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
Integrated Pest Management (IPM) is a comprehensive approach that integrates a variety of practices to minimize the loss of farm productions due to pests and pathogens with optimum use of pesticides. Early detection of pest and its control is one of the aspects of IPM. Weather based forecasting is well accepted method for this. Various meteorological data like-temperature, humidity, leaf wetness duration (LWD) plays the vital roles in the growth of microorganism responsible for disease. Effective forecasting of such diseases on the basis of climate data can help the farmers to take timely actions to restrain the diseases. This can also rationalize the use of pesticides, which are one of the causes behind land pollution. Weather based forecasting system can be considered as a part of the Agricultural decision_support System (ADSS) which is Knowledge Based System (KBS). This paper proposes fuzzy logic based structure for the plant disease forecasting system. It has been demonstrated that the proposed method can be implemented with minimum weather data like-temperature and humidity.
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