DETEKSI INDEKS KEMATANGAN BUAH MANGGIS MENGGUNAKAN METODE SUPPORT VECTOR MACHINE

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This article has been presented at ISTTH 2016 and will be published at Acta Horticulturae

2. Draft Publikasi Ilmiah

Optimized estimation of mangosteen maturity stage using SVM and color features

combination approach

Slamet Riyadi

1a

, Afrizal Zuhri

2

, Tony K. Hariadi

2

, Indira Prabasari

3

, Nafi Ananda Utama

3 1

Dept. of Information Technology, Universitas Muhammadiyah Yogyakarta, Indonesia

2

Dept. of Electrical Engineering, Universitas Muhammadiyah Yogyakarta, Indonesia

3

Dept. of Agrotechnology, Universitas Muhammadiyah Yogyakarta, Indonesia

Abstract

Estimation of maturity stage of mangosteen during harvest is critical because it

affects the quality of mangosteen. Currently, the estimation is manually performed

by labor using visual method.

Since the visual method is not precise and

consistent, the use of image processing technology promises a better result. The

objective of this research is to develop an image processing method to optimize

estimation of mangosteen maturity stage by combining Red-Green-Blue (RGB) color

features and implementing Support Vector Machine. The methodology involves fruit

collection, image acquisition and image processing step. In the image processing

step, color features i.e. sum, mean and standard deviation of R, G and B component

were extracted from images. These features were then combined and used as

parameter input for SVM training-testing. The proposed method yielded a

significant improvement on maturity stage estimation and was able to increase its

accuracy up to 91% using combination of nine features.

Keywords: mangosteen, maturity stage, image processing, features extraction, SVM

INTRODUCTION

Mangosteen (

Garcinia mangostana

L.) is a tropical fruit and a major commodity for

Indonesia export. In 2013, mangosteen export contributed about USD 5.73 million which

was the highest contribution of fruit export followed by salak (

Salacca zalacca

) and

mangos. Ministry of Trade, Indonesia, also records about 153% of quantity increment of

mangosteen export or equal to USD 13.70 in January

–

Mei 2014 compared to the same

period in the previous year. Mangosteen from Indonesia have been exported to Thailand

(42%), Malaysia (25%), Hongkong (22.3%), Uni Emirate (3%) and Singapore (2.2%)

(Kemendag 2015). Currently, only about 11% of the total mangosteen products are then

exported whereas the export price could be 5

–

8 times compared to domestic. This low

quantity of export for mangosteen occurred due to the low quality of fruit.

Estimation of maturity stage during harvest is very critical since it affects the quality

of mangosteen. If the fruit is harvested too early before its ripeness, it will not be

perfectly mature. However, if the mangosteen is harvested overripe, it cannot be exported

due to export time constraint (Palapol et al. 2009; Paull & Ketsa 2004). Nowadays,

mangosteen is selected for harvest by means of visual estimation performed by labor.

This maturity stage estimation is not precise and consistent, especially for long duration

a

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and high quantity of harvest. Estimation results in the early morning could be different

with results on the afternoon. Different labor also performs different estimation results.

Various image processing technologies had been proposed to overcome manual

methods performed by labor for any task during harvest and postharvest. Researchers

proposed quality evaluation and grading of fruit and vegetables such as apple (Bennedsen

et al. 2005), star fruit (Abdullah et al. 2006), strawberry (Liming & Yanchao 2010),

orange (Aleixos et al. 2002), papaya (Riyadi et al. 2007a; Riyadi et al. 2007b; Riyadi et

al. 2007c) and potatoes (Barnes et al. 2010). Almost all of fruits inspected are from North

America, Europe and East Asia, where only few tropical fruits are studied (Zhang et al.

2014).

Few researchers reported their results of image processing application to estimate the

maturity stage of mangosteen. Since the maturity estimation is based on skin surface color

of mangosten, all of researchers use color processing to differentiate among the stages.

Color features which are color index of red (R), green (G) and blue (B), were

conventionally correlated to four stages of mangosteen maturity and then yielded 70%

accuracy of maturity stage estimation (Suhandy & Ahmad 2003). Another method

proposed RGB score on three maturity stages and used this score as threshold values to

estimate the maturity stage of sample mangosteen (Sandra 2011). Since these two

methods implemented simple classification technique, consistency of the results highly

depend on the selection of data and threshold score. Selection and combination of RGB

color features are important to uniquely represent the maturity stage.

Advanced classification technique, namely artificial neural network, was then used to

increase the estimation accuracy. This technique used some color features such as mean

of RGB component, hue-saturation-intensity (HSI), energy, entropy, contrast and

homogeneity as parameter inputs for training-testing of neural network (Whidhiasih et al.

2012). Instead of artificial neural network for classification task, a modern method, that is

support vector machine (SVM), is widely used for various applications. The SVM was

implemented to identify skin defect of citrus and performed 96% accuracy for defect and

non-defect fruit (Khoje et al. 2013). This method also had a good performance to evaluate

2-4 objects per second which were moving on high speed conveyor belt (Weyricha et al.

2012). In many cases, the SVM method outperformed the artificial neural network (Shao

& Lunetta 2012).

Estimation of mangosteen maturity stage using image processing method is

promising. Therefore, the objective of this research is to optimize the maturity estimation

by combining features of RGB color component and implementing the SVM. Three RGB

features namely sum, mean and standard deviation of R, G and B component were used

and combined to create features sets. The best combination that produces the best

estimation accuracy was also studied. According to Ministry of Agriculture, Indonesia,

six stages of maturity standard is used in this research.

METHODS

This research was conducted in Laboratory of Postharvest, Department of

Agrotechnology and Laboratory of Advanced Multimedia, Department of Information

Technology, Universitas Muhammadiyah Yogyakarta. It involved several steps i.e. fruit

collection, image acquisition and image processing.

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Mangosteen fruits were collected from a mangosteen farm in Purworejo, Central of

Java, Indonesia. Every mangosteen was then manually classified to the respective stage

according to its color. This manual classification is the ground-truth which will be

compared to the classification by the proposed method. For initial research, a number of

24 mangosteens were selected for further step which are consisting four mangosteens per

stage.

Image acquisition

The next step is image acquisition to obtain images of fruit using equipment setup

shown in

Figure 1. A lighting chamber equipped with lamps was used to control and make a

uniform intensity of light. A digital camera was prepared in front of the open side of

chamber. A tripod was also used to adjust position and direction of camera properly. The

fruit was then put inside the chamber and the acquisition of images could be performed.

To obtain the whole surface of mangosteen, the fruit was rotated 90

o

and captured every

rotation.

90o Lighting chamber

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Figure 1. Equipment setup for image acquisition consists of lighting chamber and digital

camera with tripod. Mangosteen was rotated 90

o

to obtained whole surface

images.

Image processing

Images obtained from the previous step were then processed using Matlab software.

The image processing task consists of two steps, i.e. color features extraction and SVM

training-testing. In the extraction of color features, three features were computed, namely

sum, mean and standard deviation of R, G and B component, or in short SRGB, MRGB

and SDRGB, respectively. It should be noted that every feature related to R, G and B

component. Therefore, the total features produced are nine. These nine features were then

combined to create features sets containing six features (SRGB & MRGB, SRGB &

SDRGB and MRGB & SDRGB) and nine features (SRGB, MRGB & SDRGB). Every

feature set was used as parameter input for SVM training and testing.

RESULTS AND DISSCUSION

From the fruit collection, a number of 24 mangosteens were selected and they

consisted of four mangosteens per stage. In the images acquisition, every mangosteen was

rotated 90

o

and captured using digital camera. Therefore, a total of 96 images were

produced from the images acquisition steps.

Three features (sum, mean and standard deviation) were computed from R, G and B

component then produced nine features. For instance, Figure 2 shows scatter plot of mean

of R, G and B component that shows distribution of features for every image. It can be

seen that the scatter plot of features for sum of R and G are similar. These features

produce a pattern that is able to differentiate between stage 1, 2 and 3, while pattern for

90o Lighting chamber

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stage 4, 5 and 6 are overlap each other. From Figure 2(c), it can be seen that scatter plot

of sum of B does not indicate good distribution to differentiate among stages. However,

based on RGB color theory, a color pixel does not only depend on one or two color

components but depend on all of three color components. Therefore, all of features are

appropriate for SVM parameter input. Similar discussion is also applied on the sum and

standard deviation of R, G and B.

Figure 2. Scatter plot of mean of (a) red (R), (b) green (G) and (c) blue (B) from the

images

Nine color features obtained were then used as parameter input for SVM

training-testing. A half of the total images were selected as training data and the rest as the testing

data for SVM. Tabel 1 shows the accuracy result of maturity stage estimation for every

features set. It can be seen that the highest accuracy was obtained by combining all of

nine features those are sum, mean and standard deviation of R, G and B component.

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Tabel 1. Accuracy of maturity stage estimation

Features set

SRGB &

MRGB

SRGB &

SDRGB

MRGB &

SDRGB

SRGB,

MRGB &

SDRGB

Accuracy

83%

87%

91%

CONCLUSION

The development of image processing method by combining color features and

implementing SVM classification method to optimize estimation of maturity stage of

mangosteen has been discussed. The color features i.e. sum, mean and standard deviation

of R, G and B component were extracted from images. The combination of sum and

mean, sum and standard deviation, mean and standar deviation and all of nine features

were used as parameter input for SVM training and testing. The result confirmed that the

combination of nine features is able to increase the accuracy of estimation up to 91%.

ACKNOWLEDGEMENT

Authors would like to thank Universitas Muhammadiyah Yogyakarta, Indonesia, for

the research fund (Cluster Research Scheme

–

ABT 2015) and the publication support

(Internationalization Program

–

UMY 2016).

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Non destructive method for maturity index determination of Garcinia mangostana L using image processing technology

Indira Prabasari1, Nafi Ananda Utama1, Nur Azizah U. Hasanah1, Slamet Riyadi2, Tony K. Hariadi2, Afrizal Zuhri2

1

Faculty of Agriculture, Universitas Muhammadiyah Yogyakarta, Indonesia

2

Faculty of Engineering, Universitas Muhammadiyah Yogyakarta, Indonesia

Abstract

Postharvest maturity index for mangosteen (Garcinia mangostana L) is very important for picking and grading during postharvest processing. Skin color change is the primary maturity index for mangosteen. However determination using human eyes needed many skilled labours and was inconsistent therefore new method in image processing technology using SVM (Support Vector Machine) was employed in this study. Chemical analysis of mangosteen was performed and used as a reference of SVM method. The chemical analysis of mangosteen showed that anthocyanin content increased from 126.20 ppm at stage 1 to 213.98 ppm at stage 6. Reducing sugar content increased from 3.17% at stage 1 to 7.92% at stage 6. The same pattern was found for total soluble solid, an increase from 3.86% at stage 1 to 7.81% at stage 6. Whereas for total acid content and hardness the pattern was the opposite. Total acid conte nt was decreased from 1.78% at stage 1 to 1.06% at stage 6 and the fruit hardness of mangosteen was also declined, showing the number from 4.30 N at stage 1 to 0.69 N at stage 6. For SVM method, image aquisition was conducted for mangosteen images from stage 1 to stage 6, followed by color feature extraction for each stages. The result was trained and tested using SVM and resulted accuracy level of 83.3%.

Introduction

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This article has been presented at ISTTH 2016 and will be published at Acta Horticulturae Mangosteen is usually picked when the colour is pink to red across the skin. If it is picked too early the fruit will not be ripe perfectly and it degrades the quality (Tongdee and Suwanagul, 1989; Paull and Ketsa, 2004; Palapol, et al., 2009). The low quality of mangosteen make s the fruit is declined for export purposes and that is a loss for the farmer since the price of mangosteen overseas is about 5-8 higher than the local marketplace (Suyanti and Setyadjit, 2007).

The purplish red color skin in mangosteen is caused by the anthocyanin pigments. Anthocyanin is a pigment found in mangosteen when the fruit is ripe. The identification of man gosteen colour can be measured visually or with destructive analysis using HPLC, Spectrophotometer, GC/MS and other chemical analyzes. Measurement done by human eyes is usually subjective and inconsistent and the measurement can differ from one person to another. Meanwhile destructive method requires the fruit to be destroyed and through stages of complex analysis that take a long time to finish. Therefore it is important to develop a non destructive method in determining the ripeness of the fruit. Mechanical analysis of non destructive ends for ripeness

y olo usi g a eu al et o k NN a d fuzzy al eady idely tested (Riyadi, et.al., 2007a;

Riyadi, et.al., 2007b). Re e tly a e te h i ue has ee de eloped that is suppo t e to

a hi e “VM hi h has a higher degree of accuracy. Testing SVM on mangoes showed very

good results with a 95% accuracy rate (Nandi et al., 2014).

Material and methods

Mangosteen

Mangosteen was obtained from a fruit plantation in Purworejo, Central Java, Indonesia. The fruit was immediately brought to the Post Harvest Laboratory, Faculty of Agriculture Universit as Muhammadiyah Yogyakarta, Indonesia (UMY) and stored at room temperature. Mangosteen was classified based on the maturity level visually and divided into six crite ria of maturity (Standard ASEAN STAN 10; 2008):

Stage 0: yellowish white or yellowish white with 5-50% scattered pink spots

Stage 1: light greenish yellow with 5-50% scattered pink spots

Stage 2: light greenish yellow with 51-100% scattered pink spots

Stage 3: spots not as distinct as in stage 2

Stage 4: red to reddish purple

Stage 5: dark purple

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Method of Destructive Fruit Maturity

Detection of maturity with destructive methods was performed to obtain the reference data of mangosteen maturity.

Extraction of anthocyanin- Methods of extraction and isolation of anthocyanins were modified from Lestario et al. (2011). Mangosteen peel was cut into small pieces then macerated with methanol containing 1% HCl in 1: 4 (w/v) overnight at 5oC. The filtrate was filtered with Whatman no. 1, and partitioned with a separating funnel with the addition of diethyl ether to separate the components of non-anthocyanin (Ozela, et al., 2007). To add polarity in order to separate solvent well, it was added distilled water (filtrate volume ratio: diethyl ether: distilled water = 1: 2: 1).

Total Content of Anthocyanin - Method from Giusti and Wrolstad (1996) was used to determine the total anthocyanin content in the skin of mangosteen. Anthocyanin extract was dissolved in KCl-HCl buffer (1M, pH 1) and NaOAc buffer (1M, pH 4.5) with a ratio of extract against buffer was 1: 5 (v/v). Each solution was measured its absorbance at 520 nm and 700 nm after incubation for 15 min at RT and the results was incorporated into the formula A = [(A510-A700)pH1 - (A510-A700)pH4,5] and calculation was incorporated into the law of Lambert-Bee that A = ε.L.C.

Analysis of Reducing Sugar - Reducing sugar analysis was done used Nelson-Somogyi method. Mangosteen flesh was destroyed and filtered. Sample of 1 mL was added with distilled water up to 10 mL and taken 1 mL to be added with 9 mL of distilled water. Diluted samples was taken 1 mL and mixed with Nelson mixture (Nelson mixture of A and B 25: 1 (v /v)) then heated at 100°C for 20 min. The sample was cooled at RT. Sample was added with 1 mL of arsenomolybdat and 7 mL of distilled water and then shaken. The sample was measured its absorption at 510 nm.

Analysis of Total Soluble Solid (TSS) - TSS analysis was done by destroying the flesh of mangosteen then sealed with a refractometer (Atago, Tokyo, Japan) and calibrated with distilled water.

Analysis Tertitrasi acid (TA) - It was conducted by making a filtrate flesh of mangosteen (5 mL) and titrated with 0.1 M NaOH.

Fruit Hardness Test - Fruit hardness test was done with a hand held penetrometer.

Non destructive methods in detection of fruit maturity by image processing technology

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This article has been presented at ISTTH 2016 and will be published at Acta Horticulturae Figure 1: Development of non-destructive method for mangosteen maturity index

The description of each phases of the development of non dektruktif methods for mangosteen maturity as follows:

1. Data collection of Mangosteen - At this stage, mangosteen was photographed to obtain image data of mangosteen maturity from stage 1 to stage 6. The fruit w as photograph using digital camera with 24 mp CMOS sensor in a light box of 60x40x50 cm to create even lighting.

2. Establishing image processing technology method- The process of SVM method began with the extraction of RGB (red, green blue) color features of mangosteen image. RGB features were summed and averaged for each colour and 6 values were processed in the SVM method.

3. Results validation - Validation of the results was done to determine whether the determination of maturity index with SVM method gave accurate results as the destructive method. The data reference was the concentration of anthocyanin content and supporting data including sugar, TSS, TA and fruit hardness test.

Result and discussion

Determination of magoosten maturity index by destructive method

composing non destructive method by SVM

NO

Done

Start

YES fixing methode

Data collection of mangoosten images

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Destructive method in fruit maturity detection

Ripening process of mangosteen from stage 1 to 6 showed an increase in anthocyanin content from 126.20 ppm at stage 1 to 213.98 ppm at stage 6 (Fig. 2). The increase of anthocyanin was reflected in the change of its skin color from stage 1 to stage 6 as shown in Fig. 3. The skin color changes correlated with ethylene production and it was shown by our data on other chemical compound changes we will be discussed later. Studies showed that the development of skin color in outer pericarp of mangosteen was correlated with the increase of cyanidin-3-sophoroside and the cyanidin-3-glucosides during ripening process (Palopol et.al., 2009). Reducing sugar increased from 3.17% at stage 1 to 7.92% at stage 6 during ripening process as shown in Fig. 4, and this indicated glycolysis process where polysaccharides converted into glucose. This finding was supported by the increase of total soluble solids from from 3.86% at stage 1 to 7.81% at stage 6 and the decrease of total acid content from 1.78% at stage 1 to 1.06% at stage 6 as shown in Fig. 4. These phenomenon was affected by the increase in ethylene production during ripening process of mangosteen as suggested by Palopol et.al. (2009). Fig. 5 showed the decrease of hardness from 4.30 N at stage 1 to 0.69 N at stage 6 indicated degradation of pectin in the cell wall, and this also found in other fruit like oranges (Prabasari et.al., 2011).

Figure 2. The increase of anthocyanin content during ripening of mangosteen

Stage 1 Stage 2 Stage 3

100 120 140 160 180 200 220 240

1 2 3 4 5 6

A

n

th

o

c

y

a

n

in

(

p

m

)

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Stage 4 Stage 5 Stage 6

Figure 3. Maturity stages of mangosteen during ripening. The skin color changed from yellow green in stage 1 to deep purple in stage 6

Figure 4. The increase of reducing sugar, total soluble solid and total acid during ripening of mangosteen 0 2 4 6 8 10 12 14 16 18 20

1 2 3 4 5 6

P e r c e n t

Stage of maturity

Total Asam Tertitrasi Total Solub le Soli d 0 1 2 3 4 5

1 2 3 4 5 6

H a r d n e ss

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This article has been presented at ISTTH 2016 and will be published at Acta Horticulturae Figure 5. The decrease of hardness during ripening of mangosteen

Non destructive methods in fruit maturity detection

The sum of R and G was quite far between stage 1, 2 and 3. When the values were put into scatter plot the pattern indicated obvious group between stage 1, 2 and 3 as shown in Fig. 6 and 7. However in stage 4, 5 and 6 the pattern of scatter plot between stages was slightly overlapped. Meanwhile scatter plot from sum of B did not show distinguish groups particularly between stages 4, 5 and 6 as shown in Fig. 8. All together the result of sum of R, G and B was in line with the result of visual detection (data not shown) that differentiation between stages 1, 2, and 3 was easier than differentiation between stages 4, 5 and 6.

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This article has been presented at ISTTH 2016 and will be published at Acta Horticulturae Figure 7. Scatter plot from sum of G (green)

Figure 8. Scatter plot from sum of B (blue)

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together indicated characteristics of grouping of R, G and B and showed that it was possible to be used as a non destructive methods to diffrentiate stages of mangosteen.

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This article has been presented at ISTTH 2016 and will be published at Acta Horticulturae Figure 10. Scatter plot from mean of G (green)

Figure 11. Scatter plot from mean of B (blue)

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This article has been presented at ISTTH 2016 and will be published at Acta Horticulturae other stages were between 50 and 85%. In summary, the mean of accuracy in detecting mangosteen maturity was 83.3%.

Table 1. Result of training and testing of SVM to detect mangosteen maturity

Stage Sum of image

testing

Sum of images classified correctly

Accuracy of classification (%)

Note

1 8 8 100.0 -

2 8 8 100.0 -

3 8 7 87.5 1 image

classified as stage 4

4 8 4 50.0 1 image

classified as stage 3 meanwhile 3 images classified as stage 5

5 8 5 62.5 3 images

detected as stage 4 whereas 2 images detected as stage 6

6 8 8 87.5 -

Mean of accuraccy in stage detection 83.3

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This article has been presented at ISTTH 2016 and will be published at Acta Horticulturae In conclusion, destructive methods showed characteristics of chemical compound changes during senescence and when the same samples were used to examine non destructive methods using image processing technology it showed the accuracy in detecting mangosteen maturity with the level 0f 83.3%.

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