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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Phenology Monitoring Of Agricultural Plants Using Texture Analysis


Power of computing technology to improve the efficiency in agricultural fields is becoming increasingly more important with the current projections of expected world population growth and decrease in available land and natural resources. One of the critical improvements can be achieved by monitoring phenology of agricultural plants which would henceforth improve the timing for the harvest, pest control, yield prediction, farm monitoring, disaster warning etc. Inferring the phenological information contributes to a better understanding of relationships between productivity, vegetation health and environmental conditions. As part of a government supported project, a terrestrial observation network is built throughout Turkey. The network includes over twelve hundred agro-stations that are placed on many agricultural fields. The stations are equipped with many sensors including cameras that acquire image sequences of the farm fields in a periodic manner. In this study, we use textural analysis combined with machine learning techniques to develop measures in order to recognize and classify phenological stages of several types of plants purely based on the visual data captured every half an hour by cameras mounted on the ground agro-stations. Experimental results suggest that Histogram of Oriented Gradients (HOG) features outperform Gray Level Co-occurrence Matrix (GLCM) features for the discrimination of phenological stages.

  • TR
  • Istanbul_Tech_Univ_ITU (TR)
Data keywords
  • machine learning
Agriculture keywords
  • agriculture
  • farm
Data topic
  • big data
  • sensors
Document type

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

Institutions 10 co-publis
  • Istanbul_Tech_Univ_ITU (TR)
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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.