Autonomous on-animal sensors in sheep research: A systematic review

Fogarty, Eloise S.; Swain, D. L.; Cronin, G. M.; Trotter, Mark

doi:10.1016/j.compag.2018.04.017

Cited by 53 publications

(37 citation statements)

References 90 publications

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“…Another alternative could be the use of sensor technology. Currently, GPS tracking devices have been developed to assist farmers with flock monitoring and the identification of sick animals [29–31]. However, further research is required to improve the practicality of this technology in order to increase its applicability.…”

Section: Discussionmentioning

confidence: 99%

Evaluating the welfare of extensively managed sheep

et al. 2019

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The aim of this study was to identify the main on-farm welfare issues likely to be encountered in extensive sheep farming systems. Thirty-two commercial sheep farms in Victoria, Australia were involved in this study. Of the 32 farms involved, 30 were visited twice (at mid-pregnancy and weaning), and 2 farms only once (both at weaning). In total, 62 visits were conducted and 6,200 ewes (aged 2–5 years) were examined using six animal-based indicators: body condition score (BCS), fleece condition, skin lesions, tail length, dag score and lameness. In addition, the number of ewes that needed further care (such as sick or injured sheep) was recorded and reported to the farmers. Generalised linear mixed models were conducted to investigate associations between welfare outcomes and visit, ewe breed and location, with all three, and their interactions, as fixed factors. In all instances, farm was set as a random factor to account for specific variation between farms. Overall, the welfare of the ewe flocks, based on the six indicators measured, was considered good. A total of 86.9% of the ewes were in adequate BCS (2.5–3.5), 91% had good fleece condition, 69.2% had no skin lesions, 97.1% had low dag scores, and overall lameness was 4.7%. An important and prevalent risk to welfare identified across farms was short tail length; with 85.7% of ewes having tails docked shorter than the third palpable joint. While the welfare of the flock was good, ewes in need of further care were identified at all farms. There were 185 (3.0%) cases needing further care, and the extent of welfare compromise of these animals was considered significant. Main reasons for further care were moderate/severe lameness or foot-related issues, BCS ≤ 2 and active dermatophilosis or broken wool. To our knowledge, this study constitutes the largest assessment of ewes conducted in Australia, and the findings provide valuable insight into the main welfare issues likely to be encountered in extensive sheep farming enterprises. Future studies should develop practical technologies that can assist in the detection of the welfare issues identified in this study. In addition, the thresholds identified here could be used for future comparison and sheep welfare benchmarking programs to assess farm performance and measure continuous improvements.

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Section: Discussionmentioning

confidence: 99%

Evaluating the welfare of extensively managed sheep

et al. 2019

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“…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.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2020

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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.

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“…The use of the comprehensive assessment of body language provided by QBA coupled with the more specific information offered from behavioural indices of restlessness and grazing is well suited to automated capture. There have been remarkable advancements and success in the development of biosensors to detect behavioural changes across various species [40,41,42]. It is possible that such technologies could be adapted to detect those behaviours presented here.…”

Section: Discussionmentioning

confidence: 99%

Remote Identification of Sheep with Flystrike Using Behavioural Observations

Grant

Wickham

Anderson

et al. 2019

Animals

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Flystrike is a major problem affecting sheep in Australia. Identification of ‘flystruck’ individuals is crucial for treatment; but requires labour-intensive physical examination. As the industry moves toward more low-input systems; there is a need for remote methods to identify flystruck individuals. The aim of this study was to investigate the behaviour of sheep with breech flystrike within a paddock setting. Video footage of sixteen Merino sheep; eight later confirmed with flystrike and eight without; was collected as they moved freely within the paddock with conspecifics. Quantitative behavioural measurements and a qualitative behavioural assessment (QBA) were conducted and compared to their breech conditions (i.e., faecal/urine staining; flystrike severity). Both qualitative and quantitative assessments indicated behavioural differences between flystruck and non-flystruck animals. Flystruck sheep had a behavioural profile characterised by restless behaviour; abnormal postures and reduced grazing time (p < 0.05). Furthermore; flystruck sheep were scored to have a more ‘exhausted/irritated’ demeanour using QBA (p < 0.05). The behavioural responses also corresponded to the flystrike severity scores and condition of the breech area. We conclude that remotely assessed behaviour of flystruck sheep diverges markedly from non-flystruck sheep; and thus could be a low-input method for identifying and treating affected animals.

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Autonomous on-animal sensors in sheep research: A systematic review

Cited by 53 publications

References 90 publications

Evaluating the welfare of extensively managed sheep

Evaluating the welfare of extensively managed sheep

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

Remote Identification of Sheep with Flystrike Using Behavioural Observations

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