Quantifying defence cascade responses as indicators of pig affect and welfare using computer vision methods

Statham, Poppy T E; Hannuna, Sion; Jones, Sara; Campbell, Neill; Colborne, GR; Browne, William J.; Paul, Elizabeth S.; Mendl, Michael

doi:10.1038/s41598-020-65954-6

Cited by 13 publications

(8 citation statements)

References 79 publications

(99 reference statements)

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“…Body dimension (G), weight (G), movement (G, S), farrowing alarms (S) [32,[38][39][40][41][42]45,49,62,64,68,[88][89][90][91]93,96] a A: abattoir, G: growing pigs, P: piglets, S: sows. Stress vocalisation (G, P), object manipulation (G), defence cascade response (G), pig face recognition (S) [33,57,66,74,76,[79][80][81]87] a A: abattoir, G: growing pigs, P: piglets, S: sows. b General biomarkers: activity, weight, feeding behaviour, drinking behaviour, rectal temperature, air quality.…”

Section: Good Housing 42mentioning

confidence: 99%

Information Technologies for Welfare Monitoring in Pigs and Their Relation to Welfare Quality®

2021

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The assessment of animal welfare on-farm is important to ensure that current welfare standards are followed. The current manual assessment proposed by Welfare Quality® (WQ), although being an essential tool, is only a point-estimate in time, is very time consuming to perform, only evaluates a subset of the animals, and is performed by the subjective human. Automation of the assessment through information technologies (ITs) could provide a continuous objective assessment in real-time on all animals. The aim of the current systematic review was to identify ITs developed for welfare monitoring within the pig production chain, evaluate the ITs developmental stage and evaluate how these ITs can be related to the WQ assessment protocol. The systematic literature search identified 101 publications investigating the development of ITs for welfare monitoring within the pig production chain. The systematic literature analysis revealed that the research field is still young with 97% being published within the last 20 years, and still growing with 63% being published between 2016 and mid-2020. In addition, most focus is still on the development of ITs (sensors) for the extraction and analysis of variables related to pig welfare; this being the first step in the development of a precision livestock farming system for welfare monitoring. The majority of the studies have used sensor technologies detached from the animals such as cameras and microphones, and most investigated animal biomarkers over environmental biomarkers with a clear focus on behavioural biomarkers over physiological biomarkers. ITs intended for many different welfare issues have been studied, although a high number of publications did not specify a welfare issue and instead studied a general biomarker such as activity, feeding behaviour and drinking behaviour. The ‘good feeding’ principle of the WQ assessment protocol was the best represented with ITs for real-time on-farm welfare assessment, while for the other principles only few of the included WQ measures are so far covered. No ITs have yet been developed for the ‘Comfort around resting’ and the ‘Good human-animal relationship’ criteria. Thus, the potential to develop ITs for welfare assessment within the pig production is high and much work is still needed to end up with a remote solution for welfare assessment on-farm and in real-time.

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Section: Good Housing 42mentioning

confidence: 99%

Information Technologies for Welfare Monitoring in Pigs and Their Relation to Welfare Quality®

2021

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“…Some studies reviewed here practiced control for color (e.g. white mice [73], black mice [74], white pigs [91]), breed (e.g. British Short Haired cats [84], Labrador Retriever dogs [89]), and sex (e.g.…”

Section: Data Collectionmentioning

confidence: 99%

“…An explorative search for behaviors which potentially extends over time might also be desirable, and the degrees of freedom offered by spatiotemporal feature learning approaches is useful for such a task. [85] macaques holistic frame hand-crafted (high-level) Blumrosen et al [86] macaques holistic frame hand-crafted (high-level) Zhu [87] dogs holistic frame mixed Franzoni et al [88] dogs holistic frame learned Boneh-Shitrit et al [89] dogs holistic frame learned Ferres et al [90] dogs emotion frame hand-crafted (high-level) Statham et al [91] pigs emotion frame -…”

Section: Input: Frames Vs Sequences Of Framesmentioning

confidence: 99%

“…Table 3 dissects the results and evaluation protocols of the works surveyed here. However, we have excluded works which do not involve a down-stream classification task, but rather describe pain behavior using computer vision (e.g., Reuss et al [125] and Statham et al [91]), as well as pre-prints, and the three works which address only AU classification. The categories included in Table 3 are explained as follows.…”

Section: Performance Evaluationmentioning

confidence: 99%

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Going Deeper than Tracking: a Survey of Computer-Vision Based Recognition of Animal Pain and Affective States

Broomé¹,

Feighelstein²,

Zamansky³

et al. 2022

Preprint

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Advances in animal motion tracking and pose recognition have been a game changer in the study of animal behavior. Recently, an increasing number of works go 'deeper' than tracking, and address automated recognition of animals' internal states such as emotions and pain with the aim of improving animal welfare, making this a timely moment for a systematization of the field. This paper provides a comprehensive survey of computer vision-based research on recognition of affective states and pain in animals, addressing both facial and bodily behavior analysis. We summarize the efforts that have been presented so far within this topic -classifying them across different dimensions, highlight challenges and research gaps, and provide best practice recommendations for advancing the field, and some future directions for research.

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“…Besides, computer vision can provide information on behavioral changes such as interactions between individuals, allowing the detection of aggressive events and affiliative behaviors as nursing and playing (37). The use of image-analysis to evaluate the cascade defense has been validated in just one study, however, it still shows the potential of this tool to assess fear and stress-associated conditions (105).…”

Section: Camera-based Technologiesmentioning

confidence: 99%

A Systematic Review on Validated Precision Livestock Farming Technologies for Pig Production and Its Potential to Assess Animal Welfare

et al. 2021

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Several precision livestock farming (PLF) technologies, conceived for optimizing farming processes, are developed to detect the physical and behavioral changes of animals continuously and in real-time. The aim of this review was to explore the capacity of existing PLF technologies to contribute to the assessment of pig welfare. In a web search for commercially available PLF for pigs, 83 technologies were identified. A literature search was conducted, following systematic review guidelines (PRISMA), to identify studies on the validation of sensor technologies for assessing animal-based welfare indicators. Two validation levels were defined: internal (evaluation during system building within the same population that were used for system building) and external (evaluation on a different population than during system building). From 2,463 articles found, 111 were selected, which validated some PLF that could be applied to the assessment of animal-based welfare indicators of pigs (7% classified as external, and 93% as internal validation). From our list of commercially available PLF technologies, only 5% had been externally validated. The more often validated technologies were vision-based solutions (n = 45), followed by load-cells (n = 28; feeders and drinkers, force plates and scales), accelerometers (n = 14) and microphones (n = 14), thermal cameras (n = 10), photoelectric sensors (n = 5), radio-frequency identification (RFID) for tracking (n = 2), infrared thermometers (n = 1), and pyrometer (n = 1). Externally validated technologies were photoelectric sensors (n = 2), thermal cameras (n = 2), microphone (n = 1), load-cells (n = 1), RFID (n = 1), and pyrometer (n = 1). Measured traits included activity and posture-related behavior, feeding and drinking, other behavior, physical condition, and health. In conclusion, existing PLF technologies are potential tools for on-farm animal welfare assessment in pig production. However, validation studies are lacking for an important percentage of market available tools, and in particular research and development need to focus on identifying the feature candidates of the measures (e.g., deviations from diurnal pattern, threshold levels) that are valid signals of either negative or positive animal welfare. An important gap identified are the lack of technologies to assess affective states (both positive and negative states).

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Quantifying defence cascade responses as indicators of pig affect and welfare using computer vision methods

Cited by 13 publications

References 79 publications

Information Technologies for Welfare Monitoring in Pigs and Their Relation to Welfare Quality®

Information Technologies for Welfare Monitoring in Pigs and Their Relation to Welfare Quality®

Going Deeper than Tracking: a Survey of Computer-Vision Based Recognition of Animal Pain and Affective States

A Systematic Review on Validated Precision Livestock Farming Technologies for Pig Production and Its Potential to Assess Animal Welfare

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