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
Localisation and identification performances of a real-time location system based on ultra wide band technology for monitoring and tracking dairy cow behaviour in a semi-open free-stall barn
The objective of this study was to evaluate the localisation and identification performances of a Real-Time Location System (RTLS) based on Ultra Wide Band (UWB) technology within a semi-open free-stall barn since the conditions of the breeding environment were different from that of the 'typical open environment' used by the RTLS producer to test the system and the building characteristics were dissimilar to those of the indoor environments considered in other tests. Each dairy cow was equipped with an active tag applied to one ear and a reference tag was fixed to a pillar of the barn. A video-recording system was installed in the barn to perform the assessment of the RTLS. Top-view camera images of the area of the barn were rectified and synchronised with the RTLS. An operator validated each position of the cow computed by the RTLS by performing cow visual recognition on the camera images. To perform this validation a software specifically designed for the purpose was utilised. It is an automatic and interactive tool which includes selection and control tabs for data management, visualisation and labelling of the images with the aim of computing tag true positions. RTLS localisation and identification performances were assessed by applying an outlier data cleaning technique to tag localisation errors and using precision and sensitivity indices. Trade-off between these performances was found through the computation of three performance metrics. The combination between the outlier data cleaning technique and the trade-off analysis of RTLS performances yielded the localisation mean error that was computed by averaging the localisation errors of each tag. It was equal to about 0.11 m with an identification accuracy of nearly 100% for the reference tag, whereas for the tags applied to the cows the average localisation mean error, computed by averaging the localisation mean errors of the tags, was about 0.515 m with an identification accuracy of 98%. At the 90th percentile the average localisation mean error was about 0.967 m for the cows' tags, whereas it was about 0.17 m for the reference tag. This RTLS could be used for studying some specific aspects of cow behaviour, since its performances would not affect the computation of some cow behavioural indices that do not require a high level of precision on the cow position. In the considered barn environment the RTLS performances proved to be generally unrelated to cow behaviour, as it is observed for other systems. (C) 2014 Elsevier B.V. All rights reserved.
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