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
Towards Automatic Estimation of the Body Condition Score of Dairy Cattle Using Hand-held Images and Active Shape Models
The Body Condition Score (BCS) is considered a critical value for dairy farms, since its observation can be used to optimize milk production. Usually, the BCS is calculated by human experts after visual inspection in a time-consuming and subjective process. There are already some papers where this process is almost automated using image processing on some kinds of pictures and, in this work, the first steps towards a fully automated method based on pictures taken with common photographic cameras are described. Active Shape Models (ASM) are used to obtain a set of features that describe the back shape of cows and those features feed a classifier that computes the BCS. We show that the BCS can be estimated using only a set of angles from the back view with an error similar to that calculated between scores of two experts. To obtain those angles automatically is the hardest step in this process, but we have already achieved reasonable results on that point too.
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