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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Analysis of convergent evidence in an evidential reasoning knowledge-based classification


The use of knowledge-based systems (KBSs) that use evidential reasoning for land-cover mapping derived from remotely sensed images is spreading widely. In recent years, KBSs utilizing the Dempster-Shafer Theory of Evidence (D-S ToE) have been found most successful in a wide range of remote sensing applications, partly because of their ability to combine diverse information sources. An important feature of the D-S ToE is that it provides a measure for the evidential support (belief) accumulated for each object class at each pixel. Despite the importance of cumulative belief values (CBVs) in representing the weighting of supportive versus conflicting evidence for each class, their analysis has received little attention in the literature. The objective of the present study was to assess the performance (represented by the kappa coefficient) of a KBS based on D-S ToE and of an unsupervised classification (ISODATA), with relation to the CBV distribution determined for each class. This was done for the task of crop recognition in a wide heterogeneous region in Israel. It was found that while KBS performs very well in cases of conflicts and moderate support, the US classification performed well only in cases of homogeneity and uniqueness. Crop recognition by means of KBS was applied to almost one-third of the country's agricultural areas, and it provided a high level of differentiation among seven crop types, orchards and natural vegetation types. (c) 2005 Elsevier Inc. All rights reserved.

  • IL
    Data keywords
    • knowledge
    • knowledge based
    • reasoning
    Agriculture keywords
    • agriculture
    Data topic
    • information systems
    • modeling
    • semantics
    Document type

    Inappropriate format for Document type, expected simple value but got array, please use list format

    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.