Revolutionizing precision agriculture: the synergy of RADAR, Internet of things (IoT), and satellite technology
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Ashutosh Pagrotra
Abstract
This chapter explores the opportunity to use radio detection and ranging (RADAR) technology in combination with the Internet of things (IoT) and satellite images to improve the efficiency of precision agriculture. It begins by explaining RADAR systemsRADAR systems’ historical background and importance in monitoring, especially farming, where they can collect data even in cloudy weather conditions. The chapter also examines how RADAR and IoT are intertwined. IoT provides real-time data on soil moisture, weather conditions, and crop status, which, when combined with RADAR’s large-scale monitoring capabilities, offers a holistic picture of agricultural conditions. High-resolution satellite imagery benefits crop monitoring by capturing more spatial detail, enabling precise assessments of vegetation health and soil quality. These integrated technologies are illustrated through several case studies and examples that demonstrate their implementation and the positive outcomes in areas such as irrigation success rates, crop yield prediction, and early diagnosis of crop diseases, thereby conserving resources and improving yields. The chapter also addresses challenges such as data integration and high-resolution monitoring.
Abstract
This chapter explores the opportunity to use radio detection and ranging (RADAR) technology in combination with the Internet of things (IoT) and satellite images to improve the efficiency of precision agriculture. It begins by explaining RADAR systemsRADAR systems’ historical background and importance in monitoring, especially farming, where they can collect data even in cloudy weather conditions. The chapter also examines how RADAR and IoT are intertwined. IoT provides real-time data on soil moisture, weather conditions, and crop status, which, when combined with RADAR’s large-scale monitoring capabilities, offers a holistic picture of agricultural conditions. High-resolution satellite imagery benefits crop monitoring by capturing more spatial detail, enabling precise assessments of vegetation health and soil quality. These integrated technologies are illustrated through several case studies and examples that demonstrate their implementation and the positive outcomes in areas such as irrigation success rates, crop yield prediction, and early diagnosis of crop diseases, thereby conserving resources and improving yields. The chapter also addresses challenges such as data integration and high-resolution monitoring.
Chapters in this book
- Frontmatter I
- Preface V
- Contents VII
- Integrating Sentinel-1 satellite data with machine learning for land use classification 1
- A systematic review of deep learning techniques in microwave remote sensing: challenges, applications, and future directions 17
- Fundamentals of active and passive microwave remote sensing: principles and applications 31
- Comprehensive overview of active and passive microwave remote sensing satellite sensors 55
- Essentials of RADAR remote sensing and AI integration 73
- Fusion of scatterometer and optical remote sensing: enhanced classification and change detection 91
- AI-powered urban infrastructure monitoring using RADAR-based remote sensing 103
- Fusion of the optical and microwave images for cloud removal 123
- Integrating AI in RADAR remote sensing: enhancing data processing, interpretation, and decision-making 141
- Revolutionizing precision agriculture: the synergy of RADAR, Internet of things (IoT), and satellite technology 155
- Integrating AI with RADAR remote sensing: applications in disaster mitigation, defense, and climate change 171
- Computational techniques in RADAR remote sensing from a machine and deep learning perspective 189
- Deep learning-based water body segmentation in SAR imagery: enhancing accuracy with CNN-U-Net and EfficientNet 205
- Artificial intelligence in RADAR remote sensing: advances, challenges, and future prospects 215
- Revolutionizing agricultural and environmental analytics with synthetic aperture radar (SAR): innovations, challenges, and future directions 229
- Editors’ biographies
- Index 247
Chapters in this book
- Frontmatter I
- Preface V
- Contents VII
- Integrating Sentinel-1 satellite data with machine learning for land use classification 1
- A systematic review of deep learning techniques in microwave remote sensing: challenges, applications, and future directions 17
- Fundamentals of active and passive microwave remote sensing: principles and applications 31
- Comprehensive overview of active and passive microwave remote sensing satellite sensors 55
- Essentials of RADAR remote sensing and AI integration 73
- Fusion of scatterometer and optical remote sensing: enhanced classification and change detection 91
- AI-powered urban infrastructure monitoring using RADAR-based remote sensing 103
- Fusion of the optical and microwave images for cloud removal 123
- Integrating AI in RADAR remote sensing: enhancing data processing, interpretation, and decision-making 141
- Revolutionizing precision agriculture: the synergy of RADAR, Internet of things (IoT), and satellite technology 155
- Integrating AI with RADAR remote sensing: applications in disaster mitigation, defense, and climate change 171
- Computational techniques in RADAR remote sensing from a machine and deep learning perspective 189
- Deep learning-based water body segmentation in SAR imagery: enhancing accuracy with CNN-U-Net and EfficientNet 205
- Artificial intelligence in RADAR remote sensing: advances, challenges, and future prospects 215
- Revolutionizing agricultural and environmental analytics with synthetic aperture radar (SAR): innovations, challenges, and future directions 229
- Editors’ biographies
- Index 247
