Image analysis method to evaluate beak and head motion of broiler chickens during feeding

•Knowing precisely the jaw movement is essential for determining the optimum size of feed particles.•Detecting broilers feeding behaviour may help finding the right size of feed particles at all ages.•Using proper feed pellets size and composition will help minimizing feed wastage. While feeding bro...

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Veröffentlicht in:Computers and electronics in agriculture 2015-06, Vol.114, p.88-95
Hauptverfasser: Abdanan Mehdizadeh, S., Neves, D.P., Tscharke, M., Nääs, I.A., Banhazi, T.M.
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container_end_page 95
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container_start_page 88
container_title Computers and electronics in agriculture
container_volume 114
creator Abdanan Mehdizadeh, S.
Neves, D.P.
Tscharke, M.
Nääs, I.A.
Banhazi, T.M.
description •Knowing precisely the jaw movement is essential for determining the optimum size of feed particles.•Detecting broilers feeding behaviour may help finding the right size of feed particles at all ages.•Using proper feed pellets size and composition will help minimizing feed wastage. While feeding broiler chickens may exhibit different biomechanical movements in relation to the physical properties of feed such as size, shape and hardness. Furthermore, the chicken’s anatomical features at various ages, genders and breeds in conjunction with feed type and feeder design parameters may also have an influence on biomechanical movement. To determine the significance of these parameters during feeding, kinematic measurements related to the biomechanical motions are required. However, determining this information manually from video by a human operator is tedious and prone to errors. The aim of this study was to develop a machine vision technique which visually identifies the relevant biomechanical variables attributed to broiler feeding behaviour from high-speed video footages. A total of 369 frames from three broiler chicks of 5days old were manually measured and compared to the automatic measurement. For each bird six mandibulations (i.e. a cycle of opening and closing the beak) were manually selected, which were two different sequences of three consecutive mandibulations starting right after a feed grasping. The kinematics variables considered were: (i) head displacement (eye centre position; x- and y-axis); (ii) beak opening speed (given in mmms−1); (iii) beak closing speed (measured in mmms−1); (iv) beak opening acceleration (given in mmms−2); and (v) beak closing acceleration (given in mmms−2). Results indicated that the highest error for eye position detection was 1.05mm for x-axis and 0.67 for the y-axis. The difference between manual and automatic (algorithm output) measurements for the beak gape was 0.22±0.009mm, in which the maximum difference was 0.76mm. Regression analysis indicated that both measures are highly correlated (R2=99.2%). Statistical tests suggested that the primary probably causes of error are the speed and acceleration of the beak motion (i.e. blurred image), as well as the presence of feed particles in the first and second mandibulations right after the feed grasping (i.e. occluded beak tips by feed particles). The presented method calculated automatically the position of the eye centre (x- and y-axis) and both upper and lower beak tips
doi_str_mv 10.1016/j.compag.2015.03.017
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For each bird six mandibulations (i.e. a cycle of opening and closing the beak) were manually selected, which were two different sequences of three consecutive mandibulations starting right after a feed grasping. The kinematics variables considered were: (i) head displacement (eye centre position; x- and y-axis); (ii) beak opening speed (given in mmms−1); (iii) beak closing speed (measured in mmms−1); (iv) beak opening acceleration (given in mmms−2); and (v) beak closing acceleration (given in mmms−2). Results indicated that the highest error for eye position detection was 1.05mm for x-axis and 0.67 for the y-axis. The difference between manual and automatic (algorithm output) measurements for the beak gape was 0.22±0.009mm, in which the maximum difference was 0.76mm. Regression analysis indicated that both measures are highly correlated (R2=99.2%). Statistical tests suggested that the primary probably causes of error are the speed and acceleration of the beak motion (i.e. blurred image), as well as the presence of feed particles in the first and second mandibulations right after the feed grasping (i.e. occluded beak tips by feed particles). The presented method calculated automatically the position of the eye centre (x- and y-axis) and both upper and lower beak tips distance in a high level of accuracy, but the model can be improved by using a camera with higher resolution, a higher acquisition rate, and infrared-reflective markers. 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For each bird six mandibulations (i.e. a cycle of opening and closing the beak) were manually selected, which were two different sequences of three consecutive mandibulations starting right after a feed grasping. The kinematics variables considered were: (i) head displacement (eye centre position; x- and y-axis); (ii) beak opening speed (given in mmms−1); (iii) beak closing speed (measured in mmms−1); (iv) beak opening acceleration (given in mmms−2); and (v) beak closing acceleration (given in mmms−2). Results indicated that the highest error for eye position detection was 1.05mm for x-axis and 0.67 for the y-axis. The difference between manual and automatic (algorithm output) measurements for the beak gape was 0.22±0.009mm, in which the maximum difference was 0.76mm. Regression analysis indicated that both measures are highly correlated (R2=99.2%). Statistical tests suggested that the primary probably causes of error are the speed and acceleration of the beak motion (i.e. blurred image), as well as the presence of feed particles in the first and second mandibulations right after the feed grasping (i.e. occluded beak tips by feed particles). The presented method calculated automatically the position of the eye centre (x- and y-axis) and both upper and lower beak tips distance in a high level of accuracy, but the model can be improved by using a camera with higher resolution, a higher acquisition rate, and infrared-reflective markers. 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While feeding broiler chickens may exhibit different biomechanical movements in relation to the physical properties of feed such as size, shape and hardness. Furthermore, the chicken’s anatomical features at various ages, genders and breeds in conjunction with feed type and feeder design parameters may also have an influence on biomechanical movement. To determine the significance of these parameters during feeding, kinematic measurements related to the biomechanical motions are required. However, determining this information manually from video by a human operator is tedious and prone to errors. The aim of this study was to develop a machine vision technique which visually identifies the relevant biomechanical variables attributed to broiler feeding behaviour from high-speed video footages. A total of 369 frames from three broiler chicks of 5days old were manually measured and compared to the automatic measurement. For each bird six mandibulations (i.e. a cycle of opening and closing the beak) were manually selected, which were two different sequences of three consecutive mandibulations starting right after a feed grasping. The kinematics variables considered were: (i) head displacement (eye centre position; x- and y-axis); (ii) beak opening speed (given in mmms−1); (iii) beak closing speed (measured in mmms−1); (iv) beak opening acceleration (given in mmms−2); and (v) beak closing acceleration (given in mmms−2). Results indicated that the highest error for eye position detection was 1.05mm for x-axis and 0.67 for the y-axis. The difference between manual and automatic (algorithm output) measurements for the beak gape was 0.22±0.009mm, in which the maximum difference was 0.76mm. Regression analysis indicated that both measures are highly correlated (R2=99.2%). Statistical tests suggested that the primary probably causes of error are the speed and acceleration of the beak motion (i.e. blurred image), as well as the presence of feed particles in the first and second mandibulations right after the feed grasping (i.e. occluded beak tips by feed particles). The presented method calculated automatically the position of the eye centre (x- and y-axis) and both upper and lower beak tips distance in a high level of accuracy, but the model can be improved by using a camera with higher resolution, a higher acquisition rate, and infrared-reflective markers. The method has the potential to facilitate efficient and repeatable acquisition of biomechanical data of broiler chickens during feeding, and be used to benchmark the feed physical properties and its processing methods, likewise evolving knowledge for futures studies in feeders’ design.</abstract><pub>Elsevier B.V</pub><doi>10.1016/j.compag.2015.03.017</doi><tpages>8</tpages></addata></record>
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1872-7107
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subjects Acceleration
Biomechanics
Chickens
Eating behaviour
Feeding
Grasping
High-speed camera
Image analysis
Jaw apparatus
Mathematical models
Physical properties
Poultry
title Image analysis method to evaluate beak and head motion of broiler chickens during feeding
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