e-infrastructure Roadmap for Open Science in Agriculture

A bibliometric study

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.

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Title

Convex-Hull & DBSCAN Clustering to Predict Future Weather

en
Abstract

Machine learning methods are increasingly being used in conjunction with conventional meteorological observations in the synoptic analysis and conventional weather forecast to extract information of relevance for agriculture and food security of the human society in India. Density based clustering approach is incrementally used to predict the future weather conditions in this paper. One famous preprocessing approach, known as Convex-Hull is also used before fed the pollutant data into the clustering algorithm. This Convex-Hull method is strictly used to convert unstructured data into its corresponding structured form. These structured data is efficiently and effectively used by the DBSCAN clustering algorithm to form resultant clusters for weather derivatives. This forecasting database is totally based on the weather of Kolkata city in west Bengal and this forecasting methodology is developed to mitigating the impacts of air pollutions and launch focused modeling computations for prediction and forecasts of weather events. Here accuracy of this approach is also measured.

en
Year
2015
en
Country
  • IN
Organization
    Data keywords
    • machine learning
    en
    Agriculture keywords
    • agriculture
    en
    Data topic
    • modeling
    en
    SO
    2015 INTERNATIONAL CONFERENCE AND WORKSHOP ON COMPUTING AND COMMUNICATION (IEMCON)
    Document type

    Inappropriate format for Document type, expected simple value but got array, please use list format

    Institutions 10 co-publis
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      e-ROSA - e-infrastructure Roadmap for Open Science in Agriculture has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 730988.
      Disclaimer: The sole responsibility of the material published in this website lies with the authors. The European Union is not responsible for any use that may be made of the information contained therein.