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
We present our early work in the area of voice-based information retrieval in an agricultural information system. The specific scenario deals with an information system that has the audio content in a local language, Kannada. End-users, mostly being low-literate farmers make phone calls to browse information about their crops. Since the crops are large in number, and audio being a sequential modality, this browsing process is often inefficient. We therefore propose to build a search system through which users could speak keywords, which we foresee to be crop names, pesticide names, insect names, village names and such. The challenge lies in retrieving the right audio information that matches the user query. The specific use case is helped by the fact that the content is available in its textual format. In this paper, we present a data-model that can be used for structuring the textual content to develop the index for efficient and improved retrieval through audio queries. We present the details of the scenario and explain the data and its nuances and then propose a data model to effectively utilize the structure for efficient information retrieval in this specific context. We believe that the proposed approach can have implications for other languages and other agriculture based information systems.
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