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
Weather plays an important role in agriculture. This calls for reliable weather information, which in turn helps farmers make management decisions about their crops. In this paper, we propose an intelligent Computational model for Agro-MEteoroLogical data (CAMEL). The model serves three purposes. First, it effectively captures important information about large amounts of data collected from various weather stations distributed in a wide geographic expanse. Second, the proposed model learns from historical data and predicts future trends. This helps us obtain accurate weather forecasts. Third, through the prediction of weather trends, CAMEL gives us a better understanding of agro-meteorological data. When we compare the predicted results with the observed data, any significant difference between them may be an indication of equipment malfunction or other problems. In this way, CAMEL helps us detect abnormal data and facilitates in guarding against potential sources of error. Consequently, well-functioning equipment and accurate weather data help farmers make wise crop management decisions. Experimental results on real-life datasets show the effectiveness of our proposed intelligent computational model for agro-meteorological data.
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