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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Machine Learning Methods for Spatial Clustering on Precision Agriculture Data


Machine learning techniques typically result from the need for intelligent solutions to practical tasks. Nowadays, large data volumes are usually involved and machine learning techniques are focused on particular tasks like classification, regression or clustering. For the latter task, clustering, quite a few algorithms have been proposed, typically tailored to particular application domains and their data sets. Recently, georeferenced (or spatial) data sets keep emerging in lots of disciplines. Therefore, algorithms which are able to handle these spatial data sets should be developed. This article shortly describes a particular application area, precision agriculture, and the spatial data sets which exist there. A particular task from this area, management zone delineation, is outlined and existing spatial clustering algorithms are evaluated for this task. Based on the experiences with those algorithms and a few requirements, HACC-SPATIAL is developed. The algorithm is based on hierarchical agglomerative clustering with a spatial constraint and it is demonstrated to produce practically advantageous results on precision agriculture data sets.

  • DE
  • Otto_Von_Guericke_Univ_Magdeburg (DE)
Data keywords
  • machine learning
Agriculture keywords
  • agriculture
Data topic
  • big data
  • modeling
  • sensors
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.