Support Vector Machine for Classifying the Quality of the Egg

based on the Color

Maimunah

, Rahmadya Trias Handayanto

and Taufiqur Rokhman

Department of Computer Engineering, Universitas Islam 45, Jl. Cut Meutia 83, Bekasi, Indonesia

Department of Mechanical Engineering, Universitas Islam 45, Jl. Cut Meutia 83, Bekasi, Indonesia

Keywords: Eggshell, color intensity, support vector machine

Abstract: Egg quality is an indicator to determine the freshness of an egg. One of a method to determine the egg quality

is through an analysis of eggshell condition. Color intensity of the eggshell comprises of three classes, i.e.

dark-brown, brown, and light-brown colors. This classification is based on the pigment concentration and the

structure of eggshell. Dark brown egg stronger and thicker than the light brown one. The quality of an egg

decreases faster for bright eggshell’s color. This research classified the quality of broiler eggs based on the

eggshell color using the support vector machine. This research used color red-green-blue (RGB) image of

ninety eggs sample. Feature extraction was used to calculate the RGB of each egg. Normalization was used

to get normalize RGB parameters (rgb) before classification through the use of support vector machine.

Classification result showed the accuracy of 80 percent.

1 INTRODUCTION

Boiler egg is a favorite food that popular as the source

of animal protein. Most of the people need this kind

of food to fulfill the need for animal protein. An egg

is cheap and easy to produce, yet it supplies the need

for protein. A fresh egg is an egg without freezing

treatment, preservation, as well as no embryo

development, mixed yolk with albumen, intact, and

clean (Badan Standarisasi Nasional, 2008). An egg

consisted of three main component: eggshell,

transparent fluid (albumen), and yellow fluid (yolk).

The egg can be contaminated with the

microbiology easily, physical broken, vapor and other

molecule intrusions; therefore, decreasing the quality

of an egg (Jazil, 2013). If this situation continues, the

egg’s weight will be decreased and the egg white

tends to more watery. The intrusions are also affected

by temperature, relative humidity, and eggshell

quality. Therefore, the eggshell quality should be

examined to ensure the freshness of an egg. Before

checking the quality of an egg, grading process is

needed to avoid any doubts as well as its quality

uncertainty and to get the homogenous egg.

The egg’s quality is checked through

analyzing the interior and exterior of an egg. External

analysis checks the eggs condition, whereas the

internal analysis checks the contents of the egg.

The Eggshell is the outer part of an egg

which has some benefits, i.e. avoiding the physical

and biological damage, gas circulation through

porous at eggshell. The eggshell is the strongest part,

smooth, lime coated, and bonds to the outer

membrane. The eggshell quality depends on the form,

smoothness, thickness, completeness, and cleanness

(Badan Standarisasi Nasional, 2008) . Thin eggshell

has thinner and more porous than the thick one, so

decreases the quality faster. As an information, the

thickness of eggshell is depended on the chicken

strain, parent’s age, stress, and disease. For an old

hen, the eggshell will thinner because it can produce

sufficient calcium for the eggshell (Sakroni et

al,2015).

Eggshell texture and its thickness are

decreased following the brightness of the eggshell

color. There is a significant correlation between

eggshell color and its thickness as well as the weight.

But no clear correlation between its weight, albumen

weight, egg yolk weight and color, Haugh unit, and

the Calcium in the albumen and egg yolk. Therefore,

some quality of an egg can be considered through

seeing the eggshell color (Yang et al. 2009)

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Maimunah, ., Trias Handayanto, R. and Rokhman, T.

Support Vector Machine for Classifying the Quality of the Egg based on the Color.

DOI: 10.5220/0009947630923098

In Proceedings of the 1st International Conference on Recent Innovations (ICRI 2018), pages 3092-3098

ISBN: 978-989-758-458-9

Eggshell color was captured based on the

color intensity and classified as dark brown, brown,

and light brown. Storage time and different eggshell

color affect its weight decreasing, Haugh unit value,

and the deep of air cavity. The dark brown eggshell

should be chosen since its quality decrease less than

the light brown based on storage time (Jazil, 2013).

Some disciplines, e.g. optic, mechanical,

electrical, acoustic, digital image processing, and

machine vision, are used to classify and find the

broken eggs. Image processing algorithms are used to

detect egg cleanness and blood spot by analysing the

maximum of its histogram as a criterion. This

algorithm use HIS color space to detect the cleanness

and the blood spot. The cleanness of eggshell is

detected through area detection method. The accuracy

of this research found the blood spot of 90.66% from

broken eggs and 91.33% from original eggs with

average accuracy of the algorithm was 91%

(Dehrouyeh et al. 2010).

Digital image processing has been widely

used in some applications, especially in computer

vision. Many image processing methods have been

implemented on robotic, object classification,

biometric system, medical visualization, image repair

and restoration, industrial inspection, and human-

computer interface (Ibrahim et al. 2012).

Many research on egg quality based on the

exterior of an egg. Crack detection of an egg can be

done with Susan edge detection and fuzzy

thresholding. These researches concluded that the

proposed algorithm outperformed Otsu and Power

Law algorithms. By adding the Gaussian to the input

image with variable between 0.002 and 0.001, the

accuracy were 97% and 82%. This algorithm has

minimum error (numbers of error pixels) less than

grey level image compared to the other algorithms

(Mansoory 2012)

The algorithm in HIS color space used

extract useful features of captured images of eggs by

machine vision to detect eggs defects. The algorithm

can also detect the severity of dirt on eggshell

(Dehrouyeh, 2010)

Classification through the use of support

vector machine (SVM) has been widely used in egg’s

parasite. Feature extraction using image processing

methods. Using a multi-class support vector machine

(MCSVM) the study gives the accuracy by 97.70%

(Avci and Varol, 2009). In addition, SVM was also

used to classify kinds and salted egg’s quality. This

method is reported when classifying the salted egg, it

achieved 81.25 % accuracy (Monro, 2013)

This study implemented the image

processing of boiler eggs and classifying them based

on eggshell’s color. The classification result can be

used to help in choosing the high-quality eggs.

2 METHODS

Figure 1 shows the research framework. Broiler eggs

were used with three classes, i.e. dark brown eggshell,

brown eggshell, and light-brown eggshell. A number

of images used for dark-brown, brown, and light-

brown were 30 eggs for each class.

Figure 1: Research framework.

Image acquisition step of the egg used DSLR

camera with 18 megapixel resolution. An egg was

placed in a box mini-studio with three 18 watt LED

light beams. Figure 2 shows the image acquisition

process.

Figure 2: Image acquisition.

The study used 90 eggs; each eggshell’s class of dark

brown eggshell, brown eggshell, and light-brown

eggs were 30 eggs. The images were saved as color

images/Red-Green-Blue (RGB) images.

In pre-processing step, the image cropping

and feature extraction were employed. Cropping was

camera

Support Vector Machine for Classifying the Quality of the Egg based on the Color

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done to get small part of sample. The color feature

was used in the study. Feature extraction was needed

to gather some important information from an image.

Color images have many RGB index value. Percent

different of RGB index causes the different color

representation, e.g. green, blue, etc. Higher color

index of an image shows a more bright color and less

color index shows the dark color. In this study the

three index values were separated and used as a

classification parameter. RGB index value of egg’s

image would be normalized to get zero to one range.

The normalization of RGB index value was

represented by r, g, and b variables as classification

parameters.

Classification is a process to decide the class

of an object. It uses the model with the ability to

classify an object based on its attribute. One of the

classification method is Support Vector Machine

(SVM).

SVM can be simply explained by the

searching of the best hyperplane (H) to separate two

input classes of the input space. Figure 3 and 4 shows

some patterns having two classes: +1 and -1. The

class “+1” is shown in blue/circle, whereas the “-1”

class is shown in red/rectangle. The classification

problem is the problem to find hyperplane that

separate two groups. Some hyperplanes can be found

as discrimination boundaries.

Figure 3: The lines are discrimination boundaries.

Figure 4 : Separator line between two classes.

After all the color of egg images have been

extracted, the training and testing should be done. The

SVM training was used to train the model in order to

have an ability to match an image, whereas the testing

step should be done to ensure that the model meets

the minimum accuracy. SVM is a supervised

classification method because it uses the target/label

in training step. SVM is a nonlinear mapping to

convert data training to a new higher dimension. This

new dimension searches the hyperplane that separate

data linearly. With this method every nonlinear map

can be separate linearly in its corresponding higher

dimension with the hyperplane. SVM finds the

hyperplane through the use of support vector in the

margin.

In this research, SVM was used to classify the

eggs based on the color of eggshell. The multiclass

SVM was used with tree method. This method

compares the eggshell with the brown and light

brown eggshell. The result was then compared with

the dark-brown eggshell. Classification results were

represented in a confusion matrixes that informs the

actual and prediction of classification (Table 1).

Table 1: Confusion matrix.

Where TN (True Negative) = correct prediction; FN

(False Negative) = Incorrect prediction; FP (False

Positive) = incorrect prediction; TP (True Positive) =

correct prediction

The Confusion matrix is used to validate the

model accuracy. Accuracy was calculated using

equation (1).

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accuracy  





(1)

3 RESULT AND DISCUSSION

This research use three classes of eggshell color:

dark-brown, brown, and light-brown (Figure 5,6, and

7).

Figure 5: Dark brown egg color.

Figure 6: Brown egg color.

Figure 7: Light brown egg color.

To capture RGB feature, the images were cropped

that only showed only the egg. Stages in feature

extraction shown in Figure 8.

Figure 8: Feature extraction.

Figure 9 shows an example of image cropping.

Figure 9: Image cropping.

After cropping, RGB index value are calculated from

egg images using Matlab as in Figure 9

Figure 10: Preprocessing interface.

Table 2 shows the sample of RGB index value.

Table 2: RGB index value.

Image Color index

Color index

Color

index B

172.5 103.323 69.6447

170.058 100.607 70.1535

183.387 122.463 86.0195

176.04 123.086 96.9643

173.329 113.645 83.6685

181.252 136.334 104.862

182.933 131.47 95.2249

179.842 135.13 102.496

179.255 129.697 92.686

176.685 135.973 105.899

183.739 146.791 125.038

184.055 141.964 107.968

188.654 147.752 112.281

194.17 147.393 114.906

187.564 138.92 103.508

Support Vector Machine for Classifying the Quality of the Egg based on the Color

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All RGB index value can be represented in a chart.

Figure 11 shows the distribution of RGB index value

for dark-brown eggshell, whereas Figure 12 and 13

show brown and light-brown eggshell respectively.

Based on distribution of RGB index value chart, it

shows clearly the different of dark-brown, brown, and

light brown RGB. Light brown egg has minimum

RGB range compared to brown and dark brown

eggshell color. It means that this class has minimum

different with the others.

Figure 11: The distribution of the dark-brown

eggshell RGB index.

Figure 12: The distribution of brown eggshell RGB

index.

Figure 13: The distribution of light-brown eggshell

RGB index.

RGB index value from previous process were

normalized to get the value between 0 and 1 that were

represented by r, g, and b variables. Normalization

result from RGB index in Table 2 can be seen in Table

Table 3: Feature extraction.

The characteristic of egg images used for

classification with r, g, and b parameters are shown in

Figure 14, 15, and 16.

Figure 14: The distribution of dark-brown eggshell

RGB index after normalization.

120

150

180

210

1 4 7 101316192225

Darkbrowneggshell

120

150

180

210

1 5 9 13172125

Browneggshell

120

150

180

210

1 5 9 13172125

Lightbrowneggshell

0.2

0.4

0.6

0.8

1 5 9 13 17 21 25

Darkbrowneggshell

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Figure 15: The distribution of brown eggshell RGB

index after normalization.

Figure 16: The distribution of light-brown eggshell

RGB index after normalization.

Based on distribution chart of R, G, and B as well as

the normalization result, the light-brown eggs had the

smallest RGB index distribution compared to brown

and dark-brown eggshells.

The next step was SVM classification that

consisted of training and testing phase. Training

phase used 25 eggs for each class. The total number

of eggs for classification was 75 eggs. Five eggs were

chosen as testing data for each class (15 eggs for all

classes). The distribution of all train data and testing

data can be shown in Figure 17 and 18.

Figure 17 : The distribution of train data.

Figure 18 : The distribution of testing data.

In classification phase, multiclass SVM was used.

By tree method, the brown and light brown are

compared. The winner would be compared with dark

brown egg that give the result whether an egg is

classified in dark brown, brown or light brown class.

To solve the classification problem, the separator line

should be prepared: i) between brown and light

brown, ii) between light brown and dark brown, and

iii) between brown and dark brown; these equations

are: svmStruct1,svmStruct2 and svmStruct3.

The procedures for training the SVM can be

summarized as follow.

First, the SVM classifier was generated through

the “svmtrain” function in Matlab. At the first stage,

the separation (svmStruct1 variable) was between

brown and light brown eggshell.

svmStruct1=svmtrain(train_data,target1,

'Showplot',true)

Second, the separation classifier, svmStruct1, then

compared with the dark-brown eggshell class. The

“svmStruct2” separation classifier with the same

function to svmStruct1:

svmStruct2=svmtrain(train_data,target2,

'Showplot',true)

Third, classification was comparing the brown

eggshell with the dark-brown eggshell (svmStruct3

variable):

svmStruct3=svmtrain(train_data,target3,

'Showplot',true)

The SVM equation that has been made is as follows:

svmStruct1 =

SupportVectors: [13x3 double]

Alpha: [13x1 double]

Bias: -1.2220

KernelFunction: @linear_kernel

KernelFunctionArgs: {}

GroupNames: [75x1 double]

0.2

0.4

0.6

0.8

1 5 9 13172125

Browneggshell

0.2

0.4

0.6

0.8

1 5 9 13172125

Lightbrowneggshell

0.2

0.4

0.6

0.8

1 11213141516171

0.2

0.4

0.6

0.8

13579111315

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SupportVectorIndices: [13x1 double]

ScaleData: [1x1 struct]

FigureHandles: []

vmStruct2 =

SupportVectors: [13x3 double]

Alpha: [13x1 double]

Bias: -1.2220

KernelFunction: @linear_kernel

KernelFunctionArgs: {}

GroupNames: [75x1 double]

SupportVectorIndices: [13x1 double]

ScaleData: [1x1 struct]

FigureHandles: []

svmStruct3 =

SupportVectors: [25x3 double]

Alpha: [25x1 double]

Bias: 1.0676

KernelFunction: @linear_kernel

KernelFunctionArgs: {}

GroupNames: [75x1 double]

SupportVectorIndices: [25x1 double]

ScaleData: [1x1 struct]

FigureHandles: []

Table 3 shows the confusion matrix of the testing

result.

Table 3: Confusion matrix.

Prediction

First

class

Second

class

Third

class

Actual

First class 5 0 0

Second class 0 2 3

Third class 0 0 5

From Table 3, it is observed that accuracy obtained is

80%. From the confusion matrix, the dark brown and

light brown eggs can be identified correctly. For the

brown eggs, as much as 60% are identified as light

brown eggs. This can be caused by almost the same

intensity of brown color. The difference between

brown and light brown is not much different

4 CONCLUSIONS

In this paper, the egg images are captured to identify

the class of chicken based on the color of their

eggshell. The color feature extraction using the

average value of RGB. SVM classifier is used to

identify the classification with the accuracy is 80%.

The lighter brown eggshell the faster deterioration

quality. Consumers are encouraged to choose dark

brown consumption eggs that have the lowest level of

degradation quality during storage. Future research

on the classification performance of several

classifiers will be conducted to find the best

classifiers.

ACKNOWLEDGEMENTS

The authors would like to thank the Directorate of

Research and Community Service of the Ministry of

Research, Technology and Higher Education who

have funded this research for the Beginner Lecturer

Research scheme for the 2018 fiscal year.

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