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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Compartive Data Fusion between Genetic Programing and Nueral Network Models for Remote Sensing Images of Water Quality Monitoring


Historically, algal blooms have proliferated throughout Western Lake Erie as a result of eutrophic conditions caused by urban growth and agricultural activities. Of great concern is the blue-green algae Microcystis that thrives in eutrophic conditions and generates microcystin, a powerful hepatotoxin. Microcystin poses a threat to the delicate ecosystem of Lake Erie, and it threatens commercial fishing operations and water treatment plants using the lake as a water source. Integrated Data Fusion and Machine-learning (IDFM) is an early warning system proposed by this paper for the prediction of microcystin concentrations and distribution by measuring the surface reflectance of the water body using satellite sensors. The fine spatial resolution of Landsat is fused with the high temporal resolution of MODIS to create a synthetic image possessing both high temporal and spatial resolution. As a demonstration, the spatiotemporal distribution of microcystin within western Lake Erie is reconstructed using the band data from the fused products and applied machine-learning techniques. The performance of Artificial Neural Networks (ANN) and Genetic Programming (GP) are compared and tested against traditional two-band model regression techniques. It was found that the GP model performed slightly better at predicting microcystin with an R-2 value of 0.6020 compared to 0.5277 for ANN.

  • US
  • Univ_Cent_Florida (US)
Data keywords
  • machine learning
Agriculture keywords
  • agriculture
Data topic
  • big data
  • modeling
  • 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.