An automated sensor-based method of simple behavioural classification of sheep in extensive systems

Umstätter, Christina; Waterhouse, A.; Holland, J.

doi:10.1016/j.compag.2008.05.004

Cited by 75 publications

(44 citation statements)

References 8 publications

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“…Most studies employed various sensor systems such as global positioning system (GPS), radio frequency identification (RFID), and accelerometer for the key parameters to recognize the cow's behavior. For example, recent studies have been conducts (GPS) and accelerometer for positioning of a herd of dairy cows in the field [4][5][6][7][8][9][10][11][12]. However, in regard to the automatic estrus detection, an efficient activity type classification using tri-axial accelerometer becomes a major method for detecting estrus events in dairy cattle [3,[13][14][15].…”

Section: Introductionmentioning

confidence: 99%

Classification system for estrus behavior of cow using an accelerometer

Dulyala

Kuankid

Rattanawong

et al. 2014

Signal and Information Processing Association Annual Summit and Conference (APSIPA), 2014 Asia-Pacific

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Generally, estrus of cow can be observed from its body such as swollen vagina, slime and mydriasis or its behaviors such as bellow out, ride piggyback with the other or let the other rides piggyback. These symptoms will only occur within 18 hours. To access the best result, therefore, the insemination should be processed during on 9-18 hours of that period time. Therefore, this project proposes the application of accelerometer for checking estrus of cow. The accelerometer is installed on the cow's front leg. When the cow rides piggyback the other, resulting the output signal of accelerometer is changed. This signal will be sent to the microcontroller for processing and send the signal through wireless to computer for displaying. The proposed system will be useful to improve efficiency of the insemination.

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Section: Introductionmentioning

confidence: 99%

Classification system for estrus behavior of cow using an accelerometer

Dulyala

Kuankid

Rattanawong

et al. 2014

Signal and Information Processing Association Annual Summit and Conference (APSIPA), 2014 Asia-Pacific

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“…GPS collars have recently been used to investigate habitat selection by lowland sheep on shrubby rangeland in Argentina (Bertiller and Ares 2008) and grassland in Germany (Putfarken et al 2008). Locations of hill sheep have previously been tracked using GPS devices to investigate accuracy of GPS data, home ranges, circadian rhythm and bite rates and classification of behaviour categories (Roberts et al 1995;Rutter et al 1997;Hulbert et al 1998;Hulbert and French 2001;Umstätter et al 2008) but not habitat selection. Compared with field observations and manual mapping techniques, advantages of current GPS tracking techniques include minimizing human disturbance, higher recording frequencies and numbers of records, increased location accuracy, superior recording success rates (i.e.…”

Section: Introductionmentioning

confidence: 99%

Habitat Selection by Grazing Animals in Heterogeneous Environments: The Case of Hill Sheep In Western Ireland

Williams¹,

Walls²,

Walsh³

et al. 2012

Biology & Environment: Proceedings of the Royal Irish Acade

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Uplands and peatlands are of high conservation importance and, while grazing animals are a key management tool for conservation, grazing-related damage can easily occur. Damage to European uplands is most notable in Britain and Ireland, where Scottish Blackface is the dominant hill sheep breed. Therefore, if conservation strategies that address concerns of grazing-related damage are to be strengthened, the Scottish Blackface is a good subject and a better understanding of their resource use would be advantageous. While previous habitat selection studies of hill sheep have depended on direct observation, this particular study uses Global Positioning System tracking collars to determine ewe locations. The study site is on a mountainside in western Ireland that is dominated by blanket bog (52.8%) and wet heath (35.3%). Habitat mapping and ewe range and resource selection analyses indicated that habitat selection was significant (P B0.05), typically acid grassland is selected most followed by wet heath, with blanket bog habitats selected least. Seasonal variation in habitat selection was also evident. These results (1) corroborate the findings of previous work elsewhere on plant community/habitat selection and (2) provide additional information that can be used to strengthen existing or new hill grazing management models that are used to aid decision-making. In particular, management plans should take into consideration the availability (both in terms of total area and connectivity) of the preferred sheep habitats and specifically consider grazing pressure in and between those habitats.

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“…Similar approaches have been used for sheep behaviour classification. For example, see (Umstatter et al, 2008).…”

Section: Animal Behaviour Modellingmentioning

confidence: 99%

A unified model for context-based behavioural modelling and classification

Dabrowski

Villiers

2015

Expert Systems with Applications

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A unified Bayesian model that simultaneously performs behavioural modelling, information fusion and classification is presented. The model is expressed in the form of a dynamic Bayesian network (DBN). Behavioural modelling is performed by tracking the continuous dynamics of a entity and incorporating various contextual elements that influence behaviour. The entity is classified according to its behaviour. Classification is expressed as a conditional probability of the entity class given its tracked trajectory and the contextual elements. Inference in the DBN is performed using a derived Gaussian sum filter. The model is applied to classify vessels, according to their behaviour, in a maritime piracy situation. The novel aspects of this work include the unified approach to behaviour modelling and classification, the way in which contextual information is fused, the unique approach to classification according to behaviour and the associated derived Gaussian sum filter inference algorithm.

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An automated sensor-based method of simple behavioural classification of sheep in extensive systems

Cited by 75 publications

References 8 publications

Classification system for estrus behavior of cow using an accelerometer

Classification system for estrus behavior of cow using an accelerometer

Habitat Selection by Grazing Animals in Heterogeneous Environments: The Case of Hill Sheep In Western Ireland

A unified model for context-based behavioural modelling and classification

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