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Detecting dairy cows' lying behaviour using noisy 3D ultrawide band positioning datause asterix (*) to get italics
I. Adriaens, W. Ouweltjes, M. Pastell, E. Ellen, C. KamphuisPlease use the format "First name initials family name" as in "Marie S. Curie, Niels H. D. Bohr, Albert Einstein, John R. R. Tolkien, Donna T. Strickland"
<p>In precision livestock farming, technology-based solutions are used to monitor and manage<br>livestock and support decisions based on on-farm available data. In this study, we developed<br>a methodology to monitor the lying behaviour of dairy cows using noisy spatial positioning<br>data, thereby combining time-series segmentation based on statistical changepoints and a<br>machine learning classification algorithm using bagged decision trees. Position data (x, y, z<br>-coordinates) collected with an ultra-wide band positioning system from 30 dairy cows<br>housed in a freestall barn were used. After the data pre-processing and selection, statistical<br>changepoints were detected per cow-day (no. included = 331) in normalized 'distance from<br>the centre of the barn' and (z) time series. Accelerometer-based lying bout data were used<br>as a practical ground truth. For the segmentation, changepoint detection was compared<br>with getting-up or lying-down events as indicated by the accelerometers. For the<br>classification of segments into lying or non-lying behaviour, two data splitting techniques<br>resulting in 2 different training and test sets were implemented to train and evaluate<br>performance: one based on the data collection day and one based on cow identity. In 85.5%<br>of the lying-down or getting-up events a changepoint was detected in a window of 5<br>minutes. Of the events where no detection had taken place, 86.2% could be associated with<br>either missing data (large gaps) or a very short lying or non-lying bout. Overall classification<br>and lying behaviour prediction performance was above 91% in both independent test sets,<br>with a very high consistency across cow-days. Per cow-day, the average error in the<br>estimation of the lying durations were 7.1% and 7.8% for the cow-identity and time-based<br>data splits respectively. This resulted in sufficient accuracy for automated quantification of<br>lying behaviour in dairy cows, for example for health or welfare monitoring purposes.&nbsp;</p> should fill this box only if you chose 'All or part of the results presented in this preprint are based on data'. URL must start with http:// or https:// should fill this box only if you chose 'Scripts were used to obtain or analyze the results'. URL must start with http:// or https:// should fill this box only if you chose 'Codes have been used in this study'. URL must start with http:// or https://
spatial data; ultra-wide band technology; dairy cow; lying behavior
NonePlease indicate the methods that may require specialised expertise during the peer review process (use a comma to separate various required expertises).
Animal behaviour , Mathematical modelling, Precision livestock farming
No need for them to be recommenders of PCI Anim Sci. Please do not suggest reviewers for whom there might be a conflict of interest. Reviewers are not allowed to review preprints written by close colleagues (with whom they have published in the last four years, with whom they have received joint funding in the last four years, or with whom they are currently writing a manuscript, or submitting a grant proposal), or by family members, friends, or anyone for whom bias might affect the nature of the review - see the code of conduct
e.g. John Doe []
2022-02-28 18:19:37
Eliel Gonzalez-Garcia