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
IMAGE ANALYSIS FOR CHARACTERIZATION THE ACTIVITIES OF DAIRY COWS INSIDE THE CONFINEMENT HOUSING
Today's animal production need the use of information technology and automation in the animal's rearing environment in order to analyze their interference both in the production and animal welfare. The objective of this research was to compare the efficiency of two methodologies of image analysis for evaluating the presence of dairy cows in specific places in the confinement shed. The experiment was done in a commercial farm and the cows were monitored by six cameras. Two ways of analyzing the presence of the cows in the pre- selected places (bedding, drinker and feeder) were used: images analyzed as seen in the computer screen (T1), and automatically by the software developed for capturing the images of the cows in the freestall shed (T2). The attendance data were analyzed using the technique of the principal component analysis and to compare the two methods, it was applied the Student t - test with 95% reliability for the mean count of the presence of cows in each studied area. No significant difference between the methods was found, and both methods were efficient for registering the presence of cows in the activities.
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