precision agriculture specialist at the College of Agriculture and Natural Resources, University of Maryland College Park. His extensive experience and international exposure have profoundly influenced his innovative approaches to precision agricultural research and advanced irrigation water management. Dr. Kumar's work focuses on optimizing irrigation timing and thresholds through data collected from fields. His innovative methods include optimizing variable rate irrigation scheduling based on management zones. His research spans precision water management, smart agricultural technologies, climate-smart irrigation management, soil moisture optimization, agricultural water management, digital agriculture, hydrology, water quality, and surface and subsurface hydrologic modeling. Central to his work is the utilization of sensors, technologies, and data-driven artificial intelligence and machine learning methodologies to enhance water management in agriculture, including precision irrigation and drainage water management.