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
A plot-based approach is proposed to detect fruit trees from high spatial resolution aerial images and extract tree and plot-based parameters, such as fraction of tree cover, number of trees, and planting patterns. Each plot image, defined by the boundaries or polygons obtained from a cadastral database, is analyzed independently. The methodology is based on image processing methods: an unsupervised classification with the k-means algorithm is applied, followed by the automatic identification of the classes representing trees. Once extracted, each tree is individualized using a morphological process applied on the binary image of the trees. A set of parameters is calculated at tree and plot levels that produces a comprehensive description of the spectral and morphological aspects of the trees, as well as their spatial distribution in each plot. The methodology was tested on 0.5 m/pixel spatial resolution aerial images of 300 citrus orchard plots which included the three citrus tree typologies found in the Valencia region (Spain). The accuracy of the fruit tree extraction and the parameters calculated was evaluated by comparison with reference data obtained by manual delineation of the images. The automatically extracted fraction of tree cover was significantly related to the reference tree cover area (R-2 = 0.96). In the case of the number of detected trees, the R-2 values were always higher than 0.90 for the three typologies. Tree location was estimated with an average positional error of 40 cm. The error obtained in the characterization of the planting pattern was less than 50 cm. The proposed methodology may be applied to large agricultural databases, and the derived information combined with precision agricultural techniques could improve the efficiency of various irrigation and agricultural management tasks - such as handling per-plot water requirements and distribution. (c) 2012 Elsevier B.V. All rights reserved.
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