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
In this work we present a numerical analysis of the performance of a passive search strategy for localizing plant diseases in a crop plantation where the effect of wind is negligible. The method consists of a set of cooperative passive sensors which decode the information contained in the concentration of Volatile Organic Compounds (VOCs) emitted by plants to detect the source of a disease outbreak in a crop field. The sensors work cooperatively and they provide an estimation of the location of the infectious focus using either Bayesian or classical inference rules. We show that the key parameters affecting the efficiency of the localization method are the rate of detection, the number of chemical sensors and their distance from the source. This method is extensible to more general media, and it constitutes a more economic alternative to the traditional image-based methods and active search strategies, which become unfeasible under the presence of obstacles.
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