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: Harnessing IoT, Big Data, Crop Modeling, and AI for Agricultural Production covers the integration and application of advanced digital technologies including the Internet of Things, big data analytics, crop modeling, GIS, remote sensing and AI to improve agricultural outputs and sustainability. Providing a comprehensive exploration of the integration of advanced digital technologies in agriculture, the book addresses the urgent need to modernize agricultural practices through the use of advanced digital technologies. It incudes practical guidance, supporting research, and facilitates the transformation of agriculture into a more efficient, sustainable, and productive sector, ultimately contributing to global food security and environmental sustainability. This book will enhance understanding of the use of digital solutions in modernizing agriculture as well as providing practical insights for their effective integration.

Autoren/Hrsg.

Himanshu, Sushil Kumar

Kumar, Hemendra

Gupta, Pankaj Kumar

Fachgebiete

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

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)

Kumar, Hemendra
Dr. Hemendra Kumar is a precision agriculture faculty 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.

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 Assistant Professor of Food, Agriculture, and Natural Resources at the 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.