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Machine vision to identify broiler breeder behavior

Danilo F. Pereira a,⇑, Bruno C.B. Miyamoto a,1, Guilherme D.N. Maia b,2, G. Tatiana Sales b,Marcelo M. Magalhães a,1, Richard S. Gates b,3

a School of Business, Univ. Estadual Paulista – UNESP at Tupã, 780 Domingos da Costa Lopes Av., 17602-496, Tupã, SP, Brazilb Department of Agricultural and Biological Engineering, University of Illinois at Urbana-Champaign, 1304 W. Pennsylvania Avenue, Urbana, IL 61801, USA

a r t i c l e i n f o

 Article history:Received 15 February 2013

Received in revised form 23 August 2013

Accepted 18 September 2013

Keywords:

Image analysis

Poultry

Data mining

Precision agriculture in animal production

a b s t r a c t

Animal behavioral parameters can be used to assess welfare status in commercial broiler breeders.Behavioral parameters can be monitored with a variety of sensing devices, for instance, the use of video

cameras allows comprehensive assessment of animal behavioral expressions. Nevertheless, the develop-

ment of efficient methods and algorithms to continuously identify and differentiate animal behavior pat-

terns is needed. The objective this study was to provide a methodology to identify hen white broiler

breeder behavior using combined techniques of image processing and computer vision. These techniques

were applied to differentiate body shapes from a sequence of frames as the birds expressed their behav-

iors. The method was comprised of four stages: (1) identification of body positions and their relationship

with typical behaviors. For this stage, the number of frames required to identify each behavior was

determined; (2) collection of image samples, with the isolation of the birds that expressed a behavior

of interest; (3) image processing and analysis using a filter developed to separate white birds from the

dark background; and finally (4) construction and validation of a behavioral classification tree, using

the software tool Weka (model J48). The constructed tree was structured in 8 levels and 27 leaves,

and it was validated using two modes: the ‘‘set training’’ mode with an overall rate of success of 

96.7%, and the ‘‘cross validation’’ mode with an overall rate of success of 70.3%. The results presented here

confirmed the feasibility of the method developed to identify white broiler breeder behavior for a partic-

ular group of study. Nevertheless, more improvements in the method can be made in order to increase

the validation overall rate of success.

 2013 Elsevier B.V. All rights reserved.

1. Introduction

Animal behavior is a parameter commonly used to assess ani-

mal welfare. Behavior was first strictly defined as movements per-

formed by a living organism. However, other living expressions can

be interpreted as behavior signals and are not characterized by

movements. Vocalization and sounds, color changes, odors, and

production are examples of more complex living expressions

incorporated into the definition of behavior (Costa, 2003).

Animal behavior is a potential tool to identify welfare status of 

broiler breeders in commercial production systems (Prayitno et al.,

1997; Archer et al., 2009; Shields et al., 2005; Salgado et al., 2007).

Several types of sensing devices have been applied to monitor ani-

mal behavior. For example, electronic identification transponders

(EIDs) (Curto et al., 2007) and video cameras (Pereira et al.,

2001). In particular, video cameras are advantageous because they

allow thorough assessment of animal behavioral expressions

(Pereira, 2005; María et al., 2004; Sevegnani et al., 2005). Further,

video cameras can potentially generate more reliable data owing to

its non-invasive nature during data collection.

An alternative to improve behavioral analysis relies on the

development of a computer vision system to automatically recog-

nize body shapes assumed by the birds when expressing different

behaviors from recorded videos (Sergeant et al., 1998). Computer

vision has been widely used in various processes of different ani-

mal production systems. It includes the automation of milking sys-

tems for dairy (Rossing and Hogewerf, 1997), robotic systems for

beef cattle management (Frost et al., 2000), and to estimate body

mass of pigs (Minagawa and Iechikawa, 1992). Broiler breeder

0168-1699/$ - see front matter   2013 Elsevier B.V. All rights reserved.http://dx.doi.org/10.1016/j.compag.2013.09.012

⇑ Corresponding author. Tel.: +55 14 3404 4200.

E-mail addresses: danilo@tupa.unesp.br (D.F. Pereira), miyamototup@gmail.com

(B.C.B. Miyamoto),   gdnmaia@gmail.com   (G.D.N. Maia),   sales1@illinois.edu

(G. Tatiana Sales),  marcelo@tupa.unesp.br  (M.M. Magalhães),  rsgates@illinois.edu

(R.S. Gates).1 Tel.: +55 14 3404 4200.2 Tel.: +1 (859) 608 7570.3 Tel.: +1 (217) 244 2791; fax: +1 (217) 244 0323.

Computers and Electronics in Agriculture 99 (2013) 194–199

Contents lists available at  ScienceDirect

Computers and Electronics in Agriculture

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management is critical to the broiler production chain because

housing conditions will affect oviposition, egg quality, hatching

rate, and chicks’ thermotolerance (Yahav et al., 2001).

The objective of this study was to develop a behavioral classifi-

cation method for white broiler breeders using a novel machine vi-

sion technique method that incorporates datamining in the

analysis. The hypothesis of this work is that computer vision com-

bined with datamining analysis will aid the identification of broilerbreeder behavioral expressions. The objective was achieved with

the following steps: (a) selection of known broiler breeder behav-

ior expressions from body shape images, (b) collection of image

samples from a group of birds, (c) image processing and analysis

and (d) use of data mining and computer vision techniques to con-

struct and validate a behavioral classification tree.

2. Materials and methods

The data utilized in the methods presented here derived from

videos recorded from experiments performed in an environmen-

tally controlled chamber. Ten Hybro-PG broiler breeders were

placed in the chamber and exposed to three levels of temperature

(26, 29, and 35 C). For each temperature, the relative humiditywas kept between 60% and 70%. The floor was covered with shav-

ing bedding. Wooden nests, a tubular feeder, and bell drinkers

were installed in the chamber. A video camera with capacity of 

registering 16-frames-per-second was installed down-faced from

the ceiling and used to monitor the birds behavior during the

experiment period.

This study is divided into four sessions: (1) studying body

shapes assumed by broiler breeders when expressing certain

behaviors using sequence of frames, (2) collecting image samples,

(3) processing and analyzing images, and (4) constructing and val-

idating a behavioral classification tree.

 2.1. Behavior expression identification using sequences of image

 frames

Videos were recorded to capture various behavior expressions

as described by in the ethogram below (Table 1). The choice of 

the behavior types was based on the high frequency of occurrence

of these behaviors in the video. Nest visits were also observed,

however, the birds were out of the coverage of the camera during

the expression of this behavior, hence, this behavior could not be

analyzed using the proposed methods.

The video analysis consisted of defining the number of frames

necessary to complete or to identify each behavior described in Ta-

ble 1. Videos with the birds were recorded and analyzed using im-

age processing and image analysis (as explained below), until a

behavior expression was defined. Each type of behavior and num-

ber of frames were determined from ten independent samples. The

frames allowed the observation of sequence of body shape changes

that occur when the birds express the selected behaviors.

 2.2. Collecting image samples

A sample size n  = 30 was chosen for analysis for each behavior.Each sample unit was comprised by the number of frames deter-

mined using the method of Section 2.1. During video analyses, se-

quences of frames were collected every time a broiler breeder was

identified expressing any of the behaviors described in  Table 1. A

sequence of frames starts at the beginning of each behavioral

expression thus composing a sample of that specific behavior.

 2.3. Image processing and analysis

The method of image processing is critical to increase measure-

ment precision and analysis reliability. Images were processed

with white broiler chickens on a dark background defined by wood

shavings bedding. However, feathers and other objects in the envi-

ronment such as nests, feeders, and drinkers are often perceived asbirds, which can compromise correct classification of behavior. Im-

age processing was applied to improve and brighten up bird

images, prior to the image analysis, as a measure to reduce the

number of objects mistakenly identified as birds, and explained

below.

 2.3.1. Image processing 

The image sample used for the behavior analysis was subjected

to a five-step image processing. The procedure was performed to

increase the contrast of the image in study from its background.

Objects that were mistakenly classified as birds were removed,

or minimized in order to emphasize the contour of the birds.

First step: Images were converted from RGB (Red, Green, and

Blue) to the color system HSI (Hue, Saturation, and Intensity).Hue (H) represents a specific color; Saturation (S) represents how

pure or saturated a color is when compared to white; and Intensity

(I) represents the brightness of a color. Thus, the color system HSI

is appropriate for intensifying contrast of colored images since H, S,

and I can be processed separately. In the RGB system, on the other

hand, these components are combined together (Predirni and Sch-

wartz, 2008).

Second step: Each pixel of the bands R, G, B, S, and I was normal-

ized to the interval [0,1]. Once normalized, the pixels were then

represented by the letters  r ,  g ,  b,  s , and  i , respectively. According

to Polidorio et al. (2005), normalization of pixels is necessary for

performing mathematical operations on pixel matrices.

 Table 1

Ethogram of behaviors observed in the experiments.

Behavior Description Response variable

Wing spreading Birds spread their wings wide open Frequency of occurrence

Bristling Birds bristles their plumage and move their body laterally Frequency of occurrence

Drinking Birds approach drinkers and drink water Length of behavioral expression and frequency of  

occurrence

Scratching Birds explore their surroundings by scratching the ground with their feet and

beaks

Length of behavioral expression and frequency of 

occurrence

Resting Birds stay in resting position (lying or sitting) Length of behavioral expression and frequency of  

occurrence

Stretching Birds stretch out a wing and a leg of the same side of the body Frequency of occurrence

Preening Birds contort to reach their uropygial glands and groom their feathers with their

beaks

Length of behavioral expression and frequency of 

occurrence

Mounting A bird climbs another bird’s back Frequency of occurrence

Inactivity Birds stand on their feet without executing any of the activities described above Length of behavioral expression and frequency of 

occurrence

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Third step: White regions in the images were contrasted out

from the background and defined as possible chickens by using

Eq.  (1). This equation was adapted from the study of   Polidorio

et al. (2005) who adopted a similar relationship to contrast clouds

in satellite images.

ck ¼  2  i  s    1 r 

4

 ð1 bÞ

2  ð1Þ

The deduction of the inverse of  r  and b  in the previous relation-

ship allows the subtraction of shapes such as bedding (which al-

ready shows saturation higher than intensity) from high values.

It also allows the subtraction of low values from lighter regions

(which show low saturation and high intensity). Therefore, apply-

ing Eq. (1)yields positive values for lighter regions (possible chick-

ens) and negative values for the remainder regions.

Fourth step: A binary-like image was obtained with positive pix-

els (possible chickens) converted to 1, and the remainder negative

pixels converted to 0.

Fifth step: The last step was characterized by ‘‘purging’’ the

lighter regions that did not represent chickens and that had small

area, by using the erosion-dilation technique (Predirni and Sch-

wartz, 2008). During the erosion-dilation process, a structuringelement was defined and acted on the contours of regions with

pixels equal to 1. First, the contour was eroded by the size of the

structuring element. Then, the same contour was dilated to the size

of the structuring element. This technique is efficient to eliminate

contour imperfections and regions that are not of interest (smaller

than the structuring element).

 2.3.2. Image analysis

Image analysis is a process that falls between image processing

and computer vision, aiding the identification of the shapes of the

objects in study. Once images were processed as described previ-

ously, the following measurements of the regions corresponding

to chickens were extracted: Area ( A) calculated by summing pixels

within a contour which constitutes a chicken; Perimeter (P )calculated by summing pixels that were different from 1 which

constitute the contour; and, the Maximum (Dmax) and the Mini-

mum (Dmin) are the distances between the center of mass and

the perimeter.  Fig. 1  shows the position of each of the measure-

ments extracted from lighter regions (chickens).

The shape coefficients were then calculated by entering the

parameters extracted from the images into Eqs.  (2)–(4). The ap-

proach is somehow different than the usual approach since it uses

the maximum and minimum distance from the center of mass tothe image perimeter.

CC  ¼  P 

2

4 p  A  ð2Þ

CCmax ¼ p  Dmax2

 A  ð3Þ

CCmin ¼ p  Dmin

2

 A  ð4Þ

From the variables recorded in each frame, we computed the

rates of variation of individual and combined variables, including

distance and velocity variables used to describe movements. Once

measurements were extracted from image samples, shape coeffi-cients were calculated and measurements related to the analysis

of sequence of frames were obtained (Table 2).

 2.4. Constructing and validating a behavioral classification tree

The parameters obtained with image analysis comprised a

supervised dataset, where each set of measurements was associ-

ated to a class (behavior). The dataset was generated from the vi-

deo analysis described in the introduction of Section   2. Each

registry was formed with the values of the variables described in

Table 1. These values were calculated for each sequence of frames

determined for each behavior. Trends in the data were investigated

by means of data mining techniques in order to create a classifica-

tion tree. For that, the model J48 of supervised classification wasused in Software Weka version 3.4.11. Two training algorithms

Fig. 1.   (a) Original image amplified, (b) processed binary color image, (c) Birds contour boundaries and (d) parameters used to describe a chicken drinking water.

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were adopted to validate the tree model: cross validation and set

training.

Cross validation is a suitable statistical method used to validate

an experimental model. This method was applied to a dataset di-

vided into ten parts. Nine parts were selected to generate one iter-

ation model, and the remaining tenth part was used for validation.

The processed is completed when each part was selected to vali-

date the nine-part model, for a total of ten iterations or validation

tests. Ideally, it is expected that the model can be extrapolated to

other datasets with precisions comparable to the precision esti-

mates obtained with this model.

Set training is a method that utilizes a dataset to generate a

model, using the same dataset in the validation process, for only

one iteration. A higher precision is expected in this model (lower

degrees of freedom) if compared to the cross-validation method.

However, a limitation this is that the model was applied to the

same birds, subjected to similar experimental conditions of illumi-

nation. This method was included as a way to compare two ac-

cepted parametric validation methods.

3. Results and discussion

The number of frames required to identify each behaviors was:

wing spreading (2), bristling (4), drinking (2), scratching (5), rest-

ing (4), stretching (4), preening (4), mounting (2), and inactivity

(3). Taking as an example the behavior ‘‘preening’’, each of its sam-

ples was comprised by a sequence of frames formed by four images

of the same bird expressing that behavior, as shown in Fig. 2.

All behaviors had a sample size n  = 30 except for stretching  and

mounting  with sample sizes   n = 20 and   n  = 12, respectively. This

discrepancy was owing to a lower frequency of appearance of 

those particular behaviors in the videos watched.

The classification tree (Fig. 3) yielded 8 levels and 27 leaves. The

validation of the classification tree was performed in ‘‘cross

validation’’ and in ‘‘set training’’ modes. The same data obtained

from the sampled images that generated the classification tree

were used for its validation in the ‘‘set training’’ mode resulting

in a behavioral classification rate of success of 96.7%.

Most of the behaviors were classified according to the class they

belonged to, however, some classification errors were observed.

The behavior ‘‘preening’’, for instance, had 28 samples classified

correctly out of 30 samples taken. The remainder two samples

were incorrectly identified as ‘‘resting’’ and ‘‘bristling’’. Moreover,

one sample from the behavior ‘‘bristling’’ and one sample from

the behavior ‘‘stretching’’ were incorrectly classified as ‘‘preening’’

(see Table 3).

Data were divided into ten exclusive partitions of approxi-

mately the same size in the ‘‘cross validation’’ mode. One of the

partitions is used for validation whereas the remainder partitions

are used to generate the classification tree. This procedure is exe-

cuted ten times – with a different partition each time for validation

(Meira et al., 2008). The overall rate of success in this validation

mode was 70.25%, using ten data partitions. Table 4 shows a higher

occurrence of classification errors. The behavior ‘‘preening’’, for in-

stance, that had some samples incorrectly classified as ‘‘resting’’

and ‘‘bristling’’ in the ‘‘set training’’ mode, had samples also mis-

taken by ‘‘stretching’’ and ‘‘scratching’’ in the ‘‘cross validation’’

mode. In the latter, only 14 out of the 30 samples taken were clas-

sified correctly.

Computer vision image classification has been used in

several applications (Silva et al., 2004; Su et al., 2006). However,

the identification or correct classification of shapes in

situations where it is difficult to distinguish between colors be-

comes a challenge for computer vision systems (Duan et al.,2007).

Selecting a bird is a challenge during the identification of indi-

vidually expressed chicken behaviors, because very often the bed-

ding is ‘‘dirty’’ and covered in feathers. In cases like this, the

 Table 2

Measurements extracted from the shapes assumed by the birds when expressing the behaviors in study.

Measurements Description Measurements Description

 Aave   Average area   CC sd   Standard deviation of shape coefficient

P ave   Average perimeter   Ccmaxsd   Standard deviation of maximum shape

coefficient

Dmaxave   Average maximum distance between center of mass and perimeter   CCminsd   Standard deviation of minimum shape

coefficient

Dminave   Average minimum distance between center of mass and perimeter VA ¼  A frameN  A frame1N  Area gain velocity

CC ave   Average shape coefficient VP ¼P  frameN P  frame1

N Perimeter gain velocity

CC maxave   Average maximum shape coefficient VDmax¼Dmax frameN Dmax frame1

N Maximum distance increase velocity

CC minave   Average minimum shape coefficient VDmin¼Dmin frameN Dmin frame1

N Minimum distance increase velocity

 Asd   Standard deviation of area VCC ¼CC  frameN CC  frame1

N Coefficient of shape convergence velocity

P sd   Standard deviation of perimeter VCCmax ¼CCmax frameN CCmax frame1

N Maximum coefficient of shape convergence

velocity

Dmaxsd   Standard deviation of the maximum distance between center of mass

and perimeterVCCmin¼

CCmin frameN CCmin frame1

N Minimum coefficient of shape convergence

velocity

Dminsd   Standard deviation of the minimum distance between center of mass

and perimeter

Dmin/Dmax   Minimum and maximum distances ratio

N  is the number of frames of each analyzed behavioral sample.

Fig. 2.  Sequence of frames that comprises one of the ‘‘preening’’ behavioral samples: (a) frame 1, (b) frame 2, (c) frame 3, and (d) frame 4.

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algorithm must identify only larger shapes after image processing

(Chen et al., 2002), which will eliminate any strange objects in the

scene. When it comes to image analysis, it is necessary to investi-

gate significant and differentiable shape parameters related to

each behavior, which usually present repeatable and similar

shapes.

The methodology adopted to analyze measurements extracted

from shapes in sequence of frames has proven successful. A classi-

fication tree of behaviors expressed by broiler breeders has beengenerated with a rate of success higher than 87% (Su et al., 2006)

for detecting contaminants in wool, and higher than 90% (Shao

and Xin, 2007) for classifying images of pigs.

4. Conclusions

The methodology developed to identify broiler breeder’s behav-

iors by means of machine vision techniques has shown a rate of 

success higher than 70% for both validation methods adopted. Nev-

ertheless, the overall rate of success could be improved if the videoillumination could be better controlled. It was observed that the

illumination affected the selection of image processing methods

used to differentiate the birds from other objects and background.

Hence, improved illumination techniques would ameliorate the

process of differentiation of the birds from the background, which

would, in turn, increase the precision of the applied methods. An-

other factor that would improve identification precision is the

development of methods to track bird trajectory. For example,

the use of trajectory tracking methods for drinking and eating

behaviors implemented in areas where these behaviors occur with

high frequency.

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Classification tree’s confusion matrix (‘‘set training’’ mode).

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