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Ajouter au panierHardcover. Etat : Brand New. 302 pages. 9.25x6.10x0.91 inches. In Stock.
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Vendeur : Lucky's Textbooks, Dallas, TX, Etats-Unis
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Edité par Springer Nature Switzerland, Springer International Publishing, 2024
ISBN 10 : 3031526449 ISBN 13 : 9783031526442
Langue: anglais
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Ajouter au panierBuch. Etat : Neu. Druck auf Anfrage Neuware - Printed after ordering - In the dynamic realm of digital agriculture, the integration of big data acquisition platforms has sparked both curiosity and enthusiasm among researchers and agricultural practitioners. This book embarks on a journey to explore the intersection of artificial intelligence and agriculture, focusing on small-unmanned aerial vehicles (UAVs), unmanned ground vehicles (UGVs), edge-AI sensors and the profound impact they have on digital agriculture, particularly in the context of heterogeneous crops, such as walnuts, pomegranates, cotton, etc. For example, lightweight sensors mounted on UAVs, including multispectral and thermal infrared cameras, serve as invaluable tools for capturing high-resolution images. Their enhanced temporal and spatial resolutions, coupled with cost effectiveness and near-real-time data acquisition, position UAVs as an optimal platform for mapping and monitoring crop variability in vast expanses. This combination of data acquisition platforms and advanced analytics generates substantial datasets, necessitating a deep understanding of fractional-order thinking, which is imperative due to the inherent 'complexity' and consequent variability within the agricultural process. Much optimism is vested in the field of artificial intelligence, such as machine learning (ML) and computer vision (CV), where the efficient utilization of big data to make it 'smart' is of paramount importance in agricultural research. Central to this learning process lies the intricate relationship between plant physiology and optimization methods. The key to the learning process is the plant physiology and optimization method. Crafting an efficient optimization method raises three pivotal questions: 1.) What represents the best approach to optimization 2.) How can we achieve a more optimal optimization 3.) Is it possible to demand 'more optimal machine learning,' exemplified by deep learning, while minimizing the need for extensive labeled data for digital agriculture This book details the foundations of the plant physiology-informed machine learning (PPIML) and the principle of tail matching (POTM) framework. It is the 9th title of the 'Agriculture Automation and Control' book series published by Springer.
Edité par Springer Nature Switzerland, Springer Nature Switzerland, 2025
ISBN 10 : 3031526473 ISBN 13 : 9783031526473
Langue: anglais
Vendeur : AHA-BUCH GmbH, Einbeck, Allemagne
EUR 160,49
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Ajouter au panierTaschenbuch. Etat : Neu. Druck auf Anfrage Neuware - Printed after ordering - In the dynamic realm of digital agriculture, the integration of big data acquisition platforms has sparked both curiosity and enthusiasm among researchers and agricultural practitioners. This book embarks on a journey to explore the intersection of artificial intelligence and agriculture, focusing on small-unmanned aerial vehicles (UAVs), unmanned ground vehicles (UGVs), edge-AI sensors and the profound impact they have on digital agriculture, particularly in the context of heterogeneous crops, such as walnuts, pomegranates, cotton, etc. For example, lightweight sensors mounted on UAVs, including multispectral and thermal infrared cameras, serve as invaluable tools for capturing high-resolution images. Their enhanced temporal and spatial resolutions, coupled with cost effectiveness and near-real-time data acquisition, position UAVs as an optimal platform for mapping and monitoring crop variability in vast expanses. This combination of data acquisition platforms and advanced analytics generates substantial datasets, necessitating a deep understanding of fractional-order thinking, which is imperative due to the inherent 'complexity' and consequent variability within the agricultural process. Much optimism is vested in the field of artificial intelligence, such as machine learning (ML) and computer vision (CV), where the efficient utilization of big data to make it 'smart' is of paramount importance in agricultural research. Central to this learning process lies the intricate relationship between plant physiology and optimization methods. The key to the learning process is the plant physiology and optimization method. Crafting an efficient optimization method raises three pivotal questions: 1.) What represents the best approach to optimization 2.) How can we achieve a more optimal optimization 3.) Is it possible to demand 'more optimal machine learning,' exemplified by deep learning, while minimizing the need for extensive labeled data for digital agriculture This book details the foundations of the plant physiology-informed machine learning (PPIML) and the principle of tail matching (POTM) framework. It is the 9th title of the 'Agriculture Automation and Control' book series published by Springer.
Vendeur : California Books, Miami, FL, Etats-Unis
EUR 177,36
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Vendeur : Ria Christie Collections, Uxbridge, Royaume-Uni
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Edité par Elsevier Science & Technology, Oxford, 2025
ISBN 10 : 0323999328 ISBN 13 : 9780323999328
Langue: anglais
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Ajouter au panierPaperback. Etat : new. Paperback. Big Data Analytics in Agriculture: Algorithms and Applications focuses on quantitative and qualitative assessment using state-of-the-art technology to provide practical improvements to agricultural production. The book provides a complete mappingfrom data generation to storage to curation, processing and implementation/applicationto produce high-quality reliable information for decision-making. It follows a logical pathway to demonstrate how data contributes to a converging flow of information towards a decision support system and how it can be transformed into actionable steps.The book develops ideas surrounding a strong integration of ICT and IoT to manage rural assets to deliver improved economic and environmental performance in a spatially and temporarily variable environment. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.
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Vendeur : California Books, Miami, FL, Etats-Unis
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Vendeur : Revaluation Books, Exeter, Royaume-Uni
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Ajouter au panierHardcover. Etat : Brand New. 257 pages. 9.25x6.10x0.63 inches. In Stock.
Vendeur : Books Puddle, New York, NY, Etats-Unis
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Vendeur : Lucky's Textbooks, Dallas, TX, Etats-Unis
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