Narayan Vyas, an academician at Vivekananda Global University, specializes in computer science, focusing on IoT and Mobile App Development. He has trained over 1000 students globally and published extensively in Scopus journals. An active IEEE member, his research interests include Remote Sensing, Machine Learning, and Computer Vision.
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AI for SAR-Based Precision Agriculture
Publisher: IET
Editors: Sartajvir Singh, Vishakha Sood, Narayan Vyas, Akshar Tripathi
This book, AI for SAR-Based Precision Agriculture, offers an in-depth exploration of how Artificial Intelligence (AI) and Synthetic Aperture Radar (SAR) technologies are revolutionizing modern agriculture. It covers the theoretical foundations of SAR imaging, including sensor systems, scattering mechanisms, and signal interpretation, alongside advanced AI-driven models for agricultural monitoring.
| Important Dates | |
|---|---|
| Abstract Submission Deadline | 20 December 2025 |
| Abstract Acceptance Notification | 05 February 2026 |
| Full Chapter Submission Deadline | 30 March 2026 |
| Chapter Acceptance Notification | 30 May 2026 |
| Projected Book Release Date | June 2027 |
| Important Guidelines | |
|---|---|
| Citation Style | Vancouver |
| Formatting | 11 pt Times Roman, 1.5 line spacing |
| Originality | Plagiarism Under 10%, 0% AI Generated Content |
| Headings |
|
Chapter 1: Foundations of SAR and AI: Principles, Sensors, and Agricultural Significance
Chapter 2: AI-Powered SAR Models for Crop Monitoring, Land Use Mapping, and Classification
Chapter 3: AI-Driven Soil Moisture Retrieval Using SAR: Models and Applications
Chapter 4: PolSAR and InSAR in Agriculture: AI-Enhanced Analysis and Applications
Chapter 5: Multi-Source Data Fusion for Precision Agriculture: Kalman Filters, AI Methods, and Multi-Resolution Integration
Chapter 6: Time-Series SAR Analytics for Crop Growth Stages Using Sentinel-1 SAR Data
Chapter 7: Flood and Drought Impact Assessment on Agricultural Lands Using SAR
Chapter 8: AI-Driven Crop Type Classification Using SAR: Models and Regional Case Studies
Chapter 9: Yield Estimation and Forecasting Using SAR-Derived Indicators
Chapter 10: Monitoring Agricultural Water Use and Irrigation Efficiency with SAR Imagery
Chapter 11: Change Detection and Land Dynamics Analysis Using Multi-Temporal SAR Data
Chapter 12: AI in Multi-Seasonal Agricultural Variation Analysis Using Time-Series SAR Data
Chapter 13: SAR and Optical Fusion: Techniques, Kalman Filtering, and AI-Enhanced Monitoring
Chapter 14: Texture and Feature-Based Analysis of SAR Imagery for Crop Health Assessment
Chapter 15: Advanced DL Architectures for Multi-Temporal SAR-Based Crop Classification
Chapter 16: Spaceborne SAR Missions: Opportunities and Future Directions in Agriculture
Chapter 17: Integrating SAR with IoT and Edge Computing for Real-Time Agricultural Intelligence
Chapter 18: Next-Gen AI Models for SAR Image Understanding in Agriculture
Chapter 19: SAR in Climate-Smart Agriculture: Monitoring, Adaptation, and Resilience Strategies
Chapter 20: Ethical, Legal, and Open-Source Challenges in Operationalizing SAR for Agriculture
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