Automatic detection of deviations in activity levels in groups of broiler chickens – A pilot study

Automatic monitoring of activity levels in broiler chicken flocks may allow early detection of irregular activity patterns, indicating potential problems in the flock. Leg disorders are the main welfare concern for modern broiler chickens. Dynamic control of broiler activity during the growing period may improve the muscular-skeletal development thereby reducing leg disorders and improving welfare of the animals. The undisturbed activity of groups of broiler chickens was investigated in three steps. The first step applied a model, which was able to filter out outliers in the data stream of automatically recorded activity from overhead video cameras. The second step described the undisturbed levels of activity in groups of broiler chickens over the course of a day in week 1, 2 and 3. The third step applied a method to detect deviations in activity level, thereby giving an indication of a level change in activity within the flock of broilers. The results indicate the potential for automatic detection of deviations in activity level in flocks of broiler chickens. From these methods it is possible to develop automatic monitoring systems, which can notify the producer when the activity in the broiler flock deviates from an expected level at a given age. Such monitoring system may improve the welfare of commercial broiler chickens.

[1]  T. Heiskanen,et al.  Automated oestrus detection method for group housed sows using acceleration measurements , 2007 .

[2]  Iver Thysen,et al.  Monitoring Bulk Tank Somatic Cell Counts by a Multi-Process Kalman Filter , 1993 .

[3]  R C Newberry,et al.  Influence of light intensity on behavior and performance of broiler chickens. , 1988, Poultry science.

[4]  W. Bessei,et al.  Effect of locomotor activity on bone development and leg disorders in broilers , 1998 .

[5]  B. L. Nielsen,et al.  Effects of qualitative and quantitative feed restriction on the activity of broiler chickens , 2003 .

[6]  Anders Ringgaard Kristensen,et al.  A model for monitoring the condition of young pigs by their drinking behaviour , 2005 .

[7]  Michael A. West,et al.  Bayesian Forecasting and Dynamic Models (2nd edn) , 1997, J. Oper. Res. Soc..

[8]  Donald M. Broom,et al.  A Review of the Aetiology and Pathology of Leg Weakness in Broilers in Relation to Welfare , 2002 .

[9]  Marian Stamp Dawkins,et al.  Time budgets in Red Junglefowl as a baseline for the assessment of welfare in domestic fowl , 1989 .

[10]  Jean-Marie Aerts,et al.  Modelling the dynamic activity of broiler chickens in response to step-wise changes in light intensity , 2006 .

[11]  G. Wang,et al.  Wet litter and perches as risk factors for the development of foot pad dermatitis in floor-housed hens. , 1998, British poultry science.

[12]  W. B. Roush,et al.  Kalman filter and an example of its use to detect changes in poultry production responses , 1992 .

[13]  Fred Spiring,et al.  Introduction to Statistical Quality Control , 2007, Technometrics.

[14]  I. R. Korsgaard,et al.  Time-series models on somatic cell score improve detection of mastitis , 2008 .

[15]  J. Faure,et al.  Influence of increased environmental complexity on leg condition, performance, and level of fearfulness in broilers. , 2002, Poultry science.

[16]  C. Ekstrand,et al.  Rearing conditions and foot-pad dermatitis in Swedish broiler chickens. , 1997, Preventive veterinary medicine.

[17]  Cécile Cornou,et al.  Classification of sows' activity types from acceleration patterns using univariate and multivariate models , 2010 .

[18]  A De Vries,et al.  Application of statistical process control charts to monitor changes in animal production systems. , 2010, Journal of animal science.