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
Bovines muzzle classification is considered as a biometric classifier to maintain the safety of bovines and guarantee the livestock products. This paper presents two different bovines classifications models using Artificial Neural Network (ANN) and K-Nearest Neighbor Classifier (KNN). The proposed ANN model consists of three phases; pre-processing, feature extraction and classifications. Pre-processing techniques; histogram equalization and mathematical morphology filtering has been used. The ANN model use Segmentation-based Fractal Texture Analysis (SFTA) for extract muzzle features. The proposed KNN model consists of two phases; Expectation Maximization image segmentation and classification. Expectation Maximization image segmentation (EM) depends on extracts bovine image color and texture feature extraction. The experimental result evaluation proves the advancement of KNN model than ANN as it achieves 100% classification accuracy in case of increase number of classification groups to twenty-five compared to 92.76% classification accuracy achieved from ANN classification model. (C) 2015 Published by Elsevier B.V.
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