Image motion feature extraction for recognition of aggressive behaviors among group-housed pigs

Chen, Chen; Zhu, Weixing; Ma, Chenguang; Guo, Yizheng; Huang, Weixiong; Ruan, Chengzhi

doi:10.1016/j.compag.2017.09.013

Cited by 57 publications

(25 citation statements)

References 24 publications

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“…Machine vision has been used for several purposes in animal sciences, including determination of body weight (Tscharke and Banhazi, 2013;Kongsro, 2014), body condition score (Azzaro et al, 2011;Halachmi et al, 2013), detection of aggressive behavior (Lee et al, 2016a;Chen et al, 2017;Nasirahmadi et al, 2017), walking patterns and lameness (Stavrakakis et al, 2015), and posture and behavior during lactation (Lao et al, 2016). Often, video recordings are used to manually assess animal behavior (Oczak et al, 2013), but the manual analysis of these videos is time-consuming, and may introduce human error (Catarinucci et al, 2014).…”

Section: Machine Vision (Cameras)mentioning

confidence: 99%

Large-Scale Phenotyping of Livestock Welfare in Commercial Production Systems: A New Frontier in Animal Breeding

et al. 2020

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Genomic breeding programs have been paramount in improving the rates of genetic progress of productive efficiency traits in livestock. Such improvement has been accompanied by the intensification of production systems, use of a wider range of precision technologies in routine management practices, and high-throughput phenotyping. Simultaneously, a greater public awareness of animal welfare has influenced livestock producers to place more emphasis on welfare relative to production traits. Therefore, management practices and breeding technologies in livestock have been developed in recent years to enhance animal welfare. In particular, genomic selection can be used to improve livestock social behavior, resilience to disease and other stress factors, and ease habituation to production system changes. The main requirements for including novel behavioral and welfare traits in genomic breeding schemes are: (1) to identify traits that represent the biological mechanisms of the industry breeding goals; (2) the availability of individual phenotypic records measured on a large number of animals (ideally with genomic information); (3) the derived traits are heritable, biologically meaningful, repeatable, and (ideally) not highly correlated with other traits already included in the selection indexes; and (4) genomic information is available for a large number of individuals (or genetically close individuals) with phenotypic records. In this review, we (1) describe a potential route for development of novel welfare indicator traits (using ideal phenotypes) for both genetic and genomic selection schemes; (2) summarize key indicator variables of livestock behavior and welfare, including a detailed assessment of thermal stress in livestock; (3) describe the primary statistical and bioinformatic methods available for large-scale data analyses of animal welfare; and (4) identify major advancements, challenges, and opportunities to generate high-throughput and large-scale datasets to enable genetic and genomic selection for improved welfare in livestock. A wide variety of novel welfare indicator traits can be derived from information captured by modern technology such as sensors, automatic feeding systems, milking robots, activity monitors, video cameras, and

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Section: Machine Vision (Cameras)mentioning

confidence: 99%

Large-Scale Phenotyping of Livestock Welfare in Commercial Production Systems: A New Frontier in Animal Breeding

et al. 2020

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“…Bringing the animals closer to the farmer Oczak et al (2014) and Chen et al (2017) classified the aggressive behaviour into high-and medium-aggression among pigs. However, only using the high and medium intensity for definition and recognition of aggressive behaviours will be subject to greater interference by other behaviours.…”

Section: Image Analysismentioning

confidence: 99%

“…In the case of pig aggression, features of mean intensity and occupation index Oczak et al, 2014) were found to be useful in the past. Later, the acceleration feature (Chen et al, 2017) and motion features with higher discrimination (e.g. kinetic energy (Zhao et al, 2016), displacement, etc.)…”

Section: Image Analysismentioning

confidence: 99%

“…A fourth and final step is to classify the image frames into the important behavioural interactions in question (such as aggressive behavioural markers among pigs) based on the values of the extracted behavioural features. Methods for such classifications include linear discriminant analysis , neural networks and hierarchical clustering (Chen et al, 2017), among others. The threshold of each behavioural feature can then be used in the recognition rules of, for example, aggressive behaviours.…”

Section: Image Analysismentioning

confidence: 99%

“…As an example, when two pigs in a frame simultaneously meet at a certain acceleration (motion feature) and with a certain distance (position feature), it can be considered as a frame with head-to-head knocking. When the ratio of such frames in a period of time exceeds a fixed value, also referred to as the minimum recognition unit (Chen et al, 2017), it is considered that an event of head-to-head knocking occurred and, thus, that an aggressive interaction among the pigs in the group occurred. Further work is required to develop better rules (thresholds) to recognise each aggressive behaviour in frames of videos and to choose the accuracy, sensitivity and specificity being suitable for evaluation of the recognition results .…”

Section: Image Analysismentioning

confidence: 99%

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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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A New Approach to Automatic Heat Detection of Cattle in Video

Pasupa

Lodkaew

2019

Communications in Computer and Information Science

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Image motion feature extraction for recognition of aggressive behaviors among group-housed pigs

Cited by 57 publications

References 24 publications

Large-Scale Phenotyping of Livestock Welfare in Commercial Production Systems: A New Frontier in Animal Breeding

Large-Scale Phenotyping of Livestock Welfare in Commercial Production Systems: A New Frontier in Animal Breeding

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

A New Approach to Automatic Heat Detection of Cattle in Video

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