Sensors · 2021 · 33 citations · 53 references
AgricultureAnimal SciencesDairy ScienceAnimal WelfareLivestock ProductionPasture MonitoringAnimal Welfare AssessmentLamb HealthLivestock HealthPasture Health AssessmentPrecision Livestock FarmingSheep WelfareAccelerometer MonitoringAccelerometer SensorsAccelerometer-based Behavior MonitoringAccelerometersAnimal Behaviour MonitoringClassification Accuracy AssessmentDiel Activity PatternsLivestock Monitoring
Monitoring activity patterns of animals offers the opportunity to assess individual health and welfare in support of precision livestock farming. The purpose of this study was to use a triaxial accelerometer sensor to determine the diel activity of sheep on pasture. Six Perendale ewe lambs, each fitted with a neck collar mounting a triaxial accelerometer, were filmed during targeted periods of sheep activities: grazing, lying, walking, and standing. The corresponding acceleration data were fitted using a Random Forest algorithm to classify activity (=classifier). This classifier was then applied to accelerometer data from an additional 10 ewe lambs to determine their activity budgets. Each of these was fitted with a neck collar mounting an accelerometer as well as two additional accelerometers placed on a head halter and a body harness over the shoulders of the animal. These were monitored continuously for three days. A classification accuracy of 89.6% was achieved for the grazing, walking and resting activities (i.e., a new class combining lying and standing activity). Triaxial accelerometer data showed that sheep spent 64% (95% CI 55% to 74%) of daylight time grazing, with grazing at night reduced to 14% (95% CI 8% to 20%). Similar activity budgets were achieved from the halter mounted sensors, but not those on a body harness. These results are consistent with previous studies directly observing daily activity of pasture-based sheep and can be applied in a variety of contexts to investigate animal health and welfare metrics e.g., to better understand the impact that young sheep can suffer when carrying even modest burdens of parasitic nematodes.
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Leo Breiman · Machine Learning · 2001 · 126.2K citations · Full text
RANDOM FORESTS FOR CLASSIFICATION IN ECOLOGY
D. Richard Cutler, Thomas C. Edwards, Karen H. Beard et al. · Ecology · 2007 · 4.8K citations
Observing the unwatchable through acceleration logging of animal behavior
Danielle Brown, Roland Kays, Martin Wikelski et al. · Animal Biotelemetry · 2013 · 626 citations · Full text