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The model was successful at categorising grazing, standing, walking and lying behaviour classes with varying sensitivity, and no significant difference in model accuracy was observed between the three moving window lengths.
1 lut 2018 · This current study aimed to classify three basic sheep behaviours using a QDA model based on three accelerometer-derived metrics. Tri-axial accelerometers were deployed in three locations: front leg, neck (collar) and ear.
11 sty 2018 · Lameness is a clinical symptom associated with a number of sheep diseases around the world, having adverse effects on weight gain, fertility, and lamb birth weight, and increasing the risk of secondary diseases. Current methods to identify lame animals rely on labour intensive visual inspection.
7 lip 2022 · The aim of this study was to evaluate the performance of multiple combinations of algorithms (extreme learning machine (ELM), AdaBoost, stacking), time windows (3, 5 and 11 s) and sensor data (three-axis accelerometer (T-acc), three-axis gyroscope (T-gyr), and T-acc and T-gyr) for grazing sheep behavior classification on continuous behavior ...
1 maj 2024 · Hu et al. (2020) attached motion sensors to the neck of sheep to classify four behaviors, including ruminating, and the best F1-score for ruminating was 65.5 % (grazing, ruminating, walking and standing).
1 kwi 2022 · This study investigated the capacity of machine learning (ML) behaviour classification to monitor changes in sheep behaviour around the time of lambing using ear-borne accelerometers.
Identifying Sheep Activity from Tri-Axial Acceleration Signals Using a Moving Window Classification Model. Robin Dobos. 2020, Remote Sensing. Behaviour is a useful indicator of an individual animal’s overall wellbeing.
