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
Developing Mobile Intelligent System For Cattle Disease Diagnosis and First Aid Action Suggestion
Animal husbandry is one of main concerns of agricultural development revitalization in Indonesia. The domestic products from this sector are yet to meet the domestics' demands of meat and dairy products. Therefore, instead of continuously dependent on imported products, efforts on animal husbandry revitalization to stimulate the production growth from this sector are critically needed. The aim of this paper is to present the work of developing mobile intelligent system for cattle diseases diagnosis and first aid action suggestion system. The core intelligent engine of the system is developed using fuzzy neural network. In the sense of ubiquity of smartphones, the user interface is developed as mobile application under Android operating system. System testing over real-world cattle diagnosis medical data set and expert verification showed that the systems could diagnose correctly with validity 100% and average accuracy 96.37%. The experimental results also showed that frame base knowledge representation outperformed rule base knowledge representation.
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