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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Title

Detecting Symptoms of Diseases in Poultry through Audio Signal Processing

en
Abstract

We developed an audio signal processing algorithm that detects rales (gurgling noises that are a distinct symptom of common respiratory diseases in poultry). We derived features from the audio by calculating mel frequency cepstral coefficients (MFCCs), clustering the MFCC vectors, and examining the distribution of cluster indices over a window of time. The features are classified with a C4.5 decision tree. Our training data consisted of eight minutes of manually labeled audio selected from 25 days of continuous recording from a controlled study. The experiment group was challenged with the infectious bronchitis virus and became sick, while the control group remained healthy. We tested the algorithm on the entire dataset and obtained results that match the course of the disease. Algorithms such as this could be used to continuously monitor chickens in commercial poultry farms, providing an early warning system that could significantly reduce the costs incurred from disease.

en
Year
2014
en
Country
  • US
Organization
  • Univ_Georgia (US)
  • Georgia_Inst_Technol (US)
Data keywords
  • machine learning
en
Agriculture keywords
  • farm
en
Data topic
  • big data
  • modeling
  • sensors
en
SO
2014 IEEE GLOBAL CONFERENCE ON SIGNAL AND INFORMATION PROCESSING (GLOBALSIP)
Document type

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Institutions 10 co-publis
  • Univ_Georgia (US)
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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.