Prediction of welfare outcomes for broiler chickens using Bayesian regression on continuous optical flow data

Roberts, Stephen; Cain, Russell; Dawkins, Marian Stamp

doi:10.1098/rsif.2012.0594

Cited by 28 publications

(25 citation statements)

References 15 publications

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“…Changes in behaviour are increasingly recognised as precursors of clinical signs of disease or other problems (Toscano and others 2010, Lee and others 2011) so that changes in optical flow have the potential to give early warning of disease at the very earliest stages. For example, optical flow patterns observed in chicken flocks as young as three days old have already been shown to predict hockburn prevalence at slaughter (Roberts and others 2012). Similarly, optical flow patterns in chicken flocks of less than seven days old have been shown to predict Campylobacter prevalence at slaughter (Colles and others 2016).…”

Section: Introductionmentioning

confidence: 99%

Early warning of footpad dermatitis and hockburn in broiler chicken flocks using optical flow, bodyweight and water consumption

et al. 2017

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Footpad dermatitis and hockburn are serious welfare and economic issues for the production of broiler (meat) chickens. The authors here describe the use of an inexpensive camera system that monitors the movements of broiler flocks throughout their lives and suggest that it is possible to predict, even in young birds, the cross-sectional prevalence at slaughter of footpad dermatitis and hockburn before external signs are visible. The skew and kurtosis calculated from the authors' camera-based optical flow system had considerably more power to predict these outcomes in the 50 flocks reported here than water consumption, bodyweight or mortality and therefore have the potential to inform improved flock management through giving farmers early warning of welfare issues. Further trials are underway to establish the generality of the results.

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Section: Introductionmentioning

confidence: 99%

Early warning of footpad dermatitis and hockburn in broiler chicken flocks using optical flow, bodyweight and water consumption

et al. 2017

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“…A combination of OF and Bayesian regression was used by Roberts et al [69] to predict health and welfare on a continuous basis. Mean, variance, skewness, and kurtosis were estimated daily using an OF algorithm.…”

Section: Image Technologymentioning

confidence: 99%

Technology and Poultry Welfare

Sassi

Averós

Estévez

2016

Animals

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Consideration of animal welfare is essential to address the consumers’ demands and for the long term sustainability of commercial poultry. However, assessing welfare in large poultry flocks, to be able to detect potential welfare risks and to control or minimize its impact is difficult. Current developments in technology and mathematical modelling open new possibilities for real-time automatic monitoring of animal welfare and health. New technological innovations potentially adaptable to commercial poultry are appearing, although their practical implementation is still being defined. In this paper, we review the latest technological developments with potential to be applied to poultry welfare, especially for broiler chickens and laying hens. Some of the examples that are presented and discussed include the following: sensors for farm environmental monitoring, movement, or physiological parameters; imaging technologies such as optical flow to detect gait problems and feather pecking; infrared technologies to evaluate birds’ thermoregulatory features and metabolism changes, that may be indicative of welfare, health and management problems. All these technologies have the potential to be implemented at the commercial level to improve birds’ welfare and to optimize flock management, therefore, improving the efficiency of the system in terms of use of resources and, thus, long term sustainability.

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“…Furthermore, it is believed that the methodology used by [19] to measure the birds' activity index might be more accurate than that used in the present study [27], so further analysis is desirable in order to improve the model. The optical flow analysis it is another methodology to access flock movement [18][19][20][21][22], which has the potential to be adapt for the circumstances of this study.…”

Section: Variable Descriptionmentioning

confidence: 99%

“…It can be an efficient method to estimate the level of animals' welfare to improve flock management by aiding predictions for further decision making [17][18][19][20][21][22][23]. This study aimed to use computational image analysis techniques in order to access the behaviour of broiler chickens in a commercial house, when interacting with three different types of feeder, considering the flock motion, floor occupied area by the birds' body and eating behaviour.…”

Section: Introductionmentioning

confidence: 99%

Detection of flock movement and behaviour of broiler chickens at different feeders using image analysis

Neves

Mehdizadeh

Tscharke

et al. 2015

Information Processing in Agriculture

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Animal behaviourFeeding Image processing Motion Occupied area A B S T R A C TThe behaviour of broiler chickens has been extensively studied as a function of stocking density and environmental conditions, but limited information was reported in the scientific literature about the effect of feeder type on birds' feeding process. The main objective of this study was to assess the effects of three different feeder types in relation to the birds' behaviour in its surroundings. The analysed feeders were: tube-type with partition grid (F1); tube-type without partition grid (F2); and automatic type with partition grid (F3).The considered variables were: occupied area (OA); activity index (AI) (flock movement); total birds presented in the area (TB); and birds effectively eating (EE). OA and AI were calculated by computational image analysis while TB and EE were manually measured. The results indicated that the feeder type could have influenced the birds' behaviour regarding to OA (R 2 = 0.56), TB (R 2 = 0.48), and EE (R 2 = 0.40), but AI (R 2 = 0.01) was not found to be directly influenced by the feeder type. A higher percentage of birds effectively eating were found in F2 (86.4%), which was the one with the largest free area to access the feed. Similar average number of total birds was found in F1, but with a lower percentage of individuals effectively eating (63.3%), which means that birds were nearby this feeder performing other behaviours. Since the assessed feeders were in the same house under the same conditions, it can be suggested that not only the free area to access the feed but potentially the design of feeders could have influenced the birds' feeding preference. The real beneficial effect of the adoption of partition grid on feed trays is still uncertain, and it is also unclear whether the financial value of reduced wastage would compensate the possible reduction in feed intake.

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Prediction of welfare outcomes for broiler chickens using Bayesian regression on continuous optical flow data

Cited by 28 publications

References 15 publications

Early warning of footpad dermatitis and hockburn in broiler chicken flocks using optical flow, bodyweight and water consumption

Early warning of footpad dermatitis and hockburn in broiler chicken flocks using optical flow, bodyweight and water consumption

Technology and Poultry Welfare

Detection of flock movement and behaviour of broiler chickens at different feeders using image analysis

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