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Federated Learning for Smart Agriculture and Food Quality Enhancement
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Mathematical Models Using Artificial Intelligence for Surveillance Systems
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Mathematical Models Using Artificial Intelligence for Surveillance Systems
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Federated Learning for Smart Agriculture and Food Quality Enhancement
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Mathematical Models Using Artificial Intelligence for Surveillance Systems
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Federated Learning for Smart Agriculture and Food Quality Enhancement
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Mathematical Models Using Artificial Intelligence for Surveillance Systems
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Add to basketCondition: New. 2024. 1st Edition. hardcover. . . . . .
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Federated Learning for Smart Agriculture and Food Quality Enhancement
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Add to basketHardcover. Condition: Brand New. 350 pages. 9.41x6.26x1.46 inches. In Stock.
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Federated Learning for Smart Agriculture and Food Quality Enhancement
Language: English
Published by John Wiley and Sons Inc, US, 2025
ISBN 10: 1394338694 ISBN 13: 9781394338696
Seller: Rarewaves.com USA, London, LONDO, United Kingdom
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Hardback. Condition: New. This essential book provides a comprehensive, expert-led guide on how federated learning can revolutionize crop yield, enhance resource management, and ensure a pathway to sustainable food quality and safety. The convergence of artificial intelligence, machine learning, and data science with agriculture and food, provides remarkable opportunities to improve quality, sustainability, and productivity in the agricultural sector. Federated Learning is a promising technology that has emerged at this intersection. In the context of smart agriculture, federated learning holds promise for improving crop yield, resource management, and decision-making. Additionally, federated learning provides greater clarity and understanding in the world of agriculture, encouraging stakeholders to explore and adopt this technology for improved farm management. Readers will find the book: Explores the integration of federated learning, a novel machine learning technique, into the realm of agriculture and food quality enhancement, showcasing the latest advancements;Introduces real-world applications of federated learning in agriculture, and demonstrates the way this technology can transform farming practices, crop monitoring, pest control, and food quality assurance;By bridging the fields of agriculture, machine learning, and food science, it offers a holistic perspective on leveraging technology to address challenges in food production and quality management;Emphasizes the importance of sustainability in agriculture, exploring how federated learning can contribute to more efficient resource utilization, reduced environmental impact, and the overall sustainability of food production systems;Discusses the future directions of smart agriculture and food quality enhancement, envisioning how federated learning and other emerging technologies can continue to shape the industry and address evolving challenges. Audience Agriculture specialists, agricultural engineers, professionals associated with food safety, crop managers, quality assurance professionals, IT professionals, data scientists, and academics working towards improved quality and sustainability in agriculture.
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Add to basketHardcover. Condition: Brand New. 400 pages. 6.26x1.22x9.29 inches. In Stock.
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Cyber Forensic Frameworks for User-Centric Human Threat Intelligence Analysis
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Add to basketPaperback. Condition: New.
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Condition: New. 1st edition NO-PA16APR2015-KAP.
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Condition: New. 2024. 1st Edition. hardcover. . . . . . Books ship from the US and Ireland.
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Buch. Condition: Neu. Neuware - This essential book provides a comprehensive, expert-led guide on how federated learning can revolutionize crop yield, enhance resource management, and ensure a pathway to sustainable food quality and safety. The convergence of artificial intelligence, machine learning, and data science with agriculture and food, provides remarkable opportunities to improve quality, sustainability, and productivity in the agricultural sector. Federated Learning is a promising technology that has emerged at this intersection. In the context of smart agriculture, federated learning holds promise for improving crop yield, resource management, and decision-making. Additionally, federated learning provides greater clarity and understanding in the world of agriculture, encouraging stakeholders to explore and adopt this technology for improved farm management. Readers will find the book: - Explores the integration of federated learning, a novel machine learning technique, into the realm of agriculture and food quality enhancement, showcasing the latest advancements;- Introduces real-world applications of federated learning in agriculture, and demonstrates the way this technology can transform farming practices, crop monitoring, pest control, and food quality assurance;- By bridging the fields of agriculture, machine learning, and food science, it offers a holistic perspective on leveraging technology to address challenges in food production and quality management;- Emphasizes the importance of sustainability in agriculture, exploring how federated learning can contribute to more efficient resource utilization, reduced environmental impact, and the overall sustainability of food production systems;- Discusses the future directions of smart agriculture and food quality enhancement, envisioning how federated learning and other emerging technologies can continue to shape the industry and address evolving challenges. Audience Agriculture specialists, agricultural engineers, professionals associated with food safety, crop managers, quality assurance professionals, IT professionals, data scientists, and academics working towards improved quality and sustainability in agriculture.
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Buch. Condition: Neu. Federated Learning for Smart Agriculture and Food Quality Enhancement | Padmesh Tripathi (u. a.) | Buch | Einband - fest (Hardcover) | Englisch | 2025 | Wiley | EAN 9781394338696 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu.
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Federated Learning for Smart Agriculture and Food Quality Enhancement
Language: English
Published by John Wiley and Sons Inc, US, 2025
ISBN 10: 1394338694 ISBN 13: 9781394338696
US$ 286.43
US$ 87.26 shipping
Ships from United Kingdom to U.S.A.Quantity: 1 available
Add to basketHardback. Condition: New. This essential book provides a comprehensive, expert-led guide on how federated learning can revolutionize crop yield, enhance resource management, and ensure a pathway to sustainable food quality and safety. The convergence of artificial intelligence, machine learning, and data science with agriculture and food, provides remarkable opportunities to improve quality, sustainability, and productivity in the agricultural sector. Federated Learning is a promising technology that has emerged at this intersection. In the context of smart agriculture, federated learning holds promise for improving crop yield, resource management, and decision-making. Additionally, federated learning provides greater clarity and understanding in the world of agriculture, encouraging stakeholders to explore and adopt this technology for improved farm management. Readers will find the book: Explores the integration of federated learning, a novel machine learning technique, into the realm of agriculture and food quality enhancement, showcasing the latest advancements;Introduces real-world applications of federated learning in agriculture, and demonstrates the way this technology can transform farming practices, crop monitoring, pest control, and food quality assurance;By bridging the fields of agriculture, machine learning, and food science, it offers a holistic perspective on leveraging technology to address challenges in food production and quality management;Emphasizes the importance of sustainability in agriculture, exploring how federated learning can contribute to more efficient resource utilization, reduced environmental impact, and the overall sustainability of food production systems;Discusses the future directions of smart agriculture and food quality enhancement, envisioning how federated learning and other emerging technologies can continue to shape the industry and address evolving challenges. Audience Agriculture specialists, agricultural engineers, professionals associated with food safety, crop managers, quality assurance professionals, IT professionals, data scientists, and academics working towards improved quality and sustainability in agriculture.
