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
Object tracking in the Internet of Things (IoT) has become a hot topic over the past ten years. Currently, the integration of video and radio-frequency identification (RFID) technology plays a crucial role in item-level activity recognition. Various techniques and applications have been proposed for visual object tracking. However, identifying semantic features of item-level objects in huge size of video content is a non-trivial task, especially in supply chain management. To alleviate this problem, this paper presents a novel method that applies IoT information to facilitate video summarization. Differing from common video summarization techniques, we use IoT information to select keyframes of the video content during the background model establishment. Then we match other keyframes with the background to extract important features. Finally, a compact summarization image for queried objects is generated according to a clustering analysis. We have also performed experiments to confirm the effectiveness of the proposed work.
Inappropriate format for Document type, expected simple value but got array, please use list format