Description
Book SynopsisThis edited open access book presents the comprehensive outcome of The European DataBio Project, which examined new data-driven methods to shape a bioeconomy. These methods are used to develop new and sustainable ways to use forest, farm and fishery resources. As a European initiative, the goal is to use these new findings to support decision-makers and producers – meaning farmers, land and forest owners and fishermen.
With their 27 pilot projects from 17 countries, the authors examine important sectors and highlight examples where modern data-driven methods were used to increase sustainability. How can farmers, foresters or fishermen use these insights in their daily lives? The authors answer this and other questions for our readers. The first four parts of this book give an overview of the big data technologies relevant for optimal raw material gathering. The next three parts put these technologies into perspective, by showing useable applications from farming, forestry and fishery. The final part of this book gives a summary and a view on the future.
With its broad outlook and variety of topics, this book is an enrichment for students and scientists in bioeconomy, biodiversity and renewable resources.
Table of ContentsTerms, Acronyms and Abbreviations
Foreword (PO or Director Data at European Commission- DG CONNECT)
Introduction to the book and the project & acknowledgements (Editors, Thanasis)
Part I – Technological Foundation: Big Data Technologies for BioIndustries
Chapter 1: State of the art of technology and market potential (Caj Södergård/VTT et al)
Chapter 2: Standards (Ingo Simonis/OGC et al)
Chapter 3 : Sensor Data (Savvas Rogotis/NP et al)
Chapter 4: Geospatial Data (Eva Klien /Fraunhofer et al)
Chapter 5: Crowdsourced Data (Karel Charvat/Lespro)
Chapter 6 : Genomics Data (Ephrem Habyarimana /CREA et al)
Chapter 7: Integrating data sources with Linked Data (Christian Zinke-Wehlmann/Infai et al)
Chapter 8: Linked Data usages in DataBio (Christian Zinke-Wehlmann/Infai et al)
Chapter 9: Data Pipelines: Modelling and Evaluation of models (Kais Chaabouni)/Softeam et al
Chapter 10: Data Analytics and Machine Learning (Pekka Siltanen/VTT et al)
Chapter 11: Real Time Data Processing (Fabiana Fournier/IBM, et al)
Chapter 12: Privacy Preserving Analytics, Processing and Data Management (Baldur Kubo/Cybernetica)
Chapter 13: Data Visualisation (Eva Klien/Fraunhofer et al)
Part II – Applications in Agriculture
Chapter 14: What is Smart Agriculture (Ephrem Habyarimana/CREA, Christian Zinke-Wehlmann/Infai)
Chapter 15: NP’s Smart farming pilots (Savvas Rogotis/NP).
Chapter 16: Big Data assets applied to the calculation of Irrigation needs in large scale Irrigation Communities (Iluminada Sevilla/Tragsa et al)
Chapter 17: Genomics Biomass pilots (Ephrem Habyarimana/CREA et al)
Chapter 18: Yield estimation in Sorghum and Cultivated Potato (Ephrem Habyarimana/CREA, Nicole
Bartelds/NB Advies)
Chapter 19: Yield variability mapping (Karel Charvat, Lespro et al)
Chapter 20: Farm Weather Insurance Assessment (Antonella Catucci/e-Geos).
Chapter 21: Copernicus Data and CAP Subsidies Control (Olimpia Copăcenaru
/Terrasigna et al)
Chapter 22: Future vision, Summary and Outlook
Part III – Applications in Forestry
Chapter 23: State of the art of technology and market potential (Jukka
Miettinen/VTT)
Chapter 24: Finnish Forest Data based Metsään.fi-services (Virpi Stenman/Metsäkeskus)
Chapter 25: Forest variable estimation and change monitoring by Big Data remote
sensing ( Jukka Miettinen/VTT et al)
Chapter 26: Monitoring Forest Health: Big Data applied to diseases and plagues control (María Jose
Checa et al/Tragsa)
Chapter 27: Forest damage monitoring for the bark beetle (Petr Lukeš/ FMI)
Chapter 28: Future Vision, Summary of Big Data in Forestry (Jukka Miettinen/VTT et al)
Part IV – Applications in Fishery
Chapter 29: State of the art of technology and market potential (Karl-Johan Reite /SINTEF et al)
Chapter 30: Tuna Fisheries (Iñaki Quincoces/AZTI et al)
Chapter 31: Small pelagic fisheries (K.G.A/SINTEF et al)
Chapter 32: Future Vision, Summary and Outlook (Josean Fernandes/AZTI et al)
Part V – Summary and Outlook
Chapter 33: Summary of Potential of Big Data Technology
Chapter 34: Outreach, Perspective, Exploitation
Chapter 35: How can I apply Big Data?
