Overview
- Discuses variety of knowledge regarding extensive and evolving data management in agricultural systems
- Provides up-to-date information and knowledge on agricultural data management
- Chapter authors are leading experts in the field, providing high-end knowledge
Part of the book series: Springer Optimization and Its Applications (SOIA, volume 183)
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Table of contents (11 chapters)
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Front Matter
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Applications
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Front Matter
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Keywords
- ontologies agriculture
- open data
- image processing agriculture
- data management agriculture
- data mining agriculture
- Internet of Things (IoT)
- smart farming techniques
- knowledge-based agriculture
- web of data
- open data
- image processing agricultural systems
- digitizing agriculture
- applications in agriculture
About this book
The first part of this book (II) focuses on data technologies in relation to agriculture and presents three key points in data management, namely, data collection, data fusion, and their uses in machine learning and artificial intelligent technologies. Part 2 is devoted to the integration of these technologies in agricultural production processes by presenting specific applications in the domain. Part 3 examines the added value of data management within agricultural products value chain.
The book provides an exceptional reference for those researching and working in or adjacent to agricultural production, including engineers in machine learning and AI, operations management, decision analysis, information analysis, to name just a few.
Specific advances covered in the volume:
- Big data management from heterogenous sources
- Data mining within large data sets
- Data fusion and visualization
- IoT based management systems
- Data Knowledge Management for converting data into valuable information
- Metadata and data standards for expanding knowledge through different data platforms
- AI - based image processing for agricultural systems
- Data - based agricultural business
- Machine learning application in agricultural products value chain
Editors and Affiliations
About the editors
Dimitrios Moshou is a Professor at Aristotle University of Thessaloniki and Head of the Agricultural Engineering Lab. His research interests include the theory and applications of bio-inspired information processing, neuroscience, self-organisation, and computational intelligence. He is interested in applications of these techniques in intelligent control, pattern recognition, data fusion and cognitive robotics. Application areas include mechatronics and non-destructive quality control and monitoring of bio-products and crops
Giorgos Vasileiadis works as a Research Assistant in Institute for Bio-economy and Agri-technology (IBO | CERTH). His research interests include product, service, and mixed systems design, mechanization-engineering and production techniques and applications, as well as new technologies assessment in terms of feasibility and adoption levels.
Athanasios Balafoutis is a Researcher at Institute for Bio-economy and Agri-technology (IBO | CERTH). His research interests focus on the development input technologies for the qualitative and quantitative improvement of agricultural production and on the production and use of biomass for energy production to meet energy needs at farm or remote settlement level.
Panos Pardalos is a world leading expert in global and combinatorial optimization. He serves as Distinguished Professor of industrial and systems engineering at the University of Florida. Additionally, he is the Paul and Heidi Brown Preeminent Professor of industrial and systems engineering. He is also an affiliated faculty member of the computer and information science Department, the Hellenic Studies Center, and the biomedical engineering program. He is also the Director of the Center for Applied Optimization.
Bibliographic Information
Book Title: Information and Communication Technologies for Agriculture—Theme II: Data
Editors: Dionysis D. Bochtis, Dimitrios E. Moshou, Giorgos Vasileiadis, Athanasios Balafoutis, Panos M. Pardalos
Series Title: Springer Optimization and Its Applications
DOI: https://doi.org/10.1007/978-3-030-84148-5
Publisher: Springer Cham
eBook Packages: Mathematics and Statistics, Mathematics and Statistics (R0)
Copyright Information: Springer Nature Switzerland AG 2022
Hardcover ISBN: 978-3-030-84147-8Published: 18 March 2022
Softcover ISBN: 978-3-030-84150-8Published: 19 March 2023
eBook ISBN: 978-3-030-84148-5Published: 17 March 2022
Series ISSN: 1931-6828
Series E-ISSN: 1931-6836
Edition Number: 1
Number of Pages: XIV, 288
Number of Illustrations: 24 b/w illustrations, 89 illustrations in colour
Topics: Operations Research, Management Science, Monitoring/Environmental Analysis, Big Data