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
Modeling of Environmental Factors for Finding Optimal Conditions on Cultivating Farm Products
Since the number of farmers has been decreasing recently, shortage of the labor force is a serious problem in many farmhouses. In order to solve this problem, it is necessary to realize the system to support farmer's works in low costs. The purpose of our research is to construct the system which can predict the farmland environment in the near future. In this research, we focus on the control of soil wetness and temperature. We formalize a model for expressing the rule for predicting temperature and soil wetness from the latest environmental data of farmhouse. We show that the rule can be generated by the machine learning algorithm ID3. We research the confidence of each prediction by comparing data obtained from the experiment of cultivating farm products using a greenhouse. Based on the result, we research for finding environmental factors which are needed to create the hypothesis for the prediction of the environment transformation.
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