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

  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

  Autorentext

  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.
  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.

  Klappentext

  Section 1 Data Technologies: You Got Data.... Now What: Building the Right Solution for the Problem (Jackman).- Data fusion and its applications in Agriculture (Moshou).- Machine learning technology and its current implementation in agriculture (Anagnostis).- Section 2 Applications: Application possibilities of IoT based management systems in agriculture (Tóth).- Plant species detection using image processing and deep learning: A mobile-based application (Mangina).- Computer vision-based detection and tracking in the olive sorting pipeline (Gogos).- Integrating spatial with qualitative data to monitor land use intensity: evidence from arable land - animal husbandry systems (Vasilakos).- Air drill seeder distributor head evaluation: a comparison between laboratory tests and Computational Fluid Dynamics simulations (R. Scola).- Section 3 Value Chain: Data - based agricultural business continuity management policies (Podaras).- Soybean price trend forecast using deep learning techniques based on prices and text sentiments (F. Silva).- Use of unsupervised machine learning for agricultural supply chain data labeling (F. Silva).

  Inhalt
  Section 1 Data Technologies: You Got Data. Now What: Building the Right Solution for the Problem (Jackman).- Data fusion and its applications in Agriculture (Moshou).- Machine learning technology and its current implementation in agriculture (Anagnostis).- Section 2 Applications: Application possibilities of IoT based management systems in agriculture (Tóth).- Plant species detection using image processing and deep learning: A mobile-based application (Mangina).- Computer vision-based detection and tracking in the olive sorting pipeline (Gogos).- Integrating spatial with qualitative data to monitor land use intensity: evidence from arable land animal husbandry systems (Vasilakos).- Air drill seeder distributor head evaluation: a comparison between laboratory tests and Computational Fluid Dynamics simulations (R. Scola).- Section 3 Value Chain: Data - based agricultural business continuity management policies (Podaras).- Soybean price trend forecast using deep learning techniques based on prices and text sentiments (F. Silva).- Use of unsupervised machine learning for agricultural supply chain data labeling (F. Silva).