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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Non-invasive detection of aflatoxin-contaminated figs using fluorescence and multispectral imaging


Agricultural products are prone to aflatoxin (AF)-producing moulds (Aspergillus flavus, A. parasiticus) during harvesting, drying, processing and also storage. AF is a mycotoxin that may cause liver cancer when consumed in amounts higher than allowed limits. Figs, like other agricultural products, are mostly affected by AF-producing moulds and these moulds usually produce kojic acid together with AF. Kojic acid is a fluorescent compound and exhibiting bright greenish yellow fluorescence (BGYF) under ultraviolet (UV) light. Using this fluorescence property, fig-processing plants manually select and remove the BGYF+ figs to reduce the AF level of the processed figs. Although manual selection is based on subjective criteria and strongly depends on the expertise level of the workers, it is known as the most effective way of removing AF-contaminated samples. However, during manual selection, workers are exposed to UV radiation and this brings skin health problems. In this study, we individually investigated the figs to measure their fluorescence level, surface mould concentration and AF levels and noted a strong correlation between mould concentration and BGYF and AF, and BGYF and surface. In addition to a pairwise correlation, we proposed a machine-vision and machine-learning approach to detect the AF-contaminated figs using their multispectral images under UV light. The figs were classified in two different approaches considering their surface mould and AF level with error rates of 9.38% and 11.98%, respectively.

  • TR
  • Suleyman_Demirel_Univ (TR)
Data keywords
  • machine learning
Agriculture keywords
  • agriculture
Data topic
  • modeling
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.