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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Classification of Data Streams Applied to Insect Recognition: Initial Results


Applications such as intelligent sensors should be able to collect information about the environment and make decisions based on input data. An example is a low-cost sensor able to detect and classify species of insects using a simple laser and machine learning techniques. This sensor is an important step towards the development of intelligent traps able to attract and selectively capture insect species of interest such as disease vectors or agricultural pests, without affecting non-harmful species. The data gathered by the sensor constitutes a data stream with non-stationary characteristics, since the insects' metabolisms are influenced by environmental conditions (such as temperature, humidity and atmospheric pressure), circadian rhythm and age. Algorithms that classify data streams often assume that once a prediction is made, the actual labels are provided to assist in updating the classifier. In the case of intelligent sensors, these labels are rarely available. The objective of this paper is to evaluate methods that adapt concept drifts by regularly updating the classification models applied to insect recognition in a data stream. We show in our initial results that the philosophy of inserting and removing examples from the training set are of essential importance. We also show that a simple criterion to insert examples with high classification confidence can significantly improve the accuracy.

  • BR
  • Univ_Sao_Paulo_USP (BR)
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
  • Univ_Sao_Paulo_USP (BR)
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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.