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Librería: Books Puddle, New York, NY, Estados Unidos de America
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Añadir al carritoCondición: New. 1st edition NO-PA16APR2015-KAP.
Idioma: Inglés
Publicado por Elsevier Science & Technology, 2022
ISBN 10: 0323852149 ISBN 13: 9780323852142
Librería: THE SAINT BOOKSTORE, Southport, Reino Unido
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Añadir al carritoPaperback / softback. Condición: New. New copy - Usually dispatched within 4 working days.
Librería: Biblios, Frankfurt am main, HESSE, Alemania
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Librería: Ria Christie Collections, Uxbridge, Reino Unido
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Librería: GreatBookPrices, Columbia, MD, Estados Unidos de America
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Librería: preigu, Osnabrück, Alemania
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Añadir al carritoTaschenbuch. Condición: Neu. Deep Learning for Sustainable Agriculture | Ramesh Chandra Poonia (u. a.) | Taschenbuch | Einband - fest (Hardcover) | Englisch | 2022 | Academic Press | EAN 9780323852142 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu.
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Librería: moluna, Greven, Alemania
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Añadir al carritoCondición: New. 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 monito.
Librería: Brook Bookstore On Demand, Napoli, NA, Italia
EUR 118,06
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Librería: Revaluation Books, Exeter, Reino Unido
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Añadir al carritoPaperback. Condición: Brand New. 390 pages. 9.00x6.00x0.75 inches. In Stock. This item is printed on demand.
Idioma: Inglés
Publicado por Elsevier Science & Technology, Academic Press, 2022
ISBN 10: 0323852149 ISBN 13: 9780323852142
Librería: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Alemania
EUR 131,00
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Añadir al carritoTaschenbuch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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. Englisch.
Idioma: Inglés
Publicado por Elsevier Science & Technology, Academic Press, 2022
ISBN 10: 0323852149 ISBN 13: 9780323852142
Librería: AHA-BUCH GmbH, Einbeck, Alemania
EUR 145,67
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Añadir al carritoTaschenbuch. Condición: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - 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.