Deep Learning for Sustainable Agriculture
Deep Learning for Sustainable Agriculture

Author: Ramesh Chandra Poonia

Publisher: Academic Press

Published: 2022-01-09

Total Pages: 408

ISBN-13: 0323903622

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The evolution of deep learning models, combined with with advances in the Internet of Things and sensor technology, has gained more importance for weather forecasting, plant disease detection, underground water detection, soil quality, crop condition monitoring, and many other issues in the field of agriculture. agriculture. Deep Learning for Sustainable Agriculture discusses topics such as the impactful role of deep learning during the analysis of sustainable agriculture data and how deep learning can help farmers make better decisions. It also considers the latest deep learning techniques for effective agriculture data management, as well as the standards established by international organizations in related fields. The book provides advanced students and professionals in agricultural science and engineering, geography, and geospatial technology science with an in-depth explanation of the relationship between agricultural inference and the decision-support amenities offered by an advanced mathematical evolutionary algorithm. - Introduces new deep learning models developed to address sustainable solutions for issues related to agriculture - Provides reviews on the latest intelligent technologies and algorithms related to the state-of-the-art methodologies of monitoring and mitigation of sustainable agriculture - Illustrates through case studies how deep learning has been used to address a variety of agricultural diseases that are currently on the cutting edge - Delivers an accessible explanation of artificial intelligence algorithms, making it easier for the reader to implement or use them in their own agricultural domain

Artificial Intelligence and IoT-Based Technologies for Sustainable Farming and Smart Agriculture
Artificial Intelligence and IoT-Based Technologies for Sustainable Farming and Smart Agriculture

Author: Tomar, Pradeep

Publisher: IGI Global

Published: 2021-01-08

Total Pages: 400

ISBN-13: 1799817245

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As technology continues to saturate modern society, agriculture has started to adopt digital computing and data-driven innovations. This emergence of “smart” farming has led to various advancements in the field, including autonomous equipment and the collection of climate, livestock, and plant data. As connectivity and data management continue to revolutionize the farming industry, empirical research is a necessity for understanding these technological developments. Artificial Intelligence and IoT-Based Technologies for Sustainable Farming and Smart Agriculture provides emerging research exploring the theoretical and practical aspects of critical technological solutions within the farming industry. Featuring coverage on a broad range of topics such as crop monitoring, precision livestock farming, and agronomic data processing, this book is ideally designed for farmers, agriculturalists, product managers, farm holders, manufacturers, equipment suppliers, industrialists, governmental professionals, researchers, academicians, and students seeking current research on technological applications within agriculture and farming.

Sustainable Farming through Machine Learning
Sustainable Farming through Machine Learning

Author: Suneeta Satpathy

Publisher: CRC Press

Published: 2024-11-25

Total Pages: 301

ISBN-13: 1040254780

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This book explores the transformative potential of machine learning (ML) technologies in agriculture. It delves into specific applications, such as crop monitoring, disease detection, and livestock management, demonstrating how artificial intelligence/machine learning (AI/ML) can optimize resource management and improve overall productivity in farming practices. Sustainable Farming through Machine Learning: Enhancing Productivity and Efficiency provides an in-depth overview of AI and ML concepts relevant to the agricultural industry. It discusses the challenges faced by the agricultural sector and how AI/ML can address them. The authors highlight the use of AI/ML algorithms for plant disease and pest detection and examine the role of AI/ML in supply chain management and demand forecasting in agriculture. It includes an examination of the integration of AI/ML with agricultural robotics for automation and efficiency. The authors also cover applications in livestock management, including feed formulation and disease detection; they also explore the use of AI/ML for behavior analysis and welfare assessment in livestock. Finally, the authors also explore the ethical and social implications of using such technologies. This book can be used as a textbook for students in agricultural engineering, precision farming, and smart agriculture. It can also be a reference book for practicing professionals in machine learning, and deep learning working on sustainable agriculture applications.

Smart Agriculture
Smart Agriculture

Author: Govind Singh Patel

Publisher: CRC Press

Published: 2021-02-10

Total Pages: 222

ISBN-13: 1000327876

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This book endeavours to highlight the untapped potential of Smart Agriculture for the innovation and expansion of the agriculture sector. The sector shall make incremental progress as it learns from associations between data over time through Artificial Intelligence, deep learning and Internet of Things applications. The farming industry and Smart agriculture develop from the stringent limits imposed by a farm's location, which in turn has a series of related effects with respect to supply chain management, food availability, biodiversity, farmers' decision-making and insurance, and environmental concerns among others. All of the above-mentioned aspects will derive substantial benefits from the implementation of a data-driven approach under the condition that the systems, tools and techniques to be used have been designed to handle the volume and variety of the data to be gathered. Contributions to this book have been solicited with the goal of uncovering the possibilities of engaging agriculture with equipped and effective profound learning algorithms. Most agricultural research centres are already adopting Internet of Things for the monitoring of a wide range of farm services, and there are significant opportunities for agriculture administration through the effective implementation of Machine Learning, Deep Learning, Big Data and IoT structures.

Smart Farming Technologies for Sustainable Agricultural Development
Smart Farming Technologies for Sustainable Agricultural Development

Author: Poonia, Ramesh C.

Publisher: IGI Global

Published: 2018-08-10

Total Pages: 330

ISBN-13: 1522559108

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In order to meet food needs, farmers need to integrate the latest technologies enabling them to make more informed decisions. Smart Farming Technologies for Sustainable Agricultural Development provides innovative insights into the latest farming advancements in terms of informatics and communication. The content within this publication represents the work of topics such as sensor systems, wireless communication, and the integration of the Internet of Things in agriculture-related processes. It is a vital reference source for farmers, academicians, researchers, government agencies, technology developers, and graduate-level students seeking current research on smart farming technologies.

Sustainable Development through Machine Learning, AI and IoT
Sustainable Development through Machine Learning, AI and IoT

Author: Pawan Whig

Publisher: Springer Nature

Published: 2023-12-20

Total Pages: 384

ISBN-13: 3031470559

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This book constitutes the revised selected papers of the First International Conference, ICSD 2023, virtually held in Delhi, India, during July 15–16, 2023. The book comprises 31 full papers that were selected from a total of 129 submissions. It provides insights into the latest research and advancements in sustainable development through the integration of machine learning, artificial intelligence, and IoT technologies. It serves as a valuable resource for researchers, practitioners, and policymakers working in the field of sustainable development.

Machine Learning for Sustainable Development
Machine Learning for Sustainable Development

Author: Kamal Kant Hiran

Publisher: Walter de Gruyter GmbH & Co KG

Published: 2021-07-19

Total Pages: 214

ISBN-13: 3110702517

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The book will focus on the applications of machine learning for sustainable development. Machine learning (ML) is an emerging technique whose diffusion and adoption in various sectors (such as energy, agriculture, internet of things, infrastructure) will be of enormous benefit. The state of the art of machine learning models is most useful for forecasting and prediction of various sectors for sustainable development.

Reshaping Environmental Science Through Machine Learning and IoT
Reshaping Environmental Science Through Machine Learning and IoT

Author: Gupta, Rajeev Kumar

Publisher: IGI Global

Published: 2024-05-06

Total Pages: 459

ISBN-13:

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In the face of escalating environmental challenges such as climate change, air and water pollution, and natural disasters, traditional approaches to understanding and addressing these issues have yet to be proven sufficient. Academic scholars are compelled to seek innovative solutions that marry digital intelligence and natural ecosystems. Reshaping Environmental Science Through Machine Learning and IoT serves as a comprehensive exploration into the transformative potential of Machine Learning (ML) and the Internet of Things (IoT) to address critical environmental challenges. The book establishes a robust foundation in ML and IoT, explaining their relevance to environmental science. As the narrative unfolds, it delves into diverse applications, providing theoretical insights alongside practical knowledge. From interpreting weather patterns to predicting air and water quality, the book navigates through the intricate web of environmental complexities. Notably, it unveils approaches to disaster management, waste sorting, and climate change monitoring, showcasing the symbiotic relationship between digital intelligence and natural ecosystems. This book is ideal for audiences from students and researchers to data scientists and disaster management professionals with a nuanced understanding of IoT, ML, and Artificial Intelligence (AI).

Cryptocurrencies and Blockchain Technology Applications
Cryptocurrencies and Blockchain Technology Applications

Author: Gulshan Shrivastava

Publisher: John Wiley & Sons

Published: 2020-06-30

Total Pages: 336

ISBN-13: 111962116X

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As we enter the Industrial Revolution 4.0, demands for an increasing degree of trust and privacy protection continue to be voiced. The development of blockchain technology is very important because it can help frictionless and transparent financial transactions and improve the business experience, which in turn has far-reaching effects for economic, psychological, educational and organizational improvements in the way we work, teach, learn and care for ourselves and each other. Blockchain is an eccentric technology, but at the same time, the least understood and most disruptive technology of the day. This book covers the latest technologies of cryptocurrencies and blockchain technology and their applications. This book discusses the blockchain and cryptocurrencies related issues and also explains how to provide the security differently through an algorithm, framework, approaches, techniques and mechanisms. A comprehensive understanding of what blockchain is and how it works, as well as insights into how it will affect the future of your organization and industry as a whole and how to integrate blockchain technology into your business strategy. In addition, the book explores the blockchain and its with other technologies like Internet of Things, big data and artificial intelligence, etc.

Internet of Things and Machine Learning in Agriculture
Internet of Things and Machine Learning in Agriculture

Author: Jyotir Moy Chatterjee

Publisher: Walter de Gruyter GmbH & Co KG

Published: 2021-02-08

Total Pages: 424

ISBN-13: 3110691272

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Agriculture is one of the most fundamental human activities. As the farming capacity has expanded, the usage of resources such as land, fertilizer, and water has grown exponentially, and environmental pressures from modern farming techniques have stressed natural landscapes. Still, by some estimates, worldwide food production needs to increase to keep up with global food demand. Machine Learning and the Internet of Things can play a promising role in the Agricultural industry, and help to increase food production while respecting the environment. This book explains how these technologies can be applied, offering many case studies developed in the research world.