Department of Computer Science and Engineering, Global Institute of Management and Technology, Krishna Nagar, West Bengal, India. E-mail: [email protected]
Publishers at Scrivener Martin Scrivener ([email protected]) Phillip Carmical ([email protected])
Agricultural Informatics
Automation Using the IoT and Machine Learning
Edited by
Amitava Choudhury,
Arindam Biswas, Manish Prateek
and
Amlan Chakrabarti
This edition first published 2021 by John Wiley & Sons, Inc., 111 River Street, Hoboken, NJ 07030, USA and Scrivener Publishing LLC, 100 Cummings Center, Suite 541J, Beverly, MA 01915, USA
© 2021 Scrivener Publishing LLC
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Library of Congress Cataloging-in-Publication Data
ISBN 978-1-119-76884-5
Cover image: Pixabay.Com Cover design by Russell Richardson
Set in size of 11pt and Minion Pro by Manila Typesetting Company, Makati, Philippines
Printed in the USA
10 9 8 7 6 5 4 3 2 1
Preface
The emergence of automation in agriculture has become an important issue for every country. The world population is increasing at a very fast rate, and along with this increase in population the need for food is also increasing at a brisk pace. Traditional methods used by farmers are no longer sufficient to serve this increasing demand, resulting in the intensified use of harmful pesticides. This in turn has had a profound effect on agricultural practices, which in the end can render the land barren. This book discusses the different automation practices, including the internet of things (IoT), wireless communications, machine learning, artificial intelligence, and deep learning, currently being employed to address this problem. There are some areas of concern in the field of agriculture, such as crop disease, lack of storage, weed and water management, pesticide control, and lack of irrigation, all of which can be solved using the different techniques mentioned above.
From the earliest civilizations up till now, clothing, shelter and food have been the three primary needs of human beings that have remained constant. And even though we have become quite advanced in addressing issues related to housing and clothing, despite the increasing population (as per the Food and Agriculture Organization of the United Nations, 70% more food will need to be produced in 2050 than was produced in 2006), issues related to food production have yet to be completely addressed. In recent years, the IoT began to be used to address different industrial and technical challenges to meet this growing need. Therefore, now is the time to meet the future demands of farming which can only be accomplished by smart Agro-IoT tools. This will in turn boost productivity and minimize the pitfalls of traditional farming, which is the backbone of the world’s economy. Aided by the IoT, continuous monitoring of fields will provide useful information to farmers, ushering in a new era in farming. The IoT can be used as a tool to combat climate change; monitor and manage water, land, soil and crops; increase productivity; control insecticides/pesticides; detect plant diseases; increase the rate of crop sales; etc. This book will focus on some case studies that involve monitoring of climate conditions, greenhouse automation, crop management, cattle monitoring and management for smart farming with IoT devices, which will give a clear indication as to why these techniques should be used in agriculture rather than some of the previously developed agricultural tools currently in use.
Organization of the Book
We are delighted to present this book, which was made possible with the support and contributions from academicians from various highly reputable institutions. It is a manifestation of various interesting and important aspects of theoretical and applied research covering complementary facets of innovative algorithms and applications in the fields of agriculture and cultivation processes, including:
Machine learning algorithm and its role in agriculture
Smart farming using machine learning and the IoT
Agricultural informatics vis-à-vis the IoT
Application of agricultural drones
Real-time monitoring of
