Abstract
Behaviour is a useful indicator of an individual animal’s overall wellbeing. There is widespread agreement that measuring and monitoring individual behaviour autonomously can provide valuable opportunities to trigger and refine on-farm management decisions. Conventionally, this has required visual observation of animals across a set time period. Technological advancements, such as animal-borne accelerometers, are offering 24/7 monitoring capability. Accelerometers have been used in research to quantify animal behaviours for a number of years. Now, technology and software developers, and more recently decision support platform providers, are integrating to offer commercial solutions for the extensive livestock industries. For these systems to function commercially, data must be captured, processed and analysed in sync with data acquisition. Practically, this requires a continuous stream of data or a duty cycled data segment and, from an analytics perspective, the application of moving window algorithms to derive the required classification. The aim of this study was to evaluate the application of a ‘clean state’ moving window behaviour state classification algorithm applied to 3, 5 and 10 second duration segments of data (including behaviour transitions), to categorise data emanating from collar, leg and ear mounted accelerometers on five Merino ewes. 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. The accuracy in identifying behaviour classes was highest for the ear-mounted sensor (86%–95%), followed by the collar-mounted sensor (67%–88%) and leg-mounted sensor (48%–94%). Between-sheep variations in classification accuracy confirm the sensor orientation is an important source of variation in all deployment modes. This research suggests a moving window classifier is capable of segregating continuous accelerometer signals into exclusive behaviour classes and may provide an appropriate data processing framework for commercial deployments.
Highlights
On-animal-sensors capable of measuring individual animal behaviour and location have long been considered a potentially transformative technology for extensive livestock grazing enterprises.Such sensors have been proposed to alleviate many of the labour and cost challenges associated with monitoring livestock health [1]
Limited lying behaviour was recorded because of a lack of animal motivation to rest in a recumbent posture; these results should be interpreted with caution
While this current study did not investigate the specific causes of variability between sheep, it is speculated that the primary source of this between-animal variation in signals arose from two related causes
Summary
On-animal-sensors capable of measuring individual animal behaviour and location have long been considered a potentially transformative technology for extensive livestock grazing enterprises. Such sensors have been proposed to alleviate many of the labour and cost challenges associated with monitoring livestock health [1]. One particular class of sensor, accelerometers, offer the capability to monitor changes in physical behaviour purely on the basis of movement and orientation Such information could be used to create animal health and welfare indicators on the basis of deviations in activity patterns from baseline levels [2,3]. Limited research has been conducted on the extensive sheep grazing industry, and there has been little consideration given to optimising data processing and analysis protocols
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