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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Identifying biomass burned patches of agriculture residue using satellite remote sensing data


The combine harvesting technology which has become common in the rice-wheat system in India leaves behind large quantities of straw in the field for open residue burning, and Punjab is one such region where this is regularly happening. This becomes a source for the emission of trace gases, resulting in perturbations to regional atmospheric chemistry. The study attempts to estimate district-wise burned area from agriculture residue burning. The feasibility of using low resolution (MODIS) and moderate resolution (AWiFS) satellite data for estimation of burned areas is shown. It utilizes thermal channels of MODIS and knowledge-based approach for AWiFS data for burned area estimation. A hybrid contextual test-fire detection and tentative-fire detection algorithm for satellite thermal images has been followed to identify the fire pixels over the region. The algorithm essentially treats fire pixels as anomalies in images and can be considered a special case of the more general clutter or background suppression problem. It utilizes the local background around a potential fire pixel, and discriminates fire pixels and avoids the false alarm. It incorporates the statistical properties of individual bands and requires the manual setting of multiple thresholds. Also, a decision-tree classification based on See5 algorithm is applied to AWiFS data. When combined with image classification using a machine learning decision tree (See5) classification, it gives high accuracy. The study compares the estimated burned area over the region using the two algorithms.

  • IN
  • Jawaharlal_Nehru_Univ_JNU (IN)
  • ISRO_Indian_Space_Res_Org (IN)
Data keywords
  • knowledge
  • knowledge based
  • machine learning
Agriculture keywords
  • agriculture
Data topic
  • modeling
  • semantics
  • sensors
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.