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
Uncertain Knowledge Representation and Inferential Strategy in the Expert System of Swine Disease Diagnosis
The objective of swine disease diagnosis expert system was aimed at the usage of swine farmers in Thailand for disease diagnosis using symptoms of swine that swine farmers found for diagnosis. Our expert system is useful to swine farmers for the care of ill swine health and decreases the spread of disease in the farm that this leads to swine farmers disadvantage and overall economy. In this research we have established the novel model of uncertain knowledge representation and inference strategy using determination of significant weight of each symptom to disease diagnosis, which is defined by the veterinarian. In each diagnosis, the user must also specify symptom certainty factor, the system provides the pictures and description of symptoms, so that the user can specify the certainty factor correctly. Therefore, our model causes an accurate diagnosis of the expert system of swine disease diagnosis. From the results of diagnosis by our expert system, we found that it could diagnose accurately for 92%, which is satisfied.
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