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Seller: GreatBookPricesUK, Woodford Green, United Kingdom
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Language: English
Published by H N H International Limited, 2024
ISBN 10: 1032832800 ISBN 13: 9781032832807
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Seller: GreatBookPricesUK, Woodford Green, United Kingdom
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Language: English
Published by H N H International Limited, 2024
ISBN 10: 1032832800 ISBN 13: 9781032832807
Seller: Books Puddle, New York, NY, U.S.A.
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Language: English
Published by Taylor & Francis Ltd, 2024
ISBN 10: 1032832800 ISBN 13: 9781032832807
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Add to basketHardback. Condition: New. New copy - Usually dispatched within 4 working days.
Language: English
Published by H N H International Limited, 2024
ISBN 10: 1032832800 ISBN 13: 9781032832807
Seller: Biblios, Frankfurt am main, HESSE, Germany
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Seller: Revaluation Books, Exeter, United Kingdom
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Add to basketHardcover. Condition: Brand New. 216 pages. 9.18x6.12x9.21 inches. In Stock.
Language: English
Published by Taylor & Francis Ltd, London, 2024
ISBN 10: 1032832800 ISBN 13: 9781032832807
Seller: Grand Eagle Retail, Bensenville, IL, U.S.A.
Hardcover. Condition: new. Hardcover. This book, Smart Agriculture: Harnessing Machine Learning for Crop Management, is a comprehensive guide designed to explore the various facets of integrating machine learning into agricultural practices. It aims to provide readers with a solid foundation in machine learning concepts while demonstrating their practical applications in real-world farming scenarios. It also examines the role of remote monitoring and precision agriculture, highlighting how technologies such as remote sensing and recurrent neural networks can optimize farming practices.This book: Emphasizes sustainable agricultural practices and data-driven decision-making for eco-friendly farming. Highlights the importance of using environmentally friendly practices, and how machine learning can play a pivotal role in achieving sustainability goals. Discusses topics such as crop optimization, disease detection, pest control, resource management, precision agriculture, and sustainability. Covers predictive analytics for weather forecasting, Internet of Things applications for precision agriculture, and the role of sensors in data collection. Illustrates optimizing resource allocation, irrigation with artificial intelligence, and machine learning for soil health assessment. Whether you are a researcher, a student, an agricultural professional, or a technology enthusiast, this book offers valuable insights into the transformative power of machine learning in agriculture. It invites readers to explore the potential of machine learning to transform farming practices, improve food security, and promote environmental sustainability. This book is a comprehensive guide designed to explore the various facets of integrating machine learning into agricultural practices. It aims to provide readers with a solid foundation in machine learning concepts while demonstrating their practical applications in real-world farming scenarios. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
Language: English
Published by Taylor & Francis Ltd, London, 2024
ISBN 10: 1032832800 ISBN 13: 9781032832807
Seller: CitiRetail, Stevenage, United Kingdom
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Add to basketHardcover. Condition: new. Hardcover. This book, Smart Agriculture: Harnessing Machine Learning for Crop Management, is a comprehensive guide designed to explore the various facets of integrating machine learning into agricultural practices. It aims to provide readers with a solid foundation in machine learning concepts while demonstrating their practical applications in real-world farming scenarios. It also examines the role of remote monitoring and precision agriculture, highlighting how technologies such as remote sensing and recurrent neural networks can optimize farming practices.This book: Emphasizes sustainable agricultural practices and data-driven decision-making for eco-friendly farming. Highlights the importance of using environmentally friendly practices, and how machine learning can play a pivotal role in achieving sustainability goals. Discusses topics such as crop optimization, disease detection, pest control, resource management, precision agriculture, and sustainability. Covers predictive analytics for weather forecasting, Internet of Things applications for precision agriculture, and the role of sensors in data collection. Illustrates optimizing resource allocation, irrigation with artificial intelligence, and machine learning for soil health assessment. Whether you are a researcher, a student, an agricultural professional, or a technology enthusiast, this book offers valuable insights into the transformative power of machine learning in agriculture. It invites readers to explore the potential of machine learning to transform farming practices, improve food security, and promote environmental sustainability. This book is a comprehensive guide designed to explore the various facets of integrating machine learning into agricultural practices. It aims to provide readers with a solid foundation in machine learning concepts while demonstrating their practical applications in real-world farming scenarios. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.
Seller: preigu, Osnabrück, Germany
Buch. Condition: Neu. Smart Agriculture | Harnessing Machine Learning for Crop Management | Amol Dattatray Dhaygude (u. a.) | Buch | Einband - fest (Hardcover) | Englisch | 2024 | CRC Press | EAN 9781032832807 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu Print on Demand.
Language: English
Published by Taylor & Francis Ltd, London, 2024
ISBN 10: 1032832800 ISBN 13: 9781032832807
Seller: AussieBookSeller, Truganina, VIC, Australia
Hardcover. Condition: new. Hardcover. This book, Smart Agriculture: Harnessing Machine Learning for Crop Management, is a comprehensive guide designed to explore the various facets of integrating machine learning into agricultural practices. It aims to provide readers with a solid foundation in machine learning concepts while demonstrating their practical applications in real-world farming scenarios. It also examines the role of remote monitoring and precision agriculture, highlighting how technologies such as remote sensing and recurrent neural networks can optimize farming practices.This book: Emphasizes sustainable agricultural practices and data-driven decision-making for eco-friendly farming. Highlights the importance of using environmentally friendly practices, and how machine learning can play a pivotal role in achieving sustainability goals. Discusses topics such as crop optimization, disease detection, pest control, resource management, precision agriculture, and sustainability. Covers predictive analytics for weather forecasting, Internet of Things applications for precision agriculture, and the role of sensors in data collection. Illustrates optimizing resource allocation, irrigation with artificial intelligence, and machine learning for soil health assessment. Whether you are a researcher, a student, an agricultural professional, or a technology enthusiast, this book offers valuable insights into the transformative power of machine learning in agriculture. It invites readers to explore the potential of machine learning to transform farming practices, improve food security, and promote environmental sustainability. This book is a comprehensive guide designed to explore the various facets of integrating machine learning into agricultural practices. It aims to provide readers with a solid foundation in machine learning concepts while demonstrating their practical applications in real-world farming scenarios. This item is printed on demand. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.
Seller: AHA-BUCH GmbH, Einbeck, Germany
Buch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - This book is a comprehensive guide designed to explore the various facets of integrating machine learning into agricultural practices. It aims to provide readers with a solid foundation in machine learning concepts while demonstrating their practical applications in real-world farming scenarios.