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
Currently, the grading of arecanuts is done manually by trained experts, and no work has been attempted towards an automated classification. This paper discusses an automated technique for the classification of arecanuts, based on their texture. In our previous work [1], we made a classification using the Mean Around features, Gray Level Co-occurrence Matrix (GLCM) features, and a combination of both (Mean-around and GLCM). In this paper, we formulate different tree splitting rules and discuss the comparative results. The decision tree classifiers have been used for the classification of arecanuts into six classes. The splitting rules used for constructing the decision tree are the Gini Diversity Index (GDI), Twoing and Entropy. The results obtained from the proposed method are in good agreement with the observations of the agricultural experts, and the solutions proposed have been well received by them. The experiments on the proposed approach were carried out on a dataset of size 2214 using cross validation, with a good success rate.
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