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Open AccessArticle

Identifying Sheep Activity from Tri-Axial Acceleration Signals Using a Moving Window Classification Model

1
Precision Agriculture Research Group, University of New England, Armidale, NSW 2351, Australia
2
Food Agility CRC, University of New England, Armidale, NSW 2351, Australia
3
NSW Department of Primary Industries, Livestock Industries Centre, University of New England, Armidale, NSW 2351, Australia
*
Author to whom correspondence should be addressed.
Current address: Sheep CRC, University of New England, Armidale, NSW 2351, Australia.
Current address: Central Queensland University, CQIRP, Rockhampton, QLD 4702, Australia.
Remote Sens. 2020, 12(4), 646; https://doi.org/10.3390/rs12040646
Received: 23 January 2020 / Revised: 10 February 2020 / Accepted: 11 February 2020 / Published: 15 February 2020
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. View Full-Text
Keywords: accelerometer; activity; behaviour; livestock; monitoring; sheep accelerometer; activity; behaviour; livestock; monitoring; sheep
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Barwick, J.; Lamb, D.W.; Dobos, R.; Welch, M.; Schneider, D.; Trotter, M. Identifying Sheep Activity from Tri-Axial Acceleration Signals Using a Moving Window Classification Model. Remote Sens. 2020, 12, 646.

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