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
Shelf life prediction is an important problem in the field of food safety. This problem has been extensively studied in different research fields. In this paper, based on our agricultural JOT (Internet of Things) platform, we study this problem from the viewpoint of data mining. In our agricultural JOT platform, by setting various types of sensors, it is possible for us to collect information of a farm product during its whole life cycle such as planting, storage, processing, transportation and sale. Shelf life of a farm product is very difficult to determine since it will be affected by many factors during its life cycle, such as temperature, air/soil humidity etc. After integrate raw sensor data streams into batch id-based data streams, we adapt Back-Propagation method to the integrated sensor data streams to predict the shelf life of a farm product. Experiments are conducted on real data from our agricultural JOT platform and the experimental results demonstrate that the proposed method could provide a very good prediction for the shelf life of a farm product.
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