Predicting bite rate of grazing cattle from accelerometry data via semi-supervised regression

Hu, Shuwen; Arablouei, Reza; Bishop-Hurley, Greg; Reverter, Antônio; Ingham, Aaron

doi:10.1016/j.atech.2023.100256

Cited by 5 publications

(1 citation statement)

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“…PLF approaches provide the opportunity for continuous and objective monitoring of individual animals and may be used for the assessment of animal health and welfare, productivity, sustainability, and overall farm management [ 5 , 6 , 7 , 8 ]. Data collected by accelerometers have been used to classify livestock behaviours such as grazing, ruminating, and walking [ 9 , 10 , 11 , 12 , 13 ], as well as predict biting and chewing rates [ 14 , 15 ]. Accelerometer data can also be used to quantify activity levels, representing the cumulative forces measured over a specific time window.…”

Section: Introductionmentioning

confidence: 99%

Analyzing Cattle Activity Patterns with Ear Tag Accelerometer Data

Hu,

Reverter,

Arablouei

et al. 2024

Animals

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In this study, we equip two breeds of cattle located in tropical and temperate climates with smart ear tags containing triaxial accelerometers to measure their activity levels across different time periods. We produce activity profiles when measured by each of four statistical features, the mean, median, standard deviation, and median absolute deviation of the Euclidean norm of either unfiltered or high-pass-filtered accelerometer readings over five-minute windows. We then aggregate the values from the 5 min windows into hourly or daily (24 h) totals to produce activity profiles for animals kept in each of the test environments. To gain a better understanding of the variation between the peak and nadir activity levels within a 24 h period, we divide each day into multiple equal-length intervals, which can range from 2 to 96 intervals. We then calculate a statistical measure, called daily differential activity (DDA), by computing the differences in feature values for each interval pair. Our findings demonstrate that patterns within the activity profile are more clearly visualised from readings that have been subject to high-pass filtering and that the median of the acceleration vector norm is the most reliable feature for characterising activity and calculating the DDA measure. The underlying causes for these differences remain elusive and is likely attributable to environmental factors, cattle breeds, or management practices. Activity profiles produced from the standard deviation (a feature routinely applied to the quantification of activity level) showed less uniformity between animals and larger variation in values overall. Assessing activity using ear tag accelerometers holds promise for monitoring animal health and welfare. However, optimal results may only be attainable when true diurnal patterns are detected and accounted for.

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