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
Information technologies are becoming more and more important for agricultural development. In the intelligent monitoring and control of agriculture environments, localization has many attractive applications. On the basis of IEEE 802.15.4a, we adopt the farm channel model CM7 to position in ultra-wideband (UWB) sensor networks for agriculture environments. Maximum likelihood estimation (MLE) based UWB RSSI ranging is proposed, the relation between localization error and ranging error is analyzed. Then, we joint impulse radio UWB, RSSI ranging and localization algorithm, passive UWB RFID Tags to provide a novel UWB sensor localization network in agriculture environments. Simulation results indicate that our ranging method, localization algorithm and network model achieve a significant improvement in the performance of ranging and localization for applications in precision agriculture.
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