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
Soil water content prediction is essential to the development of advanced agriculture information systems. Because soil water content series are inherently noise and non-stationary, it is difficult to get an accurate forecasting result. Considering the problems, in this paper, a novel hybrid learning architecture is proposed according to divide-and-conquer principle, the forecasting accuracy is improved. This novel hierarchical architecture is composed of ANN (Kohonen neural network) and SVM (support vector machine). The Kohonen network is used as a classifier, which partitions the whole input space into several distinct feature regions. Then, the best SVM predictor combined with an appropriate kernel function can be achieved for correspondence regions. The experimental results based on the hybrid model exhibit good agreement with actual soil water content measurements and outperform ANN and SVM single-stage models.
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