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:
- Evolution of the number of publications between 2005 and 2015
- Map of most publishing countries between 2005 and 2015
- Network of country collaborations
- Network of institutional collaborations (+10 publications)
- Network of keywords relating to data - Link
Bee Trees' Density Estimation based On Satellite Image Analysis: A case study at Al-Baha Region in Saudi Arabia
The increasing demand for Agricultural statistics for the provision of better plantation chances leads to the use of advanced Information technology intelligent techniques. Such techniques would be utilized in order to extract, segment and analyze vegetal areas land cover using multispectral satellite images. In this paper, we present digital image processing-based method to process 0.5 m(2) satellite images of Al-Baha region in Saudi Arabia. Deployment of such an application would lead to extraction of vegetal information that is needed by beekeepers in search of better forage to maintain their colonies; and to produce honey in the region. The segmentation results may be used as key indicator for vegetation density.
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