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


An automated soil line identification method using relevance vector machine


The soil line is an important concept that describes the linear relationship between reflectance of bare soils in the near-infrared (NIR) and red (R) spectral bands. Bare soil line parameters (slope and intercept) are used in calculating several vegetation indices. Previous studies have proposed both manual and empirical procedures in estimating the bare soil parameters. Manual procedures introduce some amount of subjectivity in identifying the soil line. Empirical methods often suffer because of variations caused by soil type, moisture, and organic matter contents. The existence of non-bare soil pixels also affects these procedures. In this study, we proposed an automated supervised learning algorithm using relevance vector machine (RVM) for extracting the soil line from Landsat images. The 10-fold cross validation (10-fold CV) indicated 92% accuracy for distinguishing bare soil and other non-bare soil pixels from an image. The area under the receiver operating characteristic (ROC) curve reached a value of 0.98, indicating a significant predicting power of the proposed procedure. Additionally, this procedure was evaluated using data from 10 bare soil fields in the Texas High Plains region in 2008 and 2009. Statistical analysis indicated no significant difference between the observed and estimated bare soil line parameters. The proposed RVM-based procedure successfully incorporated machine-learning algorithms into agricultural remote sensing and eliminated the dependency on empiricism and minimized subjectivity.

  • US
  • Texas_A&M_AgriLife_Res (US)
  • Texas_Tech_Univ (US)
  • Middle_Tennessee_State_Univ (US)
Data keywords
  • machine learning
Agriculture keywords
  • agriculture
Data topic
  • big data
  • information systems
  • modeling
  • sensors
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.