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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Automated zone-specific irrigation with wireless sensor/actuator network and adaptable decision_support


Precision irrigation based on the "speaking plant" approach can save water and maximize crop yield, but implementing irrigation control can be challenging in system integration and decision making. In this paper we describe the design of an adaptable decision_support system and its integration with a wireless sensor/actuator network (WSAN) to implement autonomous closed-loop zone-specific irrigation. Using an ontology for defining the application logic emphasizes system flexibility and adaptability and supports the application of automatic inferential and validation mechanisms. Furthermore, a machine learning process has been applied for inducing new rules by analyzing logged datasets for extracting new knowledge and extending the system ontology in order to cope, for example, with a sensor type failure or to improve the accuracy of a plant state diagnosis. A deployment of the system is presented for zone specific irrigation control in a greenhouse setting. Evaluation of the developed system was performed in terms of derivation of new rules by the machine learning process, WSN performance and mote lifetime. The effectiveness of the developed system was validated by comparing its agronomic performance to traditional agricultural practices. (C) 2014 Elsevier B.V. All rights reserved.

  • GR
  • IE
  • Univ_Aegean (GR)
  • Tyndall_Natl_Inst (IE)
Data keywords
  • machine learning
  • knowledge
  • ontology
Agriculture keywords
  • agriculture
Data topic
  • big data
  • modeling
  • decision support
  • 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.