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
An Extensible and Integrated Software Architecture for Data Analysis and Visualization in Precision Agriculture
Recent technology advances in information technology and other engineering fields provide new opportunities for research and practices in precision agriculture. Using these technologies, field operators can collect voluminous data from a heterogeneous network of devices that provides real-time and multiple-factor measurement of field conditions with much finer granularity. A major challenge in precision agriculture today is how to analyze these data efficiently and use them effectively to improve farming decisions. We propose an extensible and integrated software architecture for data analysis and visualization in precision agriculture, with three distinctive features: (a) a meta-data-model-based data importation component capable of importing data in various formats from a variety of devices in different settings; (b) a data-flow-driven data processing subsystem in which a user can define his/her own data processing workflows and add custom-defined data processing operators for a specific application; (c) an overall architecture design following a client-server model that supports a variety of client devices, including mobile devices such as the Apple iPad. We implemented the software architecture in an open-source decision_support tool for precision agriculture. The tool has been successfully used in a USDA-sponsored project on canopy management for specialty crops.
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