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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Trends in Robotic Sensor Technologies for Fruit Harvesting: 2010-2015


In the modern world processes and technologies tend to be automated, autonomous and precise. The world population is constantly growing and thus food production technologies should be brought to a qualitatively new level. Quality requirements for food products also tend to increase and become more complex. Precision Agriculture provides the possibility to use soil more intelligently and effectively. Precision Agriculture includes sensor technologies for yield mapping and measuring, soil sensing, nutrient and pesticide application, irrigation control, robotic harvesting, etc. With the increase of the density and energy effectiveness of computing power, it has also become possible to use open source libraries to incorporate complex signal processing, object detection and machine learning into embedded applications. These factors have led to a situation where designs of commercially successful robotic plant inspection and harvesting solutions can emerge. This paper provides a review of modern sensor systems used in semi or fully automated robotic harvesting, including fruit detection and localization prior to pick or slice. Sensors used in selective harvesting were also reviewed. Sensor systems were classified in the following categories: computer vision, chemical sensors, tactile sensors and proximity sensors. The main trends in the future of robotic harvesting will involve usage of combinations of different sensor systems that provide accuracy and reliability. (C) 2015 The Authors. Published by Elsevier B.V.

  • LV
  • DK
  • Aarhus_Univ (DK)
Data keywords
  • machine learning
Agriculture keywords
  • agriculture
Data topic
  • sensors
Document type

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

Institutions 10 co-publis
  • Aarhus_Univ (DK)
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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.