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Information and Communication Technologies for Agriculture-Theme II: Data

Artikelnummer: 978-3-030-84147-8
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This volume is the second (II) of four under the main themes of Digitizing Agriculture and Information and Communication Technologies (ICT). The four volumes cover rapidly developing processes including Sensors (I), Data (II), Decision (III), and Actions (IV). Volumes are related to 'digital transformation" within agricultural production and provision systems, and in the context of Smart Farming Technology and Knowledge-based Agriculture. Content spans broadly from data mining and visualization to big data analytics and decision making, alongside with the sustainability aspects stemming from the digital transformation of farming. The four volumes comprise the outcome of the 12th EFITA Congress, also incorporating chapters that originated from select presentations of the Congress.
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

This volume is the second (II) of four under the main themes of Digitizing Agriculture and Information and Communication Technologies (ICT). The four volumes cover rapidly developing processes including Sensors (I), Data (II), Decision (III), and Actions (IV). Volumes are related to 'digital transformation" within agricultural production and provision systems, and in the context of Smart Farming Technology and Knowledge-based Agriculture. Content spans broadly from data mining and visualization to big data analytics and decision making, alongside with the sustainability aspects stemming from the digital transformation of farming. The four volumes comprise the outcome of the 12th EFITA Congress, also incorporating chapters that originated from select presentations of the Congress.
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
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VerlagSpringer
EinbandFester Einband
Erscheinungsjahr2022
Seitenangabe304 S.
AusgabekennzeichenEnglisch
MasseH24.1 cm x B16.0 cm x D2.2 cm 623 g
CoverlagPalgrave Macmillan
ReiheSpringer Optimization and Its Applications
AutorBochtis, Dionysis D. (Hrsg.) / Moshou, Dimitrios E. (Hrsg.) / Vasileiadis, Giorgos (Hrsg.) / Balafoutis, Athanasios (Hrsg.) / Pardalos, Panos M. (Hrsg.)

Alle Bände der Reihe "Springer Optimization and Its Applications"

Über den Autor Dionysis D. (Hrsg.) Bochtis

Dionysis Bochtis works on the field of engineering for agricultural production under enhanced ICT, automation, and robotics technologies. His Research/Academic track-record includes positions such as: Director of the Institute for Bio-economy and Agri-technology (IBO / CERTH); Professor (Agri-Robotics) University of Lincoln, UK, and Senior Scientist (Operations Management), Aarhus University, Denmark. He is the founder of the agri-tech private company: farmB Digital Agriculture. Claus Grøn Sørensen is a Professor at the Department of Electrical and Computer Engineering, University of Aarhus, Head of Operations Management Research Unit and of Centre on Smart Farming. He has extensive experience in production and operations management, decision analysis, information modeling, system analysis, and simulation and modeling of technology application in agriculture, development of management information systems and smart applications.  Multiple international positions held in international organizations, including past President of EurAgEng 2016-2018 (the European Society of Agricultural Engineers (EugAgEng)), and past Chairman for CIGR (International Commission of Agricultural Engineering) Section V on System Management. Spyros Fountas is Associate Professor at Agricultural University of Athens. He holds an MSc from Cranfield University UK in Information Technology, and PhD from Copenhagen University, Denmark in Systems Analysis on Precision Agriculture. He was also Visiting Scholar at Purdue University, USA.  He is Editor-in-Chief in the Elsevier journals of "Computers and Electronics in Agriculture" since 2014 and "Smart Agricultural Technology", since 2021. His field of expertise are precision agriculture and farm management information systems.Vasileios Moysiadis is an Automation Engineer specialized in software development Machine Learning and Big Data Analysis. His research interests also focus on high level control aspects of robotic technologies and unmanned aerial vehicles. Currently he is a Research assistant at the Agri-robotics Lab of the Institute for Bio-economy and Agri-technology (iBO), Centre for Research & Technology Hellas (CERTH). Panos M. Pardalos 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. Professor Pardalos is also an affiliated faculty member of the computer and information science department, the Hellenic Studies Center, and the biomedical engineering program. Additionally, he serves as the director of the Center for Applied Optimization. Professor Pardalos is a world leading expert in global and combinatorial optimization. His recent research interests include network design problems, optimization intelecommunications, ecommerce, data mining, biomedical applications, and massive computing. Panos Pardalos is a prolific author who lectures all over the world. He is the recipient of a multitude of fellowships and awards, the most recent of which is the Humboldt Research Award (2018).

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