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
An approach to generating task-specific computer vision systems from generic components using machine learning is presented. With this system, it is possible to learn both feature segmentation and classification from training data. This approach is applied to a disparate range of problems in the domain of agricultural produce grading: mango surface inspection and maturity evaluation, apple variety discrimination, wheat and barley classification and purple sticky rice grading. It is shown that shape, colour and texture features together produce more accurate classification results than fewer categories of feature, and that these evolved classifiers are competitive with neural networks and support vector machines.
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