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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Hyperplane algorithm - First step of the paired planes classification procedure


The objective of supervised learning is to estimate unknowns based on labeled training samples. If the unknown to be estimated is categorical or discrete, the problem is one of classification. Algorithms for supervised learning are useful tools in many areas of agriculture, medicine, and engineering, including prediction of malignant cancer, document analysis, and speech recognition. In general, Support Vector Machine algorithms have been successful in classification problems, but they have high computational complexity. In this paper, we present the Hyperplane Algorithm. It and two other related algorithms form an ensemble classifier for supervised classification. The Hyperplane Algorithm is reminiscent of a support vector machine but is low in computational complexity. It also has several other advantages compared to Support Vector Machines. Results for five real-life datasets results are shown.

  • US
  • Univ_Cincinnati_Cincinnati (US)
Data keywords
  • machine learning
Agriculture keywords
  • agriculture
Data topic
  • big data
  • modeling
Proceedings of the IASTED International Conference on Artificial Intelligence and Applications
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.