Logo des Repositoriums
 

Mastitis detection in dairy cows using Neural Networks

dc.contributor.authorKrieter, J.
dc.contributor.authorCavero, D.
dc.contributor.authorHenze, C.
dc.contributor.editorBöttinger, Stefan
dc.contributor.editorTheuvsen, Ludwig
dc.contributor.editorRank, Susanne
dc.contributor.editorMorgenstern, Marlies
dc.date.accessioned2019-05-15T10:40:53Z
dc.date.available2019-05-15T10:40:53Z
dc.date.issued2007
dc.description.abstractThe aim of the present research was to investigate the usefulness of neural networks (NN) in the early detection and control of mastitis in cows milked in an automatic milking system. A data set of 403,537 milkings involving 478 cows was used. Mastitis was determined according to udder treatment and/or somatic cell counts (2). Mastitis alerts were generated by a NN model using electrical conductivity, milk production rate, milk flow rate and days in milk as input data. The evaluation of the model was carried out according to block-sensitivity, specificity and error rate. When the block-sensitivity was set to be at least 80%, the specificities were 51.1% and 74.9% and the error rates were 51.3% and 80.5% for mastitis definitions 1 and 2, respectively. Additionally, the average number of true positive cows per day ranged from 1.2 to 6.4, and the average number of false negative positive cows per day ranged from 5.2 to 6.8 in an average herd size of 24 cows per day for the test data.en
dc.identifier.isbn978-3-88579-195-9
dc.identifier.pissn1617-5468
dc.identifier.urihttps://dl.gi.de/handle/20.500.12116/22870
dc.language.isoen
dc.publisherGesellschaft für Informatik e. V.
dc.relation.ispartofAgrarinformatik im Spannungsfeld zwischen Regionalisierung und globalen Wertschöpfungsketten – Referate der 27. GIL Jahrestagung
dc.relation.ispartofseriesLecture Notes in Informatics (LNI) - Proceedings, Volume P-101
dc.titleMastitis detection in dairy cows using Neural Networksen
dc.typeText/Conference Paper
gi.citation.endPage125
gi.citation.publisherPlaceBonn
gi.citation.startPage123
gi.conference.date05.-07. März 2007
gi.conference.locationStuttgart
gi.conference.sessiontitleRegular Research Papers

Dateien

Originalbündel
1 - 1 von 1
Lade...
Vorschaubild
Name:
123.pdf
Größe:
132.47 KB
Format:
Adobe Portable Document Format