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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Call recognition to identify cow conditions - A call-recogniser translating calls to text


In the course of evolution, nature evolved manifold means of communication. Sound is one of the most important means to convey information and to express emotional states and conditions. The acoustic monitoring of farm animals may serve as an efficient management tool to enhance animal health, welfare, and farm efficiency. The final goal of this work is a Call-Recogniser, a device that identifies the meaning of vocal utterances of cows and presents the meaning to the farmer. Such a call-recogniser must be able to recognise the meaning of species-specific calls, independent from the individual animal and the probably more or less noisy environment. As in speech recognition, the call recognition of animals can be regarded as a statistical paradigm. During the learning or training phase, feature vectors from known calls are calculated. From the feature vectors of calls with the same meaning, reference patterns are built and stored. For recognition, the feature vectors from an unknown call are calculated in the same way, and the system then determines the reference pattern that is most similar to the feature vector to be recognised, and outputs its meaning. Despite the vocabulary size and complexity of human speech, which is unique in the animal realm, sound production and reception in vertebrates have much in common. This encourages, adaptation of methods and experiences from speech recognition to recognise animal calls. The problem of animal independent call recognition is comparable to speaker independent word spotting in speech recognition. In speech recognition, double stochastic processes, such as hidden Markov models (HMMs), have proved very efficient. They are applied here to recognise animal calls, using utterances of cows as an example. The results reveal that HMMs are well suited for animal call recognition. (c) 2007 Elsevier B.V. All rights reserved.

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    Data keywords
    • vocabulary
    Agriculture keywords
    • farm
    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.