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Deep learning for sustainable agriculture /

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...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Otros Autores: Poonia, Ramesh C., 1979- (Editor ), Singh, Vijander (Editor ), Nayak, Soumya Ranjan, 1984- (Editor )
Formato: Electrónico eBook
Idioma:Inglés
Publicado: London, United Kingdom : Elsevier Academic Press, [2022]
Colección:Cognitive data science in sustainable computing.
Temas:
Acceso en línea:Texto completo

MARC

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245 0 0 |a Deep learning for sustainable agriculture /  |c edited by Ramesh Chandra Poonia, Vijander Singh, Soumya Ranjan Nayak. 
264 1 |a London, United Kingdom :  |b Elsevier Academic Press,  |c [2022] 
264 4 |c �2022 
300 |a 1 online resource (xv, 390 pages) :  |b illustrations 
336 |a text  |b txt  |2 rdacontent 
337 |a computer  |b c  |2 rdamedia 
338 |a online resource  |b cr  |2 rdacarrier 
490 1 |a Cognitive data science in sustainable computing 
504 |a Includes bibliographical references and index. 
520 |a 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. 
650 0 |a Sustainable agriculture  |x Data processing. 
650 0 |a Artificial intelligence  |x Agricultural applications. 
650 0 |a Machine learning. 
650 0 |a Agricultural innovations. 
650 6 |a Agriculture durable  |0 (CaQQLa)201-0000576  |x Informatique.  |0 (CaQQLa)201-0380011 
650 6 |a Intelligence artificielle  |x Applications agricoles.  |0 (CaQQLa)201-0276653 
650 6 |a Apprentissage automatique.  |0 (CaQQLa)201-0131435 
650 6 |a Agriculture  |x Innovations.  |0 (CaQQLa)201-0000546 
650 7 |a Agricultural innovations  |2 fast  |0 (OCoLC)fst00800915 
650 7 |a Artificial intelligence  |x Agricultural applications  |2 fast  |0 (OCoLC)fst00817248 
650 7 |a Machine learning  |2 fast  |0 (OCoLC)fst01004795 
700 1 |a Poonia, Ramesh C.,  |d 1979-  |e editor. 
700 1 |a Singh, Vijander,  |e editor. 
700 1 |a Nayak, Soumya Ranjan,  |d 1984-  |e editor. 
776 0 8 |i Print version:  |z 9780323903622 
776 0 8 |i Print version:  |z 0323852149  |z 9780323852142  |w (OCoLC)1258782528 
776 0 8 |i Print version:  |t Deep learning for sustainable agriculture  |z 0323852149  |w (OCoLC)1258782528 
830 0 |a Cognitive data science in sustainable computing. 
856 4 0 |u https://sciencedirect.uam.elogim.com/science/book/9780323852142  |z Texto completo