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.
You can access and play with the graphs:
- Evolution of the number of publications between 2005 and 2015
- Map of most publishing countries between 2005 and 2015
- Network of country collaborations
- Network of institutional collaborations (+10 publications)
- Network of keywords relating to data - Link
Automatic recognition of jaw movements in free-ranging cattle, goats and sheep, using acoustic monitoring
Sensor technologies to quantify the feeding behaviour of free-grazing domesticated herbivores are required. Acoustic monitoring is a promising method, but signal processing algorithms to automatically identify and classify sound-producing jaw movements are not well developed. We present an algorithm for jaw movement identification that is designed to be as general as possible; it requires no calibration and identifies jaw movements according to key features in the time domain that are defined in relative terms. A machine-learning approach is used to separate true jaw-movement sounds from background noise and intense spurious noises. The algorithm software performance was tested in three field studies by comparing its output with that generated by aural sequencing. For cattle grazing green pasture in a low-noise environment with a Lavalier microphone positioned on the forehead, the system achieved 94% correct identification (i.e., aural events matched by software events within a tolerance of 0.2 s) and a false positive rate (i.e., software events not similarly matched by aural events) of 7%. For goats grazing green herbage in an extremely noisy environment, and with a piezoelectric microphone positioned on the horn, the system achieved 96% correct identification and 4% false positives. For sheep grazing dry pasture in an environment characterised by frequent intense noises, and with a piezoelectric microphone positioned on the horn, the system achieved 84% correct identification and 24% false positives. Very low error rates can be obtained from the software if intense extraneous noises can be avoided. (C) 2012 IAgrE. Published by Elsevier Ltd. All rights reserved.
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