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.
Sprache
Verlagsort
Verlagsgruppe
Zielgruppe
Für höhere Schule und Studium
Für Beruf und Forschung
Postgraduate, Professional Reference, and Undergraduate Advanced
Illustrationen
16 s/w Photographien bzw. Rasterbilder, 68 Farbfotos bzw. farbige Rasterbilder, 7 s/w Zeichnungen, 23 s/w Abbildungen, 84 farbige Abbildungen, 16 farbige Zeichnungen, 28 s/w Tabellen
28 Tables, black and white; 16 Line drawings, color; 7 Line drawings, black and white; 68 Halftones, color; 16 Halftones, black and white; 84 Illustrations, color; 23 Illustrations, black and white
Maße
Höhe: 240 mm
Breite: 161 mm
Dicke: 21 mm
Gewicht
ISBN-13
978-1-032-47950-7 (9781032479507)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Klassifikation
Dr. K. Saravanan is an Associate professor, Department of Computer Science & Engineering at College of Engineering, Guindy, Anna University, Chennai, Tamilnadu, India. He has published papers in 14 international conferences and 27 international journals. He is an active researcher and academician. He has written 16 book chapters and seven books in various international publishers. He is a Member of IEI, ISTE, ACM and ISCA. His current research interests are Blockchain technology, smart cities, cloud computing, software engineering, IoT.
Dr. A. Jose Anand is an Associate Professor, Department of Electronics and Communication Engineering, KCG College of Technology, Chennai, Tamil Nadu. He has one year of industrial experience and twenty-four years of teaching experience. He published several papers in National Journal and International Journal, and also published books in polytechnic &Engineering subjects. He is a Member of CSI, IEI, IET, IETE, ISTE, INS, QCFI and EWB. His current research interests are Wireless Sensor Networks, Embedded systems, IoT, Machine Learning and Image Processing.
Dr. N. Narayanan Prasanth is an Associate Professor, Department of Database Systems, School of Computer Science and Engineering, Vellore Institute of Technology, Vellore, Tamilnadu, India. His research interests include Computer Networks, Wireless Sensor Networks, IoT, Parallel and High Performance Computing.
Professor Dr. Sam Goundar is an International Academic having taught at twelve different universities in ten different countries. He is the Editor-in-Chief of the International Journal of Blockchains and Cryptocurrencies (IJBC) - Inderscience Publishers, Editor-in-Chief of the International Journal of Fog Computing (IJFC) - IGI Publishers, Editor-in-Chief of the International Journal of Creative Computing (IJCrC) - Inderscience Publishers, Section Editor of the Journal of Education and Information Technologies (EAIT) - Springer and Editor-in-Chief (Emeritus) of the International Journal of Cloud Applications and Computing (IJCAC) - IGI Publishers. He has 126 publications in in journals and as chapters in books (many of them indexed by Scopus and Web of Science). He has written and edited thirteen books that has been published.
Dr. Christo Ananth is a Professor, Samarkand State University, Uzbekistan, Russia. He has published more than 60 papers in various Conferences and Journals. He is a reviewer for various International Peer Reviewed Journals. He has authored 7 textbooks and has organized more than 140 events such as Symposiums and Conferences. He was chosen as an elected fellow from ISECE (Malaysia) and a Life Member of ISTE (India).
Herausgeber*in
Anna Uni. Regional Campus
KCG College of Technology, India
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
