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
From the last few years, more attention has been directed towards the usage of information technology in agriculture. This new way of farming offers the promise of improving farm profitability. Using Internet the farmers can collect data like geographical- referred yield, weather, soil and other important data related to farming. The aim is to use these data to produce area-specific crop production decisions. For increasing the production quality of crop soil plays very important role. To help the farmer in deciding how to increase the crop quality based on soil. We have proposed, soil image recognizer and classifier, which classifies soil image samples based in their color and morphological features. Different types of soil image samples considered like red soil, black soil, black cotton soil. Using color and morphological features a Neural Network Based Classifier is designed. The effect of water on the soil image classifier is analyzed by adding the water into different portions of soil samples. The accuracy of the soil image classifier is improved by considering wet soil samples.
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