Agricultural Internet of Things and Decision Support for Precision Smart Farming, ISBN-13: 978-0128183731
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- Publisher: Academic Press; 1st edition (January 28, 2020)
- Language: English
- 470 pages
- ISBN-10: 012818373X
- ISBN-13: 978-0128183731
Covers precision farming systems that collect and use context data to build empirical evidence for identifying and applying agronomic decisions.
Agricultural Internet of Things and Decision Support for Smart Farming reveals how a set of key enabling technologies (KET) related to agronomic management, remote and proximal sensing, data mining, decision-making and automation can be efficiently integrated in one system. Chapters cover how KETs enable real-time monitoring of soil conditions, determine real-time, site-specific requirements of crop systems, help develop a decision support system (DSS) aimed at maximizing the efficient use of resources, and provide planning for agronomic inputs differentiated in time and space. This book is ideal for researchers, academics, post-graduate students and practitioners who want to embrace new agricultural technologies.
Table of Contents:
Cover image
Title page
Table of Contents
Copyright
Contributors
Preface
Chapter 1. Introduction to agricultural IoT
1.1. Introduction section: an integrated view on precision smart farming from a multidisciplinary perspective
Chapter 2. Monitoring
2.1. Introduction
2.2. Remote sensing
2.3. Proximal sensing
2.4. Conclusions
Chapter 3. Data processing
3.1. Introduction
3.2. Statistical approach to data fusion
Chapter 4. Support to decision-making
4.1. Introduction to decision support functions
4.2. From spatial data to site-specific decisions and action
4.3. Planning and optimization
4.4. Information systems for smart farms
Chapter 5. Smart action
5.1. Implementation of variable rate application
5.2. Smart collaborative robotics and CPS for smart agriculture
Chapter 6. Economic, environmental and social impacts
6.1. Introduction to economic, environmental and social impacts of smart farming
Chapter 7. Precision farming and IoT case studies across the world
Subchapter 7.1. France – The digital Mediterranean farm in the south of France: a model farm to facilitate the appropriation of precision farming tools and methods for wine growers and advisors
Subchapter 7.2. Greece – precision agriculture in Greece
Subchapter 7.3. Italy – nitrogen fertilization based on prescription maps and on-the-go variable rate crop sensors in northern Italy maize cultivation
Subchapter 7.4. Georgia, USA – smart irrigation in Georgia, USA. A case study on cotton
Subchapter 7.5. Argentina – evolution of precision agriculture in Argentina for the last 20 years
Subchapter 7.6. Tanzania – smart agro-farming in Africa
Subchapter 7.7. Japan – smart agriculture in Japan
Subject Index
Author Index
Annamaria Castrignanó is a research director at the Council for Agricultural Research and Economics in Bari (Italy). She has been actively involved in the discipline of pedometrics and digital soil mapping for many years. Her knowledge and expertise in geostatistics and statistics has been exemplified in several oral presentations in International conferences and peer-refereed journal articles (she has published more than 250 papers). She has been the scientific leader of national and international projects aimed at the application of precision farming to the cultivation of durum wheat and tomato in Southern Italy and at the use of proximal and remote sensing in agriculture. She has been giving several basic and advanced courses on geostatistics to national and international PhD students and researchers in Italy and abroad. She is currently involved in the implementation of multivariate geostatistical techniques of data fusion with proximal and remote sensors.
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