Geospatial Intelligence in Precision Agriculture

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 ADA-DRDO and Robert Bosch. His research focuses on IoT and signal processing, with more than 100 international publications and six granted patents.

• Preface xix1 Introduction to Geospatial Intelligence and Precision Agriculture 1Neelam Dahiya, Amanpreet Singh and Sartajvir Singh1.1 Introduction 21.2 Applications of GI in Precision Agriculture 51.3 Current Trends in the Adoption of GI in Agriculture 81.4 Challenges in Adopting GI in Agriculture 101.5 Opportunities for Enhancing GI Adoption in Agriculture 121.6 Conclusion 142 The Role of Geospatial Intelligence in Agricultural Innovations 19Umesh Kumar Singh, Santosh Korav and Sujatha H. T.2.1 Introduction to Geospatial Intelligence 202.2 Current Challenges in Agriculture 202.3 Application of Geospatial Intelligence in Agriculture 222.4 Technological Progress in Geospatial Intelligence Applications in Agriculture 252.5 Case Studies and Practical Applications 302.6 Advantages of Geospatial Intelligence in Agriculture 312.7 Challenges and Limitations of Geospatial Intelligence in Agriculture 342.8 Future Trends and Opportunities 363 Data Acquisition and Sensing Technologies in Geospatial Intelligence 43Vishvajeet D. Jadhav, Santosh Korav, Sandeep Menon and Prakash Kumar Jha3.1 Introduction 443.2 Data Acquisition Techniques 463.3 GEOINT and Sensing Technologies 493.4 Aerial Photography—A Vital Part of GEOINT 523.5 Remote Sensing Resolution 543.6 Applications of Big Data in Agriculture 553.7 Cloud Computing in Agriculture 563.8 Challenges and Future Directions 564 Integrating IoT and Geospatial Technologies for Real-Time Crop and Soil Monitoring in Precision Agriculture 63Aritra Guin, Santosh Korav and Sujatha H.T.4.1 Introduction 644.2 Overview of IoT Sensors in Agriculture 654.3 Geospatial Technologies in Agriculture 674.4 Integration of IoT and Geospatial Technologies 704.5 Benefits of Real-Time Crop Management 734.6 Challenges and Limitations 744.7 Future Trends 764.8 Conclusion 765 Real-Time Crop Management with IoT Sensors and Geospatial Technologies 83R. Kanthavel, R. Adline Freeda and R. Dhaya5.1 Introduction 845.2 Real-Time Crop Management with IoT Sensors and Geospatial Technologies 855.3 Sensor Deployment in The Field 895.4 Data Collection and Aggregation 925.5 Crop Monitoring and Analytics 975.6 The Future of Smart Farming 103Contents ix5.7 Conclusion 1056 Enhancing Irrigation Efficiency with GIS and Precision Mapping Techniques 109Himanshu and Deepika Saxena6.1 Introduction 1106.2 Fundamentals of GIS and Precision Agriculture 1116.3 Precision Mapping Techniques for Irrigation 1136.4 Challenges in Traditional Irrigation Systems 1166.5 Optimizing Irrigation Efficiency with GIS 1176.6 Remote Sensing in Precision Irrigation 1196.7 UAVs for High-Resolution Water-Use Mapping 1236.8 Challenges and Barriers to Adoption 1276.9 Future Trends and Innovation in Irrigation Efficiency 1296.10 Conclusion 1327 Integrating SAR and Multispectral Data with HSV-Based Fusion for Accurate Land Cover Classification 141Narayan Vyas7.1 Introduction 1427.2 Study Area and Satellite Dataset 1457.3 Methodology 1477.4 Results 1517.5 Discussion 1537.6 Conclusion 1548 Precision Agriculture through IoT and Geospatial Technologies for Sustainable Crop Management 159Rupinder Singh, Jaswinder Singh and Amanpreet Singh8.1 Introduction 1608.2 Literature Review 1638.3 The Role of IoT Sensors in Agriculture 1638.4 Application of Geographical Information Systems (GIS) in Crop Production 1698.5 Integration of IoT and Geospatial Technologies 1718.6 Challenges 1728.7 Conclusion 1739 Systematic Review on Agricultural Land Use Classification Based on Machine Learning and Deep Learning Approaches Using Satellite Dataset 177Nisha Sharma, Kawaljit Kaur and Sartajvir Singh9.1 Introduction 1789.2 Satellite Datasets for Agricultural LU Classification 1819.3 Methodology 1819.4 Related Work 1839.5 Analysis and Discussion 1899.6 Conclusion 19010 Remote Sensing and GIS-Based Approaches to LULC Analysis for Sustainable Agricultural Planning 197Garima, Ankita Kumari and Soubhagya Ranjan Dhall10.1 Introduction 19810.2 Technological Advancements in LULC Mapping 20010.3 Applications in Sustainable Agriculture 20310.4 Case Studies on LULC Mapping for Sustainable Agriculture 20410.5 Future Directions 20811 Image Classification Techniques for Precision Agriculture and Integrated Pest Management 213Neetu Agrawal, Mehul Mahrishi and Mukesh Kumar Gupta11.1 Introduction 21411.2 Pest Image Acquisition 21711.3 Image Pre-Processing 21811.4 AI-Driven Pest Identification 22012 Spatio-Temporal Analysis of Tropospheric NO2 Over Haryana Using Sentinel-5P and Google Earth Engine 233Narayan Vyas, Neha Bhati, Rajesh Yadav and Abdullah Alzahrani12.1 Introduction 23412.2 Study Area 23712.3 Dataset and Tools 23812.4 Methodology 23912.5 Results 24212.6 Discussion 24513 Agriculture Crop Pest Monitoring and Management Using LiDAR Technology 251Satyabrata Sarangi, Tarini Prasad Das, Ekamra Kanan Padhan, Jyoti Prakash Das and Harmohan Pradhan13.1 Introduction 25213.2 Principles of Lidar Technology in Pest Monitoring and Management 25413.3 Integration of Lidar with Other Digital Technologies for Precision Pest Monitoring and Control 25713.4 Successful Case Studies of Pest Identification and Forecasting Using Lidar System 25813.5 Challenges and Future Studies 26513.6 Conclusion 26714 Role of Google Earth Engine (GEE) in Natural Hazard Monitoring and Management 275Vijaya Ilango and Bhavadev Gajendiran14.1 Introduction 27614.2 Tools and Techniques for Natural Hazard Assessment 27814.3 Role of GEE in Disaster Monitoring and Prediction 28214.4 Conclusion 28615 Innovative Business Models for Geospatial-Enabled Agricultural Solutions 295Amit Sharma, Shubhi Sharma, Ruchi Sharma and Harshita Doad15.1 Introduction 29615.2 Understanding the Geospatial-Agriculture Nexus 29715.3 The Shift in Business Thinking: From Inputs to Intelligence 29815.4 Business Model Typologies in Geospatial AgTech 29915.5 Innovation through Collaboration and Open Models 30115.6 Real-World Case Studies 30215.7 Challenges in Scaling Geospatial Business Models 30315.8 Designing a Scalable, Inclusive Future 30415.9 Conclusion 30616 Economic Impacts of Geospatial Intelligence on Sustainable Farming 313Gurwinder Singh, Aaskaran Bishnoi and Ranjit Singh16.1 Introduction 31416.2 Background and Context 31616.3 Role of GI in Addressing Economic Challenges 31716.4 Economic Benefits of GI 31916.5 Risk Mitigation 32116.6 Market Dynamics and Economic Opportunities 32416.7 Challenges and Economic Barriers 32816.8 Case Studies or Practical Applications 33116.9 Future Economic Implications 33417 Future Directions and Challenges for Geospatial Technologies: Shaping India's Policy Landscape for Sustainable Renewable Energy 339Deepali Singh, Harshita Sharma, R. B. Singh, Seema Ahmad and Ronak Duggar17.1 Introduction 34017.2 The Policy Framework for Renewable Energy in India 34217.3 Obstacles in the Implementation of Policies 34217.4 Impact of Policy and Future Directions 34317.5 Solar Energy Vision 34417.6 Hydropower Position 34417.7 National Renewable Energy Policy Measures 34517.8 The Influence of Partnership to Advance Clean Energy 34517.9 Policy Interconnections and Energy Security 34517.10 India's Renewable Energy 34717.11 Conclusion 34818 Future Directions and Challenges for Geospatial Intelligence-Based Precision Agriculture 353Amit Sharma, Shubhi Sharma, Ruchi Sharma and Balasani Raghupathi18.1 Introduction 35418.2 Current State of GEOINT in Precision Agriculture 35518.3 Emerging Technologies and Trends 35918.4 Challenges in GEOINT-Driven Precision Agriculture 36118.5 Comparative Review of Global Research and Gaps 36518.6 Future Research Directions 36918.7 Conclusion 371References 371Index 377