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
Tobacco quality classification plays a significant role in its market price determination. Conventional methods including linear discriminant analysis, K-means clustering and BP-neural network flaw in capture the nonlinear structure. The use of support vector machine (SVM) has been shown to be a cost-effective technique, But it is used as a non-preprocessing way for a classification task. This paper extended SVM with kernel principal component analysis (KPCA) for extract valuable discriminatory information. The method is then applied to classify tobacco leaves quality of the Wulong country, one of the most important tobacco planting areas of Chongqing. The classification performance of the proposed method is proven superior compared with other statistical and machine learning methods.
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