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  • Digital Farming: Case Studies and Applications of IoT and AI in Agricultural Industry

    Digital Farming by Chandrakumar, T.; Sharma, Bhisham; Chowdhury, Subrata;

    Case Studies and Applications of IoT and AI in Agricultural Industry

    Series: Chapman & Hall/CRC Internet of Things;

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      • Publisher's listprice GBP 171.99
      • The price is estimated because at the time of ordering we do not know what conversion rates will apply to HUF / product currency when the book arrives. In case HUF is weaker, the price increases slightly, in case HUF is stronger, the price goes lower slightly.

        77 653 Ft (73 955 Ft + 5% VAT)
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      • Discounted price 69 887 Ft (66 560 Ft + 5% VAT)

    69 887 Ft

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    Product details:

    • Edition number 1
    • Publisher Chapman and Hall
    • Date of Publication 21 October 2026

    • ISBN 9781032767253
    • Binding Hardback
    • No. of pages282 pages
    • Size 234x156 mm
    • Language English
    • Illustrations 102 Illustrations, black & white; 15 Halftones, black & white; 87 Line drawings, black & white; 14 Tables, black & white
    • 700

    Categories

    Short description:

    This book explores IoT-based ecosystems and AI-driven automation in agriculture to provide solutions for intelligent farming systems and precision agriculture techniques. It provides a comprehensive view of emerging technologies’ integration and the resultant impact on the farming ecosystem.

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    Long description:

    This book explores IoT-based ecosystems and AI-driven automation in agriculture to provide solutions for intelligent farming systems and precision agriculture techniques. It provides a comprehensive view of emerging technologies’ integration and the resultant impact on the farming ecosystem. Real-world case studies and examples
    of IoT and AI applications in agriculture are included to show how IoT and AI have revolutionized conventional agriculture. The book also examines the potential and difficulties of deploying IoT and AI in the agricultural business with case studies.



    Features:
    • Demonstrates how technologies like precision agriculture, smart sensors, and automated systems can be utilized to boost production, reduce waste, and increase sustainability by optimizing resource usage and reducing
    environmental impact.
    • Explains how small sensors, intelligence drones, or low-power Internet of Things setups for detecting, monitoring, gathering, analyzing, and storing data through cloud platforms are encouraging smart and intelligent agriculture.
    • Highlights IoT and AI intervention options for effective assessment of musculoskeletal, ergonomic, and postural analyses.
    • Explains usage of AI and IoT in precision farming, agricultural drones and hopping systems, livestock monitoring, temperature monitoring, smart greenhouses, and computer imaging.
    • Discusses challenges like data security, rural connectivity issues, infrastructure limitations, and the digital divide in rural areas.



    The book is a helpful reference for professionals, scholars, and students interested in digital farming and sustainable agricultural practices. It acts as a guiding tool for practitioners, researchers, educators, and policymakers, encouraging discussions, innovations, and advancements that will shape a sustainable, efficient, and resilient future for agriculture.

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    Table of Contents:

    Chapter 1. Applications of Artificial Intelligence (AI) and IoT in Smart Farming. Chapter 2. Integrating Blockchain for Enhanced Paddy Disease Traceability: A Conceptual Model. Chapter 3. Glucose Biosensors for Diabetes Monitoring: Fundamentals and Latest Technological Developments. Chapter 4. Agriculture Drones and Hopping systems. Chapter 5. Applications and Challenges of Metaverse in the Agriculture Sector: A Review. Chapter 6. Enhancing a Predictive Model for Forecasting Paddy Seed Yields Using Generative Techniques. Chapter 7. Cost Efficiency and Environmental Impact of Agribots in Modern Farming. Chapter 8. Impact of Agribots on Agricultural Productivity and Market Performance. Chapter 9. Smart Farming and Cyber Security: Safeguarding Agricultural Data and Infrastructure. Chapter 10. A Crop Cultivation Optimization Method Based on Digital Twins. Chapter 11. Revolutionizing Agriculture: Harnessing the Power of AI for Precision Farming and Sustainable Crop Management. Chapter 12. AgriSCM: A Blockchain-Powered Agricultural Supply Chain Management. Chapter 13. RoseLeafNet: A Novel Deep Learning Framework for Real-Time Detection and Classification of Rose Leaf Diseases Based on MobileNetV3

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