Next-Gen Smart Farming
Integrating AI, IoT, Digital Twin, and Robotics for Cyber-Physical Systems
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63 663 Ft
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Product details:
- Publisher Academic Press
- Date of Publication 1 May 2026
- ISBN 9780443439186
- Binding Paperback
- No. of pages308 pages
- Size 235x191 mm
- Weight 450 g
- Language English 700
Categories
Long description:
Next-Gen Smart Farming presents a thorough, evidence-based assessment of using emerging physical and computerized systems designed to enhance agricultural efficiency. It balances demonstrated benefits with a clear articulation of potential risks.
The work surveys the entire pipeline, from sensing technologies and data collection to artificial intelligence and machine learning, spatial analysis, decision modeling, and automation. It highlights how these technologies can strengthen crop monitoring, yield forecasting, and farm management, while it also examines governance, equity, and ethical considerations essential for responsible deployment. Economic, environmental, and social impacts are analyzed, providing a comprehensive framework for evaluating trade-offs and outcomes in real-world contexts.
Framed as an interdisciplinary resource, the volume equips researchers, industry practitioners, extension specialists, and policymakers with practical methodologies, metrics, and narratives needed to implement scalable digital agriculture solutions that improve productivity, resilience, and sustainability across diverse real-world settings.
- Applies smart technologies across the entire crop-production continuum.
- Emphasizes methodological rigor, validation strategies, and transparent reporting to support replication and informed decision-making in both research and practice.
- Includes a focal case study on leaf nutrient sensing, demonstrating how data-driven insights optimize precision fertilization and resource efficiency.
Table of Contents:
1. Introduction to the use of AI technology in agriculture
2. Sensing and architecture of an agricultural cyber system
3. Geostatistics and machine learning for analyzing spatial data
4. Decision-making in smart agriculture: advanced models and techniques
5. Automation and robotics
6. Economic, environmental, social, and ethical impacts of AI in smart farming
7. Visible and near-infrared spectroscopy for nutrient analysis of leaf tissues
