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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Basic Study of Automated Diagnosis of Viral Plant Diseases Using Convolutional Neural Networks


Detecting plant diseases is usually difficult without an experts' knowledge. Therefore, fast and accurate automated diagnostic methods are highly desired in agricultural fields. Several studies on automated plant disease diagnosis have been conducted using machine learning methods. However, with these methods, it can be difficult to detect regions of interest, (ROIs) and to design and implement efficient parameters. In this study, we present a novel plant disease detection system based on convolutional neural networks (CNN). Using only training images, CNN can automatically acquire the requisite features for classification, and achieve high classification performance. We used a total of 800 cucumber leaf images to train CNN using our innovative techniques. Under the 4-fold cross-validation strategy, the proposed CNN-based system (which also extends the training dataset by generating additional images) achieves an average accuracy of 94.9% in classifying cucumbers into two typical disease classes and a non-diseased class.

  • JP
  • Hosei_Univ (JP)
Data keywords
  • knowledge
  • machine learning
Agriculture keywords
  • agriculture
Data topic
  • 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.