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 assessments of soil drying for agricultural planning


The hydrologic processes of wetting and drying play a crucial role in agricultural activities involving heavy equipment on unpaved terrain. When soil conditions moisten, equipment can become mired, causing expensive delays. While experienced users may assess soil conditions before entering off-road areas, novice users or those who must remotely assess sites before traveling may have difficulty assessing conditions reliably. One means of assessing dryness is remotely-monitored in situ sensors. Unfortunately, land owners hesitate to place sensors due to monetary costs, complexity, and sometimes infeasibility of physical visits to remote locations. This work addresses these limitations by modeling the wetting/drying process through machine learning algorithms fed by hydrologic data - remotely assessing soil conditions using only publicly-accessible information. Classification trees, k-nearest-neighbors, and boosted perceptrons deliver statistical soil dryness estimates at a site located in Urbana, IL The k-nearest-neighbor and boosted perceptron algorithms both performed with 91-94% accuracy, with most misclassifications falling within calculated margins of error. These analyses demonstrate that reasonably accurate predictions of current soil conditions are possible with only precipitation and potential evaporation data. These two values are measured throughout the continental United States and are likely to be available globally from satellite sensors in the near future. Through this type of approach, agricultural management decisions can be enabled remotely, without the time and expense of on-site visitations or extensive ground-based sensory grids. (C) 2014 Elsevier B.V. All rights reserved.

  • US
  • Univ_Illinois_Urbana_Champaign (US)
  • John_Deere_GmbH_&_Co (US)
Data keywords
  • machine learning
Agriculture keywords
  • agriculture
Data topic
  • big data
  • modeling
  • decision support
Document type

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

Institutions 10 co-publis
  • Univ_Illinois_Urbana_Champaign (US)
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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.