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
Nowadays. growing interest exists on the integration of artificial in technologies, such as neural networks and fuzzy logic. into Wireless Sensor Networks However, few attentions have been paid to integrate knowledge based systems into such networks The objective of this work is to Optimize the design or a distributed Fuzzy Rule-Based System embedded in Wireless Sensor Networks The proposed system is composed of a central computer. which includes a module to carry out knowledge bases edition. redundant rules reduction and transformation of knowledge bases with linguistic labels in others without labels, access point. sensor network, communication protocol, and Fuzzy Rule-Based Systems adapted to be executed in a sensor Results have shown that, starting from knowledge bases generated by a human expert. it is possible to obtain an optimized one with a design of rules adapted to the problem, and a reduction in number of rules without a substantial decrease in accuracy Results have shown that the use of optimized knowledge bases increases the sensor performance, decreasing then run time and battery consumption To illustrate these results. the proposed methodology has been applied to model the behavior of agriculture plagues
Inappropriate format for Document type, expected simple value but got array, please use list format