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
Identification and classification of crops cultivated areas using satellite images is important for making different types of analysis to formulate policies and plan in agricultural and environmental domains. A range of Feature Extraction Techniques (such as Statistical, Texture, DWT and DCT), having a vital role in crop classification, have been employed and analyzed to suggest the best one for classifying crop images from regions of Netherlands and Pakistan. These feature extraction techniques have been evaluated with number of classification techniques like Support Vector Machine, Naive Bayesian, K Nearest Neighbor, Decision Tree and ensemble based classifiers. A rigorous analysis has been carried out to evaluate the effectiveness of different machine learning techniques over variety of feature sets in the regions under study. Data set containing satellite images from cultivated lands of Netherlands and Pakistan have been used for experimentation. DWT and DCT have shown better precision and accuracy in classifying the crop images from Netherlands and Pakistan respectively, compared to rest of the feature extraction techniques.
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