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
This paper gives an idea about how to discover additional insights from precision agriculture data through big data approach. We present a scenario for the use of Information and Communication Technology (ICT) services in agricultural big data environment to collect huge data. Big data analytics in agriculture applications provide a new insight to give advance weather decisions, improve yield productivity and avoid unnecessary cost related to harvesting, use of pesticide and fertilizers. Paper list out the different sources of big data in precision agriculture using ICT components and types of structured and unstructured data. Also discussed big data in precision agriculture, an ICT scenario for agricultural big data, platform, its future applications and challenges in precision agriculture. Finally, we have discussed results using a programming model and distributed algorithm for data processing and forecasting application of weather.
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