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Submit a ProposalAgricultural Informatics
 | Automation Using IoT and Machine Learning Edited by Amitava Choudhury, Arindam Biswas, Manish Prateek, and Amlan Chakraborty Series: Advances in Learning Analytics for Intelligent Cloud-IoT Systems Copyright: 2021 | Status: Published ISBN: 9781119768845 | Hardcover | 295 pages | 103 illustrations Price: $195 USD  |
One Line Description
This book elucidates how the Internet of Things and machine learning-based solutions are revolutionizing the agriculture sector for increased crop yield and management.
Audience
Researchers in computer science, artificial intelligence, electronics engineering, agriculture automation, crop management and science.
Researchers in computer science, artificial intelligence, electronics engineering, agriculture automation, crop management and science.
Description
The emergence of automation in agriculture has become a critical 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 (the Food and Agriculture Organization of the United Nations estimates 70% more food will be needed in 2050 than was produced in 2006). 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.
In recent years, Internet of Things technology along with wireless communication, machine learning, artificial intelligence, and deep learning, have begun to be used to address various industrial and technical challenges to meet this growing need. These Agro-IoT tools 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 provides useful and critical information to farmers, ushering in a new era in farming. The IoT can be used as a tool to combat climate change through greenhouse automation; monitor and manage water, soil and crops; increase productivity; control insecticides/pesticides; detect plant diseases; increase the rate of crop sales; cattle monitoring etc.
Agricultural Informatics: Automation Using the IoT and Machine Learning focuses on all these topics, including a few case studies, and they give a clear indication as to why these techniques should now be widely adopted by the agriculture and farming industries.
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The emergence of automation in agriculture has become a critical 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 (the Food and Agriculture Organization of the United Nations estimates 70% more food will be needed in 2050 than was produced in 2006). 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.
In recent years, Internet of Things technology along with wireless communication, machine learning, artificial intelligence, and deep learning, have begun to be used to address various industrial and technical challenges to meet this growing need. These Agro-IoT tools 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 provides useful and critical information to farmers, ushering in a new era in farming. The IoT can be used as a tool to combat climate change through greenhouse automation; monitor and manage water, soil and crops; increase productivity; control insecticides/pesticides; detect plant diseases; increase the rate of crop sales; cattle monitoring etc.
Agricultural Informatics: Automation Using the IoT and Machine Learning focuses on all these topics, including a few case studies, and they give a clear indication as to why these techniques should now be widely adopted by the agriculture and farming industries.
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Author / Editor Details
Amitava Choudhury PhD is an assistant professor in the school of Computer Science, University of Petroleum & Energy Studies, Dehradun, India.
Amitava Choudhury PhD is an assistant professor in the school of Computer Science, University of Petroleum & Energy Studies, Dehradun, India.
Arindam Biswas PhD is an assistant professor in School of Mines and Metallurgy at Kazi Nazrul University, Asansol, West Bengal, India.
Manish Prateek PhD is Professor and Dean, School of Computer Science, at the University of Petroleum and Energy Studies, Dehradun, India.
Amlan Chakrabarti PhD is a Full Professor in the A.K. Choudhury School of Information Technology at the University of Calcutta.
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