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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Yes, you can access Predictive Analytics in Smart Agriculture by Saravanan Krishnan, A.Jose Anand, Narayanan Prasanth, Sam Goundar, Christo Ananth, Saravanan Krishnan,A.Jose Anand,Narayanan Prasanth,Sam Goundar,Christo Ananth in PDF and/or ePUB format, as well as other popular books in Technology & Engineering & Agronomy. We have over one million books available in our catalogue for you to explore.

Information

Publisher

Year

Print ISBN

eBook ISBN

Edition

Topic

Technology & Engineering

Subtopic

Agronomy

Index

Technology & Engineering

Cover Page
Half-Title Page
Title Page
Copyright Page
Contents
Contributors
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 Design of Autonomous Unmanned Ground Vehicles (UGVs) in Smart Agriculture
Index

About this book

Frequently asked questions

Information

Table of contents