Uyiosa Osagie Aigbe – författare

Digital Twins, Artificial Intelligence, and Internet of Things for a Sustainable Food Production in Agriculture: Transformative Technologies looks at how advanced technologies like digital twins, artificial intelligence (AI), and the Internet of Things (IoT) can be used in farming.

The book discusses how these innovations enhance resource efficiency, support sustainable food systems, and improve crop and livestock management. Focusing on both the theoretical and practical applications, it provides readers with current insight into sustainable agricultural practices. It also offers insights into how digital twins, AI, and IoT are integrated into agriculture for sustainable food production. The book will cover how digital copies of farms, smart computers, AI, IoT, and connected devices help farmers optimize food production. It draws its strength from a multidisciplinary approach with an emphasis on practical applications and future prospects in smart agriculture toward improved efficiency, sustainability, and profitability in food production.

This book is suitable for a broad academic audience, including undergraduate and graduate students, researchers, and professionals in fields such as agricultural science, environmental science, computer science, and engineering, as well as policymakers and government functionaries. It is meant to be used as a textbook or additional reading for courses on precision farming, smart agriculture, and sustainable food systems. The content is designed to be understandable to people who have just a cursory understanding of technology and agriculture, but it also provides in-depth analysis for practitioners and advanced students.

Digital Twins, Artificial Intelligence, and Internet of Things for a Sustainable Food Production in Agriculture: Transformative Technologies looks at how advanced technologies like digital twins, artificial intelligence (AI), and the Internet of Things (IoT) can be used in farming.

The book discusses how these innovations enhance resource efficiency, support sustainable food systems, and improve crop and livestock management. Focusing on both the theoretical and practical applications, it provides readers with current insight into sustainable agricultural practices. It also offers insights into how digital twins, AI, and IoT are integrated into agriculture for sustainable food production. The book will cover how digital copies of farms, smart computers, AI, IoT, and connected devices help farmers optimize food production. It draws its strength from a multidisciplinary approach with an emphasis on practical applications and future prospects in smart agriculture toward improved efficiency, sustainability, and profitability in food production.

This book is suitable for a broad academic audience, including undergraduate and graduate students, researchers, and professionals in fields such as agricultural science, environmental science, computer science, and engineering, as well as policymakers and government functionaries. It is meant to be used as a textbook or additional reading for courses on precision farming, smart agriculture, and sustainable food systems. The content is designed to be understandable to people who have just a cursory understanding of technology and agriculture, but it also provides in-depth analysis for practitioners and advanced students.

Harnessing Machine Learning for Environmental Restoration and Surveillance is an extensive and comprehensive book, showcasing the applications of machine learning and artificial intelligence in environmental monitoring of the quality of air, soil, and water; prediction of environmental risks; optimization and prediction of remediation processes; and enhancement of resource management.

The book offers readers an opportunity to understand how contemporary smart technology (machine learning and artificial intelligence) can be used to safeguard and improve our environment as well as to solve environmental problems and provide sustainable solutions. It offers insights and an opportunity to evaluate theoretical concepts and real-world case studies, thereby boosting innovation and collaboration. The authors give modest explanations of how artificial intelligence and machine learning can monitor pollution, forecast environmental hazards, and support the optimization process for the effective removal of environmental contaminants. Real-world examples where these advanced smart technology tools have been successfully utilized are also presented in this book.

The content and coverage of this academic text are ideal for undergraduate and postgraduate students, researchers, professionals in environmental science, data science, and engineering, as well as individuals interested in the environment and technology.

Harnessing Machine Learning for Environmental Restoration and Surveillance is an extensive and comprehensive book, showcasing the applications of machine learning and artificial intelligence in environmental monitoring of the quality of air, soil, and water; prediction of environmental risks; optimization and prediction of remediation processes; and enhancement of resource management.

The book offers readers an opportunity to understand how contemporary smart technology (machine learning and artificial intelligence) can be used to safeguard and improve our environment as well as to solve environmental problems and provide sustainable solutions. It offers insights and an opportunity to evaluate theoretical concepts and real-world case studies, thereby boosting innovation and collaboration. The authors give modest explanations of how artificial intelligence and machine learning can monitor pollution, forecast environmental hazards, and support the optimization process for the effective removal of environmental contaminants. Real-world examples where these advanced smart technology tools have been successfully utilized are also presented in this book.

The content and coverage of this academic text are ideal for undergraduate and postgraduate students, researchers, professionals in environmental science, data science, and engineering, as well as individuals interested in the environment and technology.

This book explores some of the latest and recent advances in the synthesis, characterization and applications of magnetic nanomaterials. It starts with an overview of magnetic nanomaterials, followed by a list of their synthesis and characterization methods. The book shows the potential of magnetic materials in different applications, including theranostic nanomedicine, heavy metals detection, dyes sensing, solar cells, wastewater treatment,  decontamination of soil, and detection and monitoring of toxic gases. Moreover, it explores their use as drug and gene delivery agents, their biosafety and bioregulation facets, tissue engineering applications, and their potential for combating pathogens