e-infrastructure Roadmap for Open Science in Agriculture

A bibliometric study

The e-ROSA project seeks to build a shared vision of a future sustainable e-infrastructure for research and education in agriculture in order to promote Open Science in this field and as such contribute to addressing related societal challenges. In order to achieve this goal, e-ROSA’s first objective is to bring together the relevant scientific communities and stakeholders and engage them in the process of coelaboration of an ambitious, practical roadmap that provides the basis for the design and implementation of such an e-infrastructure in the years to come.

This website highlights the results of a bibliometric analysis conducted at a global scale in order to identify key scientists and associated research performing organisations (e.g. public research institutes, universities, Research & Development departments of private companies) that work in the field of agricultural data sources and services. If you have any comment or feedback on the bibliometric study, please use the online form.

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Using high resolution CIR imagery in the classification of non-cropped areas in agricultural landscapes in the UK


With global food demand on course to double in the next 50 years the pressures of agricultural intensification on ecosystem services in highly managed landscapes are increasing. Within an agricultural landscape non-cropped areas are a key component of ecological heterogeneity and the sustainability of ecosystem services. Management of the landscape for both production of food and ecosystem services requires configuring the non-cropped areas in an optimal way, which, in turn requires large scale information on the distribution of non-cropped areas. In this study the Canny edge detection algorithm was used to delineate 93% of all boundaries within 422 ha of agricultural land in south east England. The resulting image was used in conjunction with vegetation indices derived from Color Infra Red (CIR) aerial photography and auxiliary landuse data in an Object Orientated (OO) Knowledge Based Classifier (KBC) to identify non-cropped areas. An overall accuracy of 94.27% (Kappa 0.91) for the KBC compared favorably with 63.04% (Kappa 0.55) for a pixel based hybrid classifier of the same area.

  • GB
  • Univ_Leeds (UK)
Data keywords
  • knowledge
  • knowledge based
Agriculture keywords
  • agriculture
Data topic
  • modeling
  • semantics
Document type

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Institutions 10 co-publis
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    e-ROSA - e-infrastructure Roadmap for Open Science in Agriculture has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 730988.
    Disclaimer: The sole responsibility of the material published in this website lies with the authors. The European Union is not responsible for any use that may be made of the information contained therein.