6th
International Science, SocialSciences, Engineering and Energy Conference
17-19 December, 2014, Prajaktra Design Hotel, UdonThani, Thailand
I-SEEC 2014
http//iseec2014.udru.ac.th
The classify of rubber sheet quality by image processing
with artificial neural network
P. Prabpala,e1, T. Manasrib,e2, S. Sonasangc,e3
a
Department of Electronics and Computer Technology, Faculty of Industrial Technology, LoeiRajabhat University,
Loei, 42000, Thailand
b
Mechatronics Research Unit, Faculty of Engineering, Machasarakham University, Maha Sara Kham, 44150, Thailand
c
Department of Electrical Engineering, Faculty of Engineering, Picayabundit College,
Nongbualamphu, 39000, Thailand
e1
eeono555@gmail.com* , e2 thosapol.m@msu.ac.th, e3somchat.so@pcbu.ac.th
Abstract
This research proposes to study a para rubber sheet classification by image processing with the artificial
neural network (ANN). In the proposed system, an image processing by ANN for classification of
qualities rubber sheet. The measurement of rubber sheet used into 4 levels with 100 samples, by
comparing them with a rule based on the expert and the training of data set from Loei province Thailand.
The result showed the accuracy of the proposed classification is 90 percent and will be discussed in the
following in article.
Keyword: Rubber Sheet, Image Processing,Artificial Neural Network
1. Introduction
Rubber trees plantations have been the primary and most popular profession of those in the south of
Thailand for a couple of decades, and now widely extended to the north and northeast, someone more
than in Loei province of the country [1]. Rubber is an important economic agricultural industrial crop in
Thailand because it has been the 1st ranking rubber exporter in the word[2-3].In 2010, Thailand has
exported 2.73 million tons of various form of rubber products accounting for approximately 39 %, most
products from Thailand are in the form of rubber sheet, which prices depends on their qualities. The
standards of rubber sheet quality defile in the Green Book published by the Rubber Manufacturers
Association Inc, Washington it make by the human visual inspection. When some human it hard work has
fatigues, requirement of expertise, low throughput, and lack of standardization [2].The problem of the
lack experts and monitoring systems quality of rubber sheets standards, so researchers had studied to
solve such problems by using image processing techniques. Obtaining quality of rubber sheets with
experts within10 seconds per sheet in the separation. The researchers studied the problem improvement
process was studied as follows. The image processing techniques have been used to detect the marks and
bubbles in the rubber sheet. Another problem was mark on the rubber is a problem in the separation
quality of the rubber offer a method to determine the white fungus [2,4]. In the rubber sheet by using the
characteristic colour of the mark from all areas. And then the method to detect and mark. Classification
using fuzzy techniques. Rubber is a five-level the classification[5].The researchers proposed air bubble
impurity solution by the air bubble is divided into 16 smaller areas [6].Then, the fractal dimension values
2
of these areas are calculated. Finally, the standard deviation of the 16 fractal dimensions is computed for
comparing with a predetermined threshold for classification [2].
Hence, this research proposes classification a Para rubber sheet by image processing with the artificial
neural network (ANN), the proposed system, an image processing by ANN for classification of qualities
rubber sheet. The measurement of rubber sheet used into 4 levels with 100 samples, by comparing them
with a rule based on the expert and the training of data set from Loei province Thailand.The paper is
organized as follows: Section II presents proposed methodology and experiment setup as well as
measured results will be presented in Section III. The paper is finally concluded in Section IV.
2. Methodology and Experimental setup
2.1 Imaging machines set
The imaging machines set used in the tests are creating to control the amount of light, and to test this
imaging machines set the exposure by setting the light within box with a width of 60 cm and a length of
120 cm and a height of 10 cm shown as Fig 1. The light up from the bottom of the box and the light bulbs
that illuminate the rubber sheet. And using the fluorescent lamp size 36 W 2 tubes depicts in Fig 1(b), for
to help spread the light throughout the rubber. Due to the collection of pictures to give a dark room to
make light of the external light shining through will affect the image. Ultimate by the rubber sheet of the
photo graph and put it on the glass, then the setting of camera with a timber used to store the image at a
distance of 90 cm, and photographic record then the calculations in mathematics program on computer.
Camera
Rubber set
Set boox
(a)
(b)
Fig. 1. (a) Schematic diagram of imaging machines set ; (b) imaging machines set
2.2 Testing sample
The sample of para rubber sheets used in this experiment was the Loei province with a size of
46´ 90cm2 . These samples were controlled to quality of the rubber, according to experts sorting quality
in the Loei province. That the samples of para rubber sheets were each of 4 different type of 4 grade
including: A (Very good) 25 samples, B (Good) 25 samples, C (Medium) 25 samples, and D (Poor) 25
samples as show as in Fig 2 grade-level of para rubber sheets.
3
2.3 Methodology
The methodology to test and develop the measurement setup on the classifies of rubber sheet quality
by image processing with artificial neural network is presented in Fig 3. The image archived on the box of
the sample test 100 samples; Grade A, B, C, and D of 25 samples of rubber sheets. During this step, the
classification of quality para rubber sheets based on analysis by in Matlab program to determine the grade
A
C
B
D
Fig 2. Sample test Grad level of para rubber sheets
of rubber sheets with ANN algorithm. Analysis grade of rubber sheets based on the image processing
with artificial neural network respectively, in order to compare the results from each image processing
with on ANN. After that, the measurement was performed on the according to experts sorting quality in
the Loei province. Then, the results of the measurement were compared with the experts sorting quality.
The ANN algorithm classification of the image processing based on the structure as shown as Fig 4.
The file images comes the mean color of the rubber sheet. All grades (the compiler 1).The file image
comes determine the color of the rubber sheet. All grades (the compiler 2). Translate into a binary file
open filters to determine the pattern of the rubber sheet all grades (3 variables). Variable in 3 training with
was learning in artificial neural network all grades of rubber sheets. Grading rubber sheet with the above
mentioned. Then find the correct grade rubber sheet with the actual grades from the experts, then the
percentage of correct. Then the results are presented on the experiment results and discussion.
4
St art
color of t he
rubber sheet
compiler 1
pat t ern of t he
rubber sheet
compiler 2
binary file
compiler 3
ANN
percent age compared t o t he mast er
END
Fig.3 Methodology of the classify of rubber sheet quality by image processing
x1
x2
x3
w1
w2
w3
wn
xn
Fig 4. Schematic diagram of ANN

5
3. Experimental results and discussion
Fig.5 as shown that the results of the grey scale (back-white) point the grad- level were grad A, B, C,
and D respectively. The results of a separate-grade black and white the grade A as shown as Fig 5 (a),
with the large volume of volume of the black more white levels rang 6.00E+06 to 1.40E+07 point
respectively. The grade B were black of levels rang 7E+06 to 9.2E+06 points more than white as
illustrated in Fig. 5(b). The grade C were black of levels rang 7.3E+06 to 8.1E+06 points which suggests
that the black and the white is not different a significant as illustrated in Fig. 5(c). And results of the grade
D were black-white levels in the range 1.5E+06 to 7.2E+07 points and then the black level is not very
different show in Fig. 5(d). But after process AAN separation qualities of the para rubber sheets then
determine grad level: A, B, C, and D. The extraction grad level by ANN then results found as shown in
Table 1; The results extraction accurate of the grad A, B, C, and D were 92% 88% 84% and 96
respectively.
1.60E+07
1.40E+07
1.20E+07
1.00E+07
8.00E+06
Back
6.00E+06
White
4.00E+06
2.00E+06
1.00E+07
9.00E+06
8.00E+06
7.00E+06
6.00E+06
5.00E+06
4.00E+06
3.00E+06
2.00E+06
1.00E+06
samples
0
1
3
5
7
Back
White
samples
0
1
9 11 13 15 17 19 21 23 25
3
5
7
9 11 13 15 17 19 21 23 25
(b)
(a)
8.00E+06
9.00E+06
8.00E+06
7.00E+06
7.00E+06
6.00E+06
6.00E+06
5.00E+06
5.00E+06
Back
White
4.00E+06
3.00E+06
Back
White
4.00E+06
3.00E+06
2.00E+06
2.00E+06
1.00E+06
1.00E+06
samples
0
1
3
5
7
9 11 13 15 17 19 21 23 25
samples
0
1
(c)
3
5
7
9 11 13 15 17 19 21 23 25
(d)
Fig.5 Results of the Grey scale by (a) A, (b) B, (c) D, and (d) D
Table 1. The extract grad levels use ANN
Grad level
A
B
C
D
Average
ANN (%)
92
88
84
96
90
Percent error (%)
8
12
16
4
10
6
When the comparison between the result of the accurate grad level use ANN and human expert form
25 samples at 4 level i.e. A, B, C, and D. These ANN extraction values of the average 90% all grad level
respectively and percent error of 10% as illustrated in Table 1. Therefore, the development of the imaging
machines set within extract using image processing based on ANN is the useful equipment for the user to
identify a quality and grad level (A B C D) obvious.
4. Conclusion
This article present separation quality of rubber sheets by image processing based on ANN. The
result can be extraction the grad level (A B C D) obvious. Then the comparison between the result of the
accurate grad level use imaging processing by ANN and human expert have the average 90%. The
technique give reduces the error separate using human expert of quality rubber sheets.
Acknowledgements
The authors are grateful to Faculty of Industrial Technology, Loei University for financially supported,
Faculty of Engineeging, Mahasarakham University, Maha Sarakham for supporting supported MatLab
program and measurement. Last but not least, thank you the Department of Electrical Engineering.
Faculty of Engineering, Pitchayabundit College.
References
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Dimension” IEEE International Symposium on Intelligent Signal Processing and Communication System (ISPACS), Decmber.
2011.
[3] Nujanart Kungpisdan et al., “Rubber Technical Document : Rubber’s Transform Industy,” Department of Agriculture,
Ministy of Agriculture and Cooperrative, 2010.
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technology Thonburi,1999.
[5] Sittichoke Unkeaw, Thanet kaorapapong, “Inspection of White mould on Surface Rubber Sheet Using Image Processing with
Color Characteristics Threshold Technique” ,EECON-29, November. 2006
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