Please use this identifier to cite or link to this item: https://hdl.handle.net/1959.11/29603
Title: Identifying Sheep Activity from Tri-Axial Acceleration Signals Using a Moving Window Classification Model
Contributor(s): Barwick, Jamie  (author)orcid ; Lamb, David William  (author); Dobos, Robin  (author)orcid ; Welch, Mitchell  (author)orcid ; Schneider, Derek  (author)orcid ; Trotter, Mark  (author)
Publication Date: 2020-02-15
Open Access: Yes
DOI: 10.3390/rs12040646
Handle Link: https://hdl.handle.net/1959.11/29603
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.
Publication Type: Journal Article
Source of Publication: Remote Sensing, 12(4), p. 1-13
Publisher: MDPI AG
Place of Publication: Switzerland
ISSN: 2072-4292
Fields of Research (FoR) 2008: 070104 Agricultural Spatial Analysis and Modelling
070203 Animal Management
Fields of Research (FoR) 2020: 300206 Agricultural spatial analysis and modelling
300302 Animal management
Socio-Economic Objective (SEO) 2008: 830310 Sheep - Meat
830311 Sheep - Wool
Socio-Economic Objective (SEO) 2020: 100412 Sheep for meat
100413 Sheep for wool
Peer Reviewed: Yes
HERDC Category Description: C1 Refereed Article in a Scholarly Journal
Appears in Collections:Journal Article
School of Environmental and Rural Science
School of Science and Technology

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