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
During the last ten years, automatic guidance systems have become more common in agricultural vehicles. However, users of auto-guided systems can be confused by the growing variety of options commercially available, as well as by the guidance accuracies advertised by different manufacturers. This work proposes an algorithm to evaluate the performance of auto-steered machines for any kind of vehicle and any type of guidances system in the general case of straight row and curved row guidance. The core of the algorithm is based on the comparison of two trajectories: reference course and actual path. The algorithm searches in a neighboring area for the reference points and calculates the deviations. Statistical analysis of the errors provides quantitative information to evaluate the behavior of auto-steered vehicles. The advantage of this technique rests on its independence of regression methods and its immunity to outliers. This methodology was applied to an automatically guided self-propelled forage harvester at both low-speed and high-speed guidance. Results are presented, and when compared to those obtained applying conventional linear regression, they show a slighter impact of outliers and a more efficient procedure and data management.
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