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
In this article, a mission planner of field coverage operations for an autonomous agricultural tractor is presented. Missions for a particular autonomous tractor are defined using an XML (extendible markup language) formatted file that can be uploaded to the tractor through the user interface. Using the tree hierarchy of the mission file, several actions are determined, including the sequence of points the tractor has to follow, the type of motion between successive points (e.g., straight motion or maneuvering), the type of predefined turning routine used in maneuvering, and the actions that should be taken once the tractor reaches the desired point (e.g., raising or lowering the attached tool, turning on or turning off the power take-off). In order to automatically create the XML mission files, a program was developed using the MATLAB technical programming language. The program uses data regarding the field (geometry, dimensions, field sub-regions, working direction, initial and final desired locations of the tractor), the operating width, and the operation type (mowing, spraying) as inputs. The planning method is based on an algorithmic approach where field coverage planning is transformed and formulated, via semantic representations, as a vehicle routing problem (VRP). By using this approach, the total non-working distance can be reduced by up to 50% compared to the conventional non-optimized method. Three sets of experiments are presented. In the first set, three fields were separately covered; in the second set, three neighboring fields were covered as part of a single tractor mission; and in the third set of experiments, a single field was covered during a hypothetical spraying operation for two different locations of the refilling facility.
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