Analysis of aggressive behaviours of pigs by automatic video recordings

Oczak, Maciej; Ismayilova, Gunel; Costa, Annamaria; Viazzi, Stefano; Sonoda, Lilia Thays; Fels, Michaela; Bahr, Claudia; Hartung, J.; Guarino, Marcella; Berckmans, Daniël; Vranken, Erik

doi:10.1016/j.compag.2013.09.015

Cited by 53 publications

(22 citation statements)

References 33 publications

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“…Using these types of observations and considering the existing literature (McGlone ; Meynhardt ; Oczak et al. ), vocal behavior was sorted into seven classes: (1) alarm, (2) alert/nervous, (3) attacked, (4) chased, (5) contact, (6) scared/threatened, and (7) submissive. When the reaction of the whole group was to flee after a call was produced, this vocalization was contextually labeled as an ‘alarm’ call.…”

Section: Methodsmentioning

confidence: 99%

“…Unlike individual identification, interaction type and context were easier to assess, as observa-tions of movement, call loudness, and/or the sequence in which calls were emitted did not depend as much on lighting. Using these types of observations and considering the existing literature (McGlone 1985;Meynhardt 1990;Oczak et al 2013), vocal behavior was sorted into seven classes:…”

Section: Data Collectionmentioning

confidence: 99%

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Structural Classification of Wild Boar (Sus scrofa) Vocalizations

et al. 2016

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Determining whether a species' vocal communication system is graded or discrete requires definition of its vocal repertoire. In this context, research on domestic pig (Sus scrofa domesticus) vocalizations, for example, has led to significant advances in our understanding of communicative functions. Despite their close relation to domestic pigs, little is known about wild boar (Sus scrofa) vocalizations. The few existing studies, conducted in the 1970s, relied on visual inspections of spectrograms to quantify acoustic parameters and lacked statistical analysis. Here, we use objective signal processing techniques and advanced statistical approaches to classify 616 calls recorded from semi‐free ranging animals. Based on four spectral and temporal acoustic parameters—quartile Q25, duration, spectral flux, and spectral flatness—extracted from a multivariate analysis, we refine and extend the conclusions drawn from previous work and present a statistically validated classification of the wild boar vocal repertoire into four call types: grunts, grunt‐squeals, squeals, and trumpets. While the majority of calls could be sorted into these categories using objective criteria, we also found evidence supporting a graded interpretation of some wild boar vocalizations as acoustically continuous, with the extremes representing discrete call types. The use of objective criteria based on modern techniques and statistics in respect to acoustic continuity advances our understanding of vocal variation. Integrating our findings with recent studies on domestic pig vocal behavior and emotions, we emphasize the importance of grunt‐squeals for acoustic approaches to animal welfare and underline the need of further research investigating the role of domestication on animal vocal communication.

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Section: Methodsmentioning

confidence: 99%

Section: Data Collectionmentioning

confidence: 99%

Structural Classification of Wild Boar (Sus scrofa) Vocalizations

et al. 2016

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“…In earlier studies (Shao and Xin, 2008;Kashiha et al, 2013; Two cases considered whole sequence and sequence with border cases omitted since they are ambiguous for human inspection. Oczak et al, 2013), segmentation presumed the flooring being uniform due to the lack of bedding material that compromized the earlier segmentation technologies. Therefore, in studies and applications in less constrained types of animal environments, with varying backgrounds, the approach used in this paper should be beneficial.…”

Section: Resultsmentioning

confidence: 99%

Development of automatic surveillance of animal behaviour and welfare using image analysis and machine learned segmentation technique

Nilsson

Herlin

Ardö

et al. 2015

Animal

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In this paper the feasibility to extract the proportion of pigs located in different areas of a pig pen by advanced image analysis technique is explored and discussed for possible applications. For example, pigs generally locate themselves in the wet dunging area at high ambient temperatures in order to avoid heat stress, as wetting the body surface is the major path to dissipate the heat by evaporation. Thus, the portion of pigs in the dunging area and resting area, respectively, could be used as an indicator of failure of controlling the climate in the pig environment as pigs are not supposed to rest in the dunging area. The computer vision methodology utilizes a learning based segmentation approach using several features extracted from the image. The learning based approach applied is based on extended state-of-the-art features in combination with a structured prediction framework based on a logistic regression solver using elastic net regularization. In addition, the method is able to produce a probability per pixel rather than form a hard decision. This overcomes some of the limitations found in a setup using grey-scale information only. The pig pen is a difficult imaging environment because of challenging lighting conditions like shadows, poor lighting and poor contrast between pig and background. In order to test practical conditions, a pen containing nine young pigs was filmed from a top view perspective by an Axis M3006 camera with a resolution of 640 × 480 in three, 10-min sessions under different lighting conditions. The results indicate that a learning based method improves, in comparison with greyscale methods, the possibility to reliable identify proportions of pigs in different areas of the pen. Pigs with a changed behaviour (location) in the pen may indicate changed climate conditions. Changed individual behaviour may also indicate inferior health or acute illness.

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“…Thus, more work is needed to be able to recognise the unwanted behavioural trait, aggression, from the wanted behavioural trait, play, using image analysis. One approach to achieve this distinction would be the ability to recognise specific aggressive markers from image analysis, including the head-to-head knocking, head-to-body knocking, parallel pressing, inverse parallel pressing, ear biting, neck biting, body biting and tail biting (Oczak et al, 2013).…”

Section: Image Analysismentioning

confidence: 99%

Review: Precision livestock farming: building ‘digital representations’ to bring the animals closer to the farmer

Norton

Chen

Larsen

et al. 2019

Animal

120

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Economic pressures continue to mount on modern-day livestock farmers, forcing them to increase herds sizes in order to be commercially viable. The natural consequence of this is to drive the farmer and the animal further apart. However, closer attention to the animal not only positively impacts animal welfare and health but can also increase the capacity of the farmer to achieve a more sustainable production. State-of-the-art precision livestock farming (PLF) technology is one such means of bringing the animals closer to the farmer in the facing of expanding systems. Contrary to some current opinions, it can offer an alternative philosophy to 'farming by numbers'. This review addresses the key technology-oriented approaches to monitor animals and demonstrates how image and sound analyses can be used to build 'digital representations' of animals by giving an overview of some of the core concepts of PLF tool development and value discovery during PLF implementation. The key to developing such a representation is by measuring important behaviours and events in the livestock buildings. The application of image and sound can realise more advanced applications and has enormous potential in the industry. In the end, the importance lies in the accuracy of the developed PLF applications in the commercial farming system as this will also make the farmer embrace the technological development and ensure progress within the PLF field in favour of the livestock animals and their well-being. ImplicationsThis work address the main approaches utilised in developing precision livestock farming tools. Precision livestock farming is an approach that enables the farmer with more objective information about the animal to make better choices about the sustainability of their production system. This paper demonstrates some of the key solutions and the approaches taken to develop technologies with sound and image analyses. †

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Analysis of aggressive behaviours of pigs by automatic video recordings

Cited by 53 publications

References 33 publications

Structural Classification of Wild Boar (Sus scrofa) Vocalizations

Structural Classification of Wild Boar (Sus scrofa) Vocalizations

Development of automatic surveillance of animal behaviour and welfare using image analysis and machine learned segmentation technique

Review: Precision livestock farming: building ‘digital representations’ to bring the animals closer to the farmer

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