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.

You can access and play with the graphs:

Discover all records
Home page


A novel hyperspectral segmentation algorithm-concept and evaluation


Nowadays, the hyperspectral imaging is the focus of intense research, because its applications can be very useful in the natural disaster monitoring and agricultural monitoring to enumerate only a few. The main problem of systems using hyperspectral imaging is the cost of labelling, because it requires the domain experts, who label the region or prepare the labelled learning set for machine learning methods. The article presents a novel Hyperspectral Segmentation Algorithm which is a part of a general framework used for image classification. The algorithm is based on an image decomposition into homogeneous regions using a novel similarity measure. Three different region representations are proposed using the matrix notation. An additional procedure merges similar regions into larger ones to reduce human expert engagement in region labelling. The algorithm has been evaluated on the number of benchmark datasets to investigate the influence of algorithm parameters on the final performance. Comparison with competing methods proved that the considered algorithm is an interesting proposition in hyperspectral image analysis tasks.

  • PL
  • ES
  • CZ
  • Univ_Pais_Vasco_UPV_EHU (ES)
  • Wroclaw_Univ_Sci_&_Technol (PL)
  • Tech_Univ_Ostrava_VSB (CZ)
Data keywords
  • machine learning
Agriculture keywords
  • agriculture
Data topic
  • big data
  • knowledge transfer
  • modeling
Document type

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

Institutions 10 co-publis
    Powered by Lodex 8.20.3
    logo commission europeenne
    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.