Vishakha Sood – författare

Hyperautomation in Precision Agriculture: Advancements and Opportunities for Sustainable Farming is the first book to focus on the integration of multiple techniques and technologies to create an ecosystem sustaining approach that doesn’t compromise soil health or environmental safety as it increases crop yield. The book highlights the integration of state-of-the-art tools and working models to address the various challenges in the field of agriculture. It also identifies and discusses the potential and challenges of hyperautomation in sustainable agriculture with respect to efficiency improvement and human enhancement of automated operations.Hyperautomation is a true digital transformation in sustainable agriculture utilizing advanced techniques such as robotic process automation (RPA), digital process automation (DPA), unmanned aerial vehicle (UAV), controlled-environment agriculture (CEA), remote sensing, internet of things (IoT), crop modeling, precision farming, sustainable yield, image analysis, data fusion, artificial intelligence (AI), machine learning (ML), and deep learning (DL).Provides a comprehensive overview of the current state-of-the-art of automation in agricultureEnables improved productivity and resource optimizationPresents advanced monitoring/mapping methods in soil properties, nutrients, crop growth, and yield

Hyperautomation in Precision Agriculture: Advancements and Opportunities for Sustainable Farming is the first book to focus on the integration of multiple techniques and technologies to create an ecosystem sustaining approach that doesn''t compromise soil health or environmental safety as it increases crop yield. The book highlights the integration of state-of-the-art tools and working models to address the various challenges in the field of agriculture. It also identifies and discusses the potential and challenges of hyperautomation in sustainable agriculture with respect to efficiency improvement and human enhancement of automated operations.Hyperautomation is a true digital transformation in sustainable agriculture utilizing advanced techniques such as robotic process automation (RPA), digital process automation (DPA), unmanned aerial vehicle (UAV), controlled-environment agriculture (CEA), remote sensing, internet of things (IoT), crop modeling, precision farming, sustainable yield, image analysis, data fusion, artificial intelligence (AI), machine learning (ML), and deep learning (DL).- Provides a comprehensive overview of the current state-of-the-art of automation in agriculture- Enables improved productivity and resource optimization- Presents advanced monitoring/mapping methods in soil properties, nutrients, crop growth, and yield

Google Earth Engine and Artificial Intelligence for Earth Observation: Algorithms and Sustainable Applications explores a wide range of transformative data fusion techniques of Artificial Intelligence (AI) technologies applied to Google Earth Engine (GEE) techniques. It includes a wide range of scientific domains that can utilize remote sensing and geographic information systems (GIS) through detailed case studies. This book delves into the challenges of AI-driven tools and technologies for Earth observation data analysis, offering possible solutions and directly addressing current and upcoming needs within Earth observation. Google Earth Engine and Artificial Intelligence for Earth Observation: Algorithms and Sustainable Applications is a useful reference for geospatial scientists, remote sensing experts, and environmental scientists utilizing remote sensing to apply the latest AI techniques to data obtained from GEE for their research and teaching.Includes utilization of AI with GEE tools for a spectrum of scientific domains in remote sensing and geographic information systems (GIS) including natural hazard assessment, aquatic and hydrological applications, and forest coverHighlights the challenges and possible solutions for AI-driven tools and technologies for Earth observation data analysisIncludes detailed case studies showing specific considerations and exceptions for applications of AI in GEE for Earth observation

Google Earth Engine and Artificial Intelligence for Earth Observation: Algorithms and Sustainable Applications explores a wide range of transformative data fusion techniques of Artificial Intelligence (AI) technologies applied to Google Earth Engine (GEE) techniques. It includes a wide range of scientific domains that can utilize remote sensing and geographic information systems (GIS) through detailed case studies. This book delves into the challenges of AI-driven tools and technologies for Earth observation data analysis, offering possible solutions and directly addressing current and upcoming needs within Earth observation. Google Earth Engine and Artificial Intelligence for Earth Observation: Algorithms and Sustainable Applications is a useful reference for geospatial scientists, remote sensing experts, and environmental scientists utilizing remote sensing to apply the latest AI techniques to data obtained from GEE for their research and teaching.- Includes utilization of AI with GEE tools for a spectrum of scientific domains in remote sensing and geographic information systems (GIS) including natural hazard assessment, aquatic and hydrological applications, and forest cover- Highlights the challenges and possible solutions for AI-driven tools and technologies for Earth observation data analysis- Includes detailed case studies showing specific considerations and exceptions for applications of AI in GEE for Earth observation

Earth Observation using Scatterometers: State-of-the-Art Techniques, Applications, and Challenges explores the critical role scatterometers play in addressing natural disasters, climate action, and food security. It provides comprehensive guidance on leveraging scatterometers for real-time applications, including advanced techniques like super-resolution mapping and multi-source data fusion. This book is an essential resource for understanding the challenges and opportunities of scatterometer satellite datasets. In addition to covering the latest advancements in algorithms and emerging applications, it empowers professionals to efficiently analyze vast amounts of Earth observation data.The book addresses scatterometers' expanding applicability in oceanography, agriculture, the cryosphere, and related Earth-science fields, making it invaluable for scientists, geospatial data analysts, and students of Remote Sensing and Geoscience.Provides detailed instruction on the applications of scatterometers to a wide range of Earth sciences, including benefits and case studies showing real world useCovers the use of scatterometers in analyzing and responding to natural disasters, including analysis of sea-ice melt, cyclone predictions, etc.Includes techniques for monitoring the cryosphere and global climate, with dedicated methodology and data analysis

Earth Observation using Scatterometers: State-of-the-Art Techniques, Applications, and Challenges explores the critical role scatterometers play in addressing natural disasters, climate action, and food security. It provides comprehensive guidance on leveraging scatterometers for real-time applications, including advanced techniques like super-resolution mapping and multi-source data fusion. This book is an essential resource for understanding the challenges and opportunities of scatterometer satellite datasets. In addition to covering the latest advancements in algorithms and emerging applications, it empowers professionals to efficiently analyze vast amounts of Earth observation data.The book addresses scatterometers' expanding applicability in oceanography, agriculture, the cryosphere, and related Earth-science fields, making it invaluable for scientists, geospatial data analysts, and students of Remote Sensing and Geoscience. - Provides detailed instruction on the applications of scatterometers to a wide range of Earth sciences, including benefits and case studies showing real world use- Covers the use of scatterometers in analyzing and responding to natural disasters, including analysis of sea-ice melt, cyclone predictions, etc. - Includes techniques for monitoring the cryosphere and global climate, with dedicated methodology and data analysis

This book explores the cutting-edge integration of generative artifical intelligence (AI) techniques to enhance environmental remote sensing, providing a comprehensive guide from foundational algorithms to practical applications. It explains how advanced AI technology can be used to improve the way we monitor and understand the environment from a distance, such as through satellites or drones. It starts with an explanation of basic algorithms behind generative AI and gradually moves to complex algorithms, showing how they can be applied to real-world environmental issues, such as tracking climate change, monitoring deforestation, and predicting natural disasters. Features Includes real-world examples and case studies showing how generative AI improves environmental monitoring. Provides step-by-step explanations of algorithms and their implementation. Explains complex concepts in simple and easy-to-understand language and introduces strategies to address environmental challenges using AI-driven solutions. Offers cutting-edge research and advancements in AI and remote sensing, including the application of generative AI models like GANs and VAEs. Applies to diverse fields such as urban planning, agriculture, and disaster management. Discusses ethical considerations and challenges when integrating AI with remote sensing. This book is for researchers and practitioners in environmental monitoring, urban planning, agriculture, and disaster management using remote sensing technologies and AI to address environmental challenges and sustainability. It is also intended for university professors, graduate, and postgraduate students in environmental science, geospatial analysis, computer science, and data science working on projects related to AI and remote sensing.

This book explores the cutting-edge integration of generative artifical intelligence (AI) techniques to enhance environmental remote sensing, providing a comprehensive guide from foundational algorithms to practical applications. It explains how advanced AI technology can be used to improve the way we monitor and understand the environment from a distance, such as through satellites or drones. It starts with an explanation of basic algorithms behind generative AI and gradually moves to complex algorithms, showing how they can be applied to real-world environmental issues, such as tracking climate change, monitoring deforestation, and predicting natural disasters. Features Includes real-world examples and case studies showing how generative AI improves environmental monitoring. Provides step-by-step explanations of algorithms and their implementation. Explains complex concepts in simple and easy-to-understand language and introduces strategies to address environmental challenges using AI-driven solutions. Offers cutting-edge research and advancements in AI and remote sensing, including the application of generative AI models like GANs and VAEs. Applies to diverse fields such as urban planning, agriculture, and disaster management. Discusses ethical considerations and challenges when integrating AI with remote sensing. This book is for researchers and practitioners in environmental monitoring, urban planning, agriculture, and disaster management using remote sensing technologies and AI to address environmental challenges and sustainability. It is also intended for university professors, graduate, and postgraduate students in environmental science, geospatial analysis, computer science, and data science working on projects related to AI and remote sensing.

This book explores the cutting-edge integration of generative artifical intelligence (AI) techniques to enhance environmental remote sensing, providing a comprehensive guide from foundational algorithms to practical applications. It explains how advanced AI technology can be used to improve the way we monitor and understand the environment from a distance, such as through satellites or drones. It starts with an explanation of basic algorithms behind generative AI and gradually moves to complex algorithms, showing how they can be applied to real-world environmental issues, such as tracking climate change, monitoring deforestation, and predicting natural disasters. FeaturesIncludes real-world examples and case studies showing how generative AI improves environmental monitoring.Provides step-by-step explanations of algorithms and their implementation.Explains complex concepts in simple and easy-to-understand language and introduces strategies to address environmental challenges using AI-driven solutions.Offers cutting-edge research and advancements in AI and remote sensing, including the application of generative AI models like GANs and VAEs.Applies to diverse fields such as urban planning, agriculture, and disaster management.Discusses ethical considerations and challenges when integrating AI with remote sensing.This book is for researchers and practitioners in environmental monitoring, urban planning, agriculture, and disaster management using remote sensing technologies and AI to address environmental challenges and sustainability. It is also intended for university professors, graduate, and postgraduate students in environmental science, geospatial analysis, computer science, and data science working on projects related to AI and remote sensing.

To secure the future of global food systems in an era of climate disruption, this comprehensive, hands-on reference equips you with the cutting-edge geospatial intelligence and AI tools needed to build highly profitable, sustainable, and climate-resilient farms. 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 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.

To secure the future of global food systems in an era of climate disruption, this comprehensive, hands-on reference equips you with the cutting-edge geospatial intelligence and AI tools needed to build highly profitable, sustainable, and climate-resilient farms. 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 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.