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
Comparative assessment of feature selection and classification techniques for visual inspection of pot plant seedlings
Homogeneity plays an important role in ornamental plant and flower production. As assessing the quality of seedlings is an effective way of predicting plant growth performance, a vision system capable of performing this task is desirable. Yet, the optical sorting of agricultural products must find ways to incorporate knowledge from human experts into the computational solution. Our aim is evaluating feature selection techniques with respect to the performance of vision-based inspection and classification of pot plant seedlings. A large feature set was initially obtained from seedlings images and several subsets were generated with various features selection techniques. The performance of each subset was compared to some of the most popular classifiers in the literature: Naive Bayes, k-Nearest Neighbors, Logistic Regression, C4.5, Random Forest, Multilayer Perceptron as well as Partial Least Squares and Support Vector Machine Discriminant Analysis. The best classifier and subset configuration is presented; our results show that feature selection was indeed advantageous, generating accuracy gains of up to 7.4%. (c) 2013 Elsevier B.V. All rights reserved.
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