Identifying Health Status in Grazing Dairy Cows from Milk Mid-Infrared Spectroscopy by Using Machine Learning Methods

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dc.citation.issue8
dc.citation.volume11
dc.contributor.authorContla Hernández B
dc.contributor.authorLopez-Villalobos N
dc.contributor.authorVignes M
dc.contributor.editorVan Winden S
dc.coverage.spatialSwitzerland
dc.date.accessioned2024-01-05T02:07:32Z
dc.date.accessioned2024-07-25T06:40:37Z
dc.date.available2021-07-21
dc.date.available2024-01-05T02:07:32Z
dc.date.available2024-07-25T06:40:37Z
dc.date.issued2021-08
dc.description.abstractThe early detection of health problems in dairy cattle is crucial to reduce economic losses. Mid-infrared (MIR) spectrometry has been used for identifying the composition of cow milk in routine tests. As such, it is a potential tool to detect diseases at an early stage. Partial least squares discriminant analysis (PLS-DA) has been widely applied to identify illness such as lameness by using MIR spectrometry data. However, this method suffers some limitations. In this study, a series of machine learning techniques-random forest, support vector machine, neural network (NN), convolutional neural network and ensemble models-were used to test the feasibility of identifying cow sickness from 1909 milk sample MIR spectra from Holstein-Friesian, Jersey and crossbreed cows under grazing conditions. PLS-DA was also performed to compare the results. The sick cow records had a time window of 21 days before and 7 days after the milk sample was analysed. NN showed a sensitivity of 61.74%, specificity of 97% and positive predicted value (PPV) of nearly 60%. Although the sensitivity of the PLS-DA was slightly higher than NN (65.6%) the specificity and PPV were lower (79.59% and 15.25%, respectively). This indicates that by using NN, it is possible to identify a health problem with a reasonable level of accuracy.
dc.description.confidentialfalse
dc.edition.editionAugust 2021
dc.format.pagination2154-
dc.identifier.author-urlhttps://www.ncbi.nlm.nih.gov/pubmed/34438612
dc.identifier.citationContla Hernández B, Lopez-Villalobos N, Vignes M. (2021). Identifying Health Status in Grazing Dairy Cows from Milk Mid-Infrared Spectroscopy by Using Machine Learning Methods.. Animals (Basel). 11. 8. (pp. 2154-).
dc.identifier.doi10.3390/ani11082154
dc.identifier.eissn2076-2615
dc.identifier.elements-typejournal-article
dc.identifier.issn2076-2615
dc.identifier.numberARTN 2154
dc.identifier.piiani11082154
dc.identifier.urihttps://mro.massey.ac.nz/handle/10179/70660
dc.languageeng
dc.publisherMDPI (Basel, Switzerland)
dc.publisher.urihttps://www.mdpi.com/2076-2615/11/8/2154
dc.relation.isPartOfAnimals (Basel)
dc.rights(c) 2021 The Author/s
dc.rightsCC BY 4.0
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectcow health
dc.subjectmachine learning
dc.subjectmid-infrared (MIR) spectrometry
dc.subjectmilk spectra
dc.subjectneural networks
dc.titleIdentifying Health Status in Grazing Dairy Cows from Milk Mid-Infrared Spectroscopy by Using Machine Learning Methods
dc.typeJournal article
pubs.elements-id447570
pubs.organisational-groupOther
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