Identifying Health Status in Grazing Dairy Cows from Milk Mid-Infrared Spectroscopy by Using Machine Learning Methods
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Date
2021-08
Open Access Location
Journal Title
Journal ISSN
Volume Title
Publisher
MDPI (Basel, Switzerland)
Rights
(c) 2021 The Author/s
CC BY 4.0
CC BY 4.0
Abstract
The 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.
Description
Keywords
cow health, machine learning, mid-infrared (MIR) spectrometry, milk spectra, neural networks
Citation
Contla 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-).