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Geospatial Intelligence in Precision Agriculture

Edited by Gurwinder Singh, Vishakha Sood, Narayan Vyas, Surendra Yadav, and Dankan Gowda V.
Copyright: 2026   |   Expected Pub Date: 2026
ISBN: 9781394346608  |  Hardcover  |  

Price: $225 USD
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Description
With a growing population, rising demand for food, and accelerating climate change, geospatial intelligence is reshaping how the world farms. This book delivers a comprehensive, hands-on guide to modern precision agriculture, bringing together the core enabling technologies of remote sensing, GIS, IoT, artificial intelligence, and cloud-based earth-observation platforms into one practical, decision-focused reference. From planting to harvest, readers see how location-aware data and smart sensors are used to monitor crops, manage irrigation, assess soil health, classify land use, and quantify environmental impact with a resolution and speed that traditional methods cannot match.
Across eighteen carefully sequenced chapters, the book moves from foundations to frontier applications. Each chapter is grounded in real-world case studies that show how these tools are already being deployed across India and other emerging agricultural economies. The book ends with a forward look at AI-driven analytics, multisensor fusion, and cloud technologies that will define the next decade. The result is a single, authoritative volume for students, researchers, agri-tech innovators, and policy professionals who want to understand and apply geospatial intelligence to build farming systems that are productive, profitable, and sustainable.

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Author / Editor Details
Gurwinder Singh, PhD works with the School of Advanced Computing and the Advanced Centre of Research and Innovation at Chandigarh Group of Colleges University, Mohali, Punjab, India. He is a recipient of the Young Scientist Award and a member of the International Society for Photogrammetry and Remote Sensing. His research focuses on remote sensing, agricultural land-use classification, machine learning, and deep learning.

Vishakha Sood, PhD is a Project Scientist in the Centre of Excellence SEnSRS at the Indian Institute of Technology Ropar. She holds a PhD from Chitkara University and is the founder of Aiotronics Automation Pvt. Ltd. Her research focuses on satellite sensors, remote sensing, and digital image analysis.

Narayan Vyas is with the Department of Computer Science and Engineering at Vivekananda Global University. He has authored more than 60 research publications and edited more than ten books. His research focuses on remote sensing, IoT, machine learning, deep learning, and computer vision.

Surendra Yadav, PhD is with the Department of Computer Science and Engineering at Vivekananda Global University, Jaipur, Rajasthan, India with more than 20 years of experience. He has led GIS-based projects for the Karnataka and Delhi Agricultural Marketing Boards and the National Institute of Agriculture Marketing. He has more than 50 publications spanning remote sensing, GIS, cloud computing, and data mining.

Dankan Gowda V., PhD is an Assistant Professor in the Department of Electronics and Communication Engineering at the BMS Institute of Technology and Management, Bangalore, India. He brings 15 years of teaching and industry experience, including roles at ADADRDO and Robert Bosch. His research focuses on IoT and signal processing, with more than 100 international publications and six granted patents. 

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Table of Contents
Preface
1. Introduction to Geospatial Intelligence and Precision Agriculture
Neelam Dahiya, Amanpreet Singh and Sartajvir Singh
1.1 Introduction
1.1.1 Components of GI
1.1.2 Remote Sensing
1.1.3 GPS
1.1.4 GIS
1.1.5 Spatial Analytics
1.2 Applications of GI in Precision Agriculture
1.2.1 Crop Health Monitoring
1.2.2 Soil Mapping
1.2.2.1 Variable Rate Technology (VRT)
1.2.2.2 Irrigation Management
1.2.2.3 Yield Monitoring
1.2.2.4 Weed and Pest Detection
1.3 Current Trends in the Adoption of GI in Agriculture
1.4 Challenges in Adopting GI in Agriculture
1.4.1 Low Resources and High Starting Costs
1.4.2 Technical Expertise and Digital Literacy
1.4.3 Data Integration and Standardization Challenges
1.4.4 Inadequate Infrastructure and Connectivity
1.4.5 Data Privacy, Security, and Ownership Concerns
1.4.6 Cultural and Behavioral Resistance
1.4.7 Environmental and Physical Constraints
1.5 Opportunities for Enhancing GI Adoption in Agriculture
1.5.1 Supportive Government
1.5.2 Capacity Building and Skill Development
1.5.3 Public–Private Partnerships (PPPs)
1.5.4 Integration with Climate-Smart Agriculture
1.5.5 High-Value Crops
1.6 Conclusion
References
2. The Role of Geospatial Intelligence in Agricultural Innovations
Umesh Kumar Singh, Santosh Korav and Sujatha H. T.
2.1 Introduction to Geospatial Intelligence
2.2 Current Challenges in Agriculture
2.2.1 Global Food Security
2.2.2 Climate Change and Environmental Pressures
2.2.3 Resource Management
2.2.4 Sustainability Concerns
2.3 Application of Geospatial Intelligence in Agriculture
2.3.1 Precision Agriculture
2.3.2 Irrigation Management
2.3.3 Pest and Disease Monitoring
2.3.4 Land Use Planning and Farm Management
2.3.5 Disaster and Risk Management
2.4 Technological Progress in Geospatial Intelligence Applications in Agriculture
2.4.1 Satellite Remote Sensing
2.4.2 Drones and Unmanned Aerial Vehicles
2.4.3 AI and ML
2.4.4 IoT Sensors
2.5 Case Studies and Practical Applications
2.5.1 Examples of Implemented Agricultural Innovations
2.5.2 Government and Policy Integration
2.5.3 International Collaborations
2.6 Advantages of Geospatial Intelligence in Agriculture
2.6.1 Increase in Productivity and Efficiency
2.6.2 Enhanced Crop Management
2.6.3 Sustainability
2.6.4 Carbon Sequestration
2.6.5 Climate Change Adaptation Plan
2.7 Challenges and Limitations of Geospatial Intelligence in Agriculture
2.7.1 Data Accessibility
2.7.2 Cost and Infrastructure Barriers
2.7.3 Privacy and Ethical Considerations
2.8 Future Trends and Opportunities
2.8.1 Advancements in Satellite and Sensor Technology
2.8.2 AI Integration and Predictive Analytics
2.8.3 Market Trends and Global Food Dynamics
2.8.4 Cross-Sector Collaborations
2.8.5 Conclusion
References
3. Data Acquisition and Sensing Technologies in Geospatial Intelligence
Vishvajeet D. Jadhav, Santosh Korav, Sandeep Menon and Prakash Kumar Jha
3.1 Introduction
3.2 Data Acquisition Techniques
3.2.1 Types of Data Acquisition
3.3 GEOINT and Sensing Technologies
3.4 Aerial Photography—A Vital Part of GEOINT
3.4.1 Data Fusion: Advanced Integration Techniques for Multi-Sensor Environments
3.4.2 Classification Framework
3.4.3 Data Fusion Techniques Can Be Categorized Based on the Relationships Between Data Sources
3.4.4 Abstraction and Architecture
3.4.5 System Components
3.4.6 Implementation Significance
3.5 Remote Sensing Resolution
3.6 Applications of Big Data in Agriculture
3.7 Cloud Computing in Agriculture
3.8 Challenges and Future Directions
3.8.1 Conclusion
References
4. Integrating IoT and Geospatial Technologies for Real-Time Crop and Soil Monitoring in Precision Agriculture
Aritra Guin, Santosh Korav and Sujatha H.T.
4.1 Introduction
4.2 Overview of IoT Sensors in Agriculture
4.3 Geospatial Technologies in Agriculture
4.4 Integration of IoT and Geospatial Technologies
4.5 Benefits of Real-Time Crop Management
4.5.1 More Disease-Free Crops and Higher Yields
4.5.2 Resources Used Efficiently
4.5.3 Smart Decision-Making
4.5.4 Adaptation to Climate Change
4.5.5 Increased Productivity and Profitability
4.5.6 Sustainability and Environmental Benefits
4.6 Challenges and Limitations
4.6.1 Security Related Concerns
4.6.2 Information Management
4.6.3 Fault Tolerance
4.6.4 Requirement of Memory
4.7 Future Trends
4.8 Conclusion
References
5. Real-Time Crop Management with IoT Sensors and Geospatial Technologies
R. Kanthavel, R. Adline Freeda and R. Dhaya
5.1 Introduction
5.1.1 Introduction of IoT and Overview of Geospatial Technologies in Agriculture
5.1.2 Convergence of IoT and Geospatial Technologies
5.1.3 Impact on Contemporary Agriculture
5.2 Real-Time Crop Management with IoT Sensors and Geospatial Technologies
5.2.1 IoT Sensors for Real-Time Data Collection
5.2.2 Geospatial Technologies for Spatial Data Analysis
5.2.3 Challenges and Considerations
5.3 Sensor Deployment in The Field
5.3.1 Best Practices for Sensor Deployment
5.3.2 Issues in Sensor Deployment
5.4 Data Collection and Aggregation
5.4.1 Data Collection from IoT Sensors
5.4.2 Data Collection from Geospatial Technologies
5.4.3 Data Aggregation
5.4.4 Data Processing and Analysis
5.4.5 Challenges in Data Collection and Aggregation
5.5 Crop Monitoring and Analytics
5.5.1 Monitoring of Environmental Conditions
5.5.2 Crop Health Monitoring
5.5.3 Remote Sensing for Crop Monitoring
5.5.4 Crop Monitoring through Data Analytics
5.5.5 Crop Monitoring and Analytics Innovations
5.6 The Future of Smart Farming
5.6.1 Smart Farming Innovations
5.6.2 Challenges to Overcome
5.7 Conclusion
References
6. Enhancing Irrigation Efficiency with GIS and Precision Mapping Techniques
Himanshu and Deepika Saxena
6.1 Introduction
6.2 Fundamentals of GIS and Precision Agriculture
6.2.1 GIS in Agriculture
6.3 Precision Mapping Techniques for Irrigation
6.3.1 Soil Moisture Mapping
6.3.2 Weather Forecasting
6.3.3 Farmer-Friendly Mobile App-Based Operation
6.3.4 Drone-Based Monitoring
6.3.5 Evapotranspiration Mapping
6.3.6 Zonal Mapping
6.3.7 Variable Water Application Rates
6.4 Challenges in Traditional Irrigation Systems
6.4.1 Water Wastage
6.4.2 Energy Consumption
6.4.3 Soil Degradation
6.5 Optimizing Irrigation Efficiency with GIS
6.5.1 Soil Moisture Mapping and Crop Water Need
6.5.2 Site-Specific Irrigation Scheduling
6.5.3 Zoning of Water Management
6.5.4 Real-Time Monitoring and Decision Support System
6.5.5 Water Flow Mapping and Drainage Planning
6.6 Remote Sensing in Precision Irrigation
6.6.1 Role of Remote Sensing in Detecting Water Stress
6.6.1.1 Vegetative Indices
6.6.1.2 Leaf Chlorophyll
6.6.1.3 Canopy Temperature
6.6.1.4 Plant Photosynthetic Efficiency
6.7 UAVs for High-Resolution Water-Use Mapping
6.7.1 Soil Moisture, Crop Water Demand, and Irrigation
6.7.2 Crop Water Status Monitoring
6.8 Challenges and Barriers to Adoption
6.8.1 High Initial Cost and Financial Barriers
6.8.2 Limited Access to Technology and Infrastructure
6.8.3 Technology Complexity and Lack of Skilled Labor
6.9 Future Trends and Innovation in Irrigation Efficiency
6.9.1 Robotic Irrigation System
6.9.2 Advanced AI and IoT-Supported Irrigation
6.9.3 Holographic Irrigation Systems
6.9.4 Irrigation with Sound Waves
6.9.5 3D GIS
6.9.6 Mobile GIS
6.9.7 Big Data and ML
6.10 Conclusion
References
7. Integrating SAR and Multispectral Data with HSV-Based Fusion for Accurate Land Cover Classification
Narayan Vyas
7.1 Introduction
7.2 Study Area and Satellite Dataset
7.2.1 Study Area
7.2.2 Satellite Dataset
7.3 Methodology
7.3.1 Preprocessing
7.3.2 Satellite Data Fusion
7.3.3 LULC Classification
7.3.4 Accuracy Assessment
7.4 Results
7.4.1 Quantitative Analysis of Classified Maps
7.4.2 Visual Analysis of Classified Maps
7.5 Discussion
7.6 Conclusion
References
8. Precision Agriculture through IoT and Geospatial Technologies for Sustainable Crop Management
Rupinder Singh, Jaswinder Singh and Amanpreet Singh
8.1 Introduction
8.1.1 Smart Agriculture: Transforming Traditional Practices
8.1.2 The Role of IoT in Agriculture
8.1.3 Geospatial Technologies in Crop Management
8.1.4 Advantages of Using IoT and Geospatial Technologies
8.2 Literature Review
8.3 The Role of IoT Sensors in Agriculture
8.3.1 Overview of IoT in Agriculture
8.3.2 Applications of IoT Sensors
8.3.3 Case Studies
8.4 Application of Geographical Information Systems (GIS) in Crop Production
8.4.1 Brief Introduction of Geospatial Technologies
8.4.2 Remote Sensing Applications
8.4.3 GIS
8.5 Integration of IoT and Geospatial Technologies
8.5.1 Data Analytics and Decision Support Systems
8.5.2 Useful Examples and Case/Field Implementation
8.6 Challenges
8.7 Conclusion
References
9. Systematic Review on Agricultural Land Use Classification Based on Machine Learning and Deep Learning Approaches Using Satellite Dataset
Nisha Sharma, Kawaljit Kaur and Sartajvir Singh
9.1 Introduction
9.1.1 Applications of LULC
9.2 Satellite Datasets for Agricultural LU Classification
9.3 Methodology
9.4 Related Work
9.4.1 ML-Based LULC Classification
9.4.2 DL-Based LULC Classification
9.5 Analysis and Discussion
9.6 Conclusion
References
10. Remote Sensing and GIS-Based Approaches to LULC Analysis for Sustainable Agricultural Planning
Garima, Ankita Kumari and Soubhagya Ranjan Dhall
10.1 Introduction
10.2 Technological Advancements in LULC Mapping
10.2.1 ML and AI
10.2.2 Cloud Computing Platforms
10.2.2.1 A Study of the Sonipat District, Haryana
10.2.2.2 Mekong Delta in Cambodia
10.3 Applications in Sustainable Agriculture
10.3.1 Climate Change Adaptation
10.3.2 Soil Health and Carbon Sequestration
10.4 Case Studies on LULC Mapping for Sustainable Agriculture
10.5 Future Directions
10.5.1 Conclusion
References
11. Image Classification Techniques for Precision Agriculture and Integrated Pest Management
Neetu Agrawal, Mehul Mahrishi and Mukesh Kumar Gupta
11.1 Introduction
11.2 Pest Image Acquisition
11.2.1 Use of Existing Pest Dataset
11.2.2 Images Taken from Search Engines
11.2.3 Laboratory-Based Images
11.2.4 On-Field Image Acquisition
11.3 Image Pre-Processing
11.4 AI-Driven Pest Identification
11.4.1 Machine Learning Models for Pest Image Classification
11.4.1.1 Support Vector Machine
11.4.1.2 Artificial Neural Network
11.4.2 Deep Learning Models
11.4.2.1 InceptionV3
11.4.2.2 ResNet
11.4.2.3 DenseNet
11.4.3 Few-Shot Learning
11.4.4 Conclusion
References
12. Spatio-Temporal Analysis of Tropospheric NO2 Over Haryana Using Sentinel-5P and Google Earth Engine
Narayan Vyas, Neha Bhati, Rajesh Yadav and Abdullah Alzahrani
12.1 Introduction
12.2 Study Area
12.3 Dataset and Tools
12.4 Methodology
12.4.1 Region of Interest Definition
12.4.2 Temporal Scope and Parameter Selection
12.4.3 Feature Generation and Unit Conversion
12.4.4 Visualization and Export
12.5 Results
12.5.1 Visual Analysis
12.5.2 Statistical Analysis
12.6 Discussion
12.6.1 Conclusion
References
13. Agriculture Crop Pest Monitoring and Management Using LiDAR Technology
Satyabrata Sarangi, Tarini Prasad Das, Ekamra Kanan Padhan, Jyoti Prakash Das and Harmohan Pradhan
13.1 Introduction
13.2 Principles of Lidar Technology in Pest Monitoring and Management
13.2.1 LiDAR Technology Operating Principle in Smart Farming
13.2.2 LiDAR Technology Used for Insect–Pest Monitoring and Management
13.3 Integration of Lidar with Other Digital Technologies for Precision Pest Monitoring and Control
13.4 Successful Case Studies of Pest Identification and Forecasting Using Lidar System
13.5 Challenges and Future Studies
13.6 Conclusion
References
14. Role of Google Earth Engine (GEE) in Natural Hazard Monitoring and Management
Vijaya Ilango and Bhavadev Gajendiran
14.1 Introduction
14.2 Tools and Techniques for Natural Hazard Assessment
14.2.1 Geographical Information Systems Technology Method
14.2.2 Remote Sensing
14.2.3 Satellite Imaging
14.2.4 Google Earth Engine
14.3 Role of GEE in Disaster Monitoring and Prediction
14.3.1 Vegetation and Wildfires
14.3.2 Coastal Mapping and Water Level
14.3.3 Drought
14.3.4 Flood
14.3.5 Earthquake and Consequent Landslides and Flow Slides
14.4 Conclusion
References
15. Innovative Business Models for Geospatial-Enabled Agricultural Solutions
Amit Sharma, Shubhi Sharma, Ruchi Sharma and Harshita Doad
15.1 Introduction
15.2 Understanding the Geospatial-Agriculture Nexus
15.3 The Shift in Business Thinking: From Inputs to Intelligence
15.4 Business Model Typologies in Geospatial AgTech
15.5 Innovation through Collaboration and Open Models
15.6 Real-World Case Studies
15.7 Challenges in Scaling Geospatial Business Models
15.8 Designing a Scalable, Inclusive Future
15.9 Conclusion
References
16. Economic Impacts of Geospatial Intelligence on Sustainable Farming
Gurwinder Singh, Aaskaran Bishnoi and Ranjit Singh
16.1 Introduction
16.2 Background and Context
16.3 Role of GI in Addressing Economic Challenges
16.4 Economic Benefits of GI
16.4.1 Cost Reduction
16.4.2 Increased Productivity and Yield
16.5 Risk Mitigation
16.5.1 Predictive Analytics
16.5.2 Sustainability and Long-Term Economic Gains
16.6 Market Dynamics and Economic Opportunities
16.6.1 Access to New Markets
16.6.2 Supply Chain Efficiency
16.6.3 Policy and Incentive Alignment
16.7 Challenges and Economic Barriers
16.7.1 Initial Investment Costs
16.7.2 Training and Skill Development
16.7.3 Scalability Issues
16.8 Case Studies or Practical Applications
16.8.1 Case Study 1: Small-Scale Farm in India
16.8.2 Case Study 2: Large-Scale Farm in the USA
16.9 Future Economic Implications
16.9.1 Emerging Trends
16.9.2 Scaling Economic Benefits
16.9.3 Policy Recommendations
16.9.4 Conclusion
References
17. Future Directions and Challenges for Geospatial Technologies: Shaping India’s Policy Landscape for Sustainable Renewable Energy
Deepali Singh, Harshita Sharma, R. B. Singh, Seema Ahmad and Ronak Duggar
17.1 Introduction
17.2 The Policy Framework for Renewable Energy in India
17.3 Obstacles in the Implementation of Policies
17.4 Impact of Policy and Future Directions
17.5 Solar Energy Vision
17.6 Hydropower Position
17.7 National Renewable Energy Policy Measures
17.8 The Influence of Partnership to Advance Clean Energy
17.9 Policy Interconnections and Energy Security
17.10 India’s Renewable Energy
17.11 Conclusion
References
18. Future Directions and Challenges for Geospatial Intelligence-Based Precision Agriculture
Amit Sharma, Shubhi Sharma, Ruchi Sharma and Balasani Raghupathi
18.1 Introduction
18.2 Current State of GEOINT in Precision Agriculture
18.2.1 Satellite Imagery: Sentinel, Landsat, and MODIS
18.2.2 UAV and Drone-Based Monitoring Systems
18.2.3 Field Sensors and GPS Technologies
18.2.4 Case Studies Highlighting Current Implementations
18.2.4.1 Case Study 1: India—Rice Crop Monitoring with Sentinel-1 and GEE
18.2.4.2 Case Study 2: USA—Corn and Soybean Monitoring with UAVs
18.2.4.3 Case Study 3: Africa—IoT-Based Sensor Networks for Smallholder Farmers
18.3 Emerging Technologies and Trends
18.3.1 AI and ML for Geospatial Data Interpretation
18.3.2 Cloud Computing Platforms: GEE, AWS, Microsoft Planetary Computer
18.3.3 Multisource and Multitemporal Data Fusion
18.3.4 Blockchain and Edge Computing for Secure, Real-Time Intelligence
18.4 Challenges in GEOINT-Driven Precision Agriculture
18.4.1 Technical Challenges
18.4.1.1 Spatial and Spectral Resolution Trade-Offs
18.4.1.2 Cloud Cover, Atmospheric Noise, and Time-Series Inconsistencies
18.4.1.3 Interoperability between Sensors, Platforms, and Algorithms
18.4.2 Economic and Infrastructural Challenges
18.4.2.1 High Cost of Technology Adoption
18.4.2.2 Lack of Digital Literacy and Rural Connectivity
18.4.3 Policy and Ethical Challenges
18.4.3.1 Data Privacy, Ownership, and Consent
18.4.3.2 Lack of Open-Access Standards and Global Cooperation
18.5 Comparative Review of Global Research and Gaps
18.5.1 Developed vs. Developing Country Perspectives
18.5.2 Gaps in Accessibility, Scalability, and Localized Model Training
18.5.3 Role of Indigenous Knowledge and Community Participation
18.6 Future Research Directions
18.6.1 Real-Time Analytics Integrated with AI-Based Prediction
18.6.2 Generative AI and Large Language Models for Agricultural Advisories
18.6.3 Multi-Modal Sensor Integration: Thermal, Microwave, Spectral, and Soil Sensors
18.7 Conclusion
References
Index

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