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    Predictive Analytics in Smart Agriculture

    Predictive Analytics in Smart Agriculture by Krishnan, Saravanan; Anand, A.Jose; Prasanth, Narayanan;

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      • Publisher's listprice GBP 125.00
      • 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.

        63 262 Ft (60 250 Ft + 5% VAT)
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    63 262 Ft

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    Availability

    Estimated delivery time: In stock at the publisher, but not at Prospero's office. Delivery time approx. 3-5 weeks.
    Not in stock at Prospero.

    Why don't you give exact delivery time?

    Delivery time is estimated on our previous experiences. We give estimations only, because we order from outside Hungary, and the delivery time mainly depends on how quickly the publisher supplies the book. Faster or slower deliveries both happen, but we do our best to supply as quickly as possible.

    Product details:

    • Edition number 1
    • Publisher CRC Press
    • Date of Publication 18 December 2023

    • ISBN 9781032479507
    • Binding Hardback
    • No. of pages312 pages
    • Size 234x156 mm
    • Weight 453 g
    • Language English
    • Illustrations 23 Illustrations, black & white; 84 Illustrations, color; 16 Halftones, black & white; 68 Halftones, color; 7 Line drawings, black & white; 16 Line drawings, color; 28 Tables, black & white
    • 571

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    Short description:

    This book explores computational engineering techniques and applications in agriculture development. Recent technologies such as cloud computing, IoT, big data, and machine learning are focused on for smart agricultural engineering. This book provides practical and use case oriented approaches for IOT-based agricultural systems.

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    Long description:

    Predictive Analysis in Smart Agricultureexplores computational engineering techniques and applications in agriculture development. Recent technologies such as cloud computing, IoT, big data, and machine learning are focused on for smart agricultural engineering. The book also provides a case-oriented approach for IoT-based agricultural systems.


    This book deals with all aspects of smart agriculture with state-of-the-art predictive analysis in the complete 360-degree view spectrum. The book includes the concepts of urban and vertical farming using Agro IoT systems and renewable energy sources for modern agriculture trends. It discusses the real-world challenges, complexities in Agro IoT, and advantages of incorporating smart technology. It also presents the rapid advancement of the technologies in the existing Agri model by applying the various techniques. Novel architectural solutions in smart agricultural engineering are the core aspects of this book. Several predictive analysis tools and smart agriculture are also incorporated.


    This book can be used as a textbook for students in predictive analysis, agriculture engineering, precision farming, and smart agriculture. It can also be a reference book for practicing professionals in cloud computing, IoT, big data, machine learning, and deep learning working on smart agriculture applications.

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    Table of Contents:

    Chapter 1. Farming Assistance Using Machine Learning and Internet of Things



    Chapter 2. Automated Seasonal Crop Mapping and Acreage Estimation Framework Using Machine Learning Algorithms: A Survey



    Chapter 3. Artificial Intelligence in Precision Agriculture: A Systematic Review on Tools, Techniques and Applications



    Chapter 4. Chatbot for Smart Farming using AI and NLP Techniques



    Chapter 5. Soil Analysis and Nutrient Recommendation System Using IoT and Multilayer Perceptron (MLP) Model



    Chapter 6. IoT Enabled Smart Irrigation with Machine Learning Models for Precision Farming



    Chapter 7. Leaf-CAP: A Capsule Network-based Tea Leaf Disease Recognition and Detection



    Chapter 8. Agri Retail Product Management System



    Chapter 9. Challenges and Prospects of Implementing Information and Communication Technology for Small-Scale Farmers. 



    Chapter 10. Navigating Ethical and Legal Challenges in Smart Agriculture: Insights from Farmers



    Chapter 11. Decision Support System for Smart Agriculture in Predictive Analysis



    Chapter 12. Broad Framework of Digital Twins In Agricultural Domain



    Chapter 13. Predictive Analytics of Climate Change: The Future of Global Warming Lies in Data Analytics



    Chapter 14. Applications of Drones in Predictive Analytics



    Chapter 15. Autonomous Unmanned Ground Vehicles (UGVs) in Smart Agriculture

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