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
Agriculture is one of the major sources of income in developing countries like India. Pests are one of the main sources for the degradation of quality and quantity of the major crops such as Rice and Wheat. The lack of knowledge about technical & scientific methods to prevent pest diseases is the main reason for less production of these commodities. This paper presents an architectural framework of an agriculture Expert System and describes the design and development of the rule based expert system for rice and wheat crop pest management. The designed system is intended for the diagnosis of diseases caused by pests in the rice & wheat plants respectively and it also facilitates different components including decision_support module with interactive console base user interface for diagnosis on the basis of response(s) of the user made against the queries related to particular disease symp-toms. This paper provides a new approach for knowledge representation in expert systems for agricultural domain. The Explanation block (EB) of the system provides the explanation for a particular decision taken by the system. Explanation block gives the clear view of logic followed by kernel of the expert system.
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