Fuzzification of Rubber Tree White Root Disease Based on Leaf’s Discolouration

Faridatul Aima Ismail1, Hadzli Hashim1, Nina Korlina Madzhi1, Noor Ezan Abdullah1, Rosidah Sam1, Sufian Latib1, Mohd Suhaimi Sulaiman2

1Faculty of Electrical Engineering Universiti Teknologi MARA, Selangor,

Malaysia hadzli66@gmail.com

2Faculty of Electrical Engineering

Universiti Teknologi MARA, Dungun, Terengganu, Malaysia

Abstract— This paper describes research work in developing an intelligent model for detecting white root disease stages by using fuzzy logic. In this work, classification of three stages of white root disease is based on discolouration of the tree’s leaf. These stages are healthy, medium & worse and each of them were tested with respect to vein, main vein and peteolute. The reflectance of the leaves discolouration sample is measured by using MCS600 Carl Zeiss spectrometer. Analysis and justification are done statistically for features extraction and selection as input for fuzzy system. From the statistical result there is strong evidence that the stages of the white root disease can be discriminated from each other. Input value for the fuzzy system, based on the selection of the result from statistic software. Two inputs have been selected to be the input for the fuzzy system which are main vein and vein. Overall performance of the system is 78.33% accuracy after being tested with 300 samples in order to distinguish healthy, medium and worse respectively.

Index Terms— SPSS, spectrometer, Fuzzy logic, healthy, medium and worse stages.

I. INTRODUCTION According to the book of “Maladies of Hevea in Malaysia”

there are four main factors that contribute to rubber tree disease. The diseases are root, panel, stem and branch and leaf disease. This research focuses only on root disease where there are six types of root diseases which are white, red, brown, ustulina, poria and stinking root disease. The scope of this research is on white root disease because it is the main contributor to the disease of rubber tree in Malaysia [1].

Basically, white root disease can be traced after discolouration of leaf occurs. Once the discolouration of leaf is being detected the rubber tree is already being infected. Until today, there is no device that can trace the disease from healthy to worse. Normally, only expert person in this field is trained to identify the white root disease. Sometimes, documented leaf is used as guidance.

Therefore, in this research a technique to identify various stages of white root disease is applied. The idea is to transform

the human knowledge which is the expert person into computer knowledge which is the intelligent model (fuzzy). As a result, the system can do early detection of white root disease in future and thus save the tree from die and prevents the disease from spreading to other trees.

This study focused on discriminating three stages of white root disease which are healthy, medium and worse. Spectrometer was used to collect the data from the sample and analysis was done using SPSS software to produce statistical result before selecting the appropriate features for developing a fuzzy system.

Recently, Noor Ezan has proposed a classification system of three types of rubber tree leaf disease through automation model that utilized primary RGB model [2]. The infected images were being identified through region of interest (ROI) before being processed to quantity the normalized indices from RGB color distribution. Another research proposed by Fairul Nazmie have developed a classification model for rubber tree seed clones based on shape features through imaging technique [3]. The RGB images of seeds were captured and then the images were processed using segmentation technique and utilized Artificial Neural Network (ANN) for modeling system. Muhammad Adib Haron has developed a classification of rubber tree leaf disease using spectrometer [4]. This research, focus on five types of leaf disease to be classified using spectrometer and SPSS. Spectrometer used to get reflectance measurement meanwhile, SPSS used to discriminate the main features between five types of leaf disease.

Furthermore, previous research on fuzzy control technique has been done by Zhiming Wang to improve positioning precision and decrease the exceeding initial value of solar cell transfer robot [5]. The positioning of solar cell is control by PID controller and fuzzy self-adjusting function. While another work by Zhongze Fan also design a system framework for the engine fault diagnosis by using fuzzy match method of fuzzy rule set which are a series of fuzzy neural network. This

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system used to export fault detection time, fault isolation time, fault type and fault degree [6].

Hence, this work suggests a significant technique which used to classify the stages of white root disease with respect to the percentage of light reflectance value and the various locations of the rubber tree’s leaf. This technique was chosen because it has a direct perception of light reflectance compared to Artificial Neural Network (ANN). The lighting was under controlled and has no influence from external lighting. Thus, the sets of data are reliable for developing the intelligent model.

II. METHODOLOGY In this section, flow of data collection which will be

inserted later into Fuzzy Logic is discussed. Figure 1 shows the methodology flow in completing this work.

Fig. 1. Flow chart of overall process.

A. Samples Collection The leaf samples for this research were taken from Rubber

Research Institute of Malaysia (RRIM) at Sg. Buloh, Selangor, Malaysia. The samples were collected from 6 clones which are

RRIM2004, RRIM2024, RRIM2025, RRIM928, PB260 and PB350. Each of these clones consists of the health, medium and worse stage. Figure 2 shows example for each stages from RRIM2004.

Fig. 2. RRIM2004 leaf samples for various stages.

B. Samples Categorization There are three stages of white root disease that are being

tested which are healthy, medium and worse. Each of the stage is being divided with three sub components. The sub components are vein, main vein and peteolute. Figure 2 shows the sub component for one of the rubber clone which is RRIM2025. In this project, a total 900 samples were tested for each clone. Each sub component consists of a 100 samples thus producing 300 samples for each stage and finally making 900 samples for each clone. For this work, a total of 5400 samples were being experimental and measured. The flow of the selection of part testing can be observed as in Figure 3.

Fig. 3. Sub component being tested.

C. Reflectance Measurement Spectrometer is an instrument used to get the reflectance

measurement. The model of this spectrometer is MCS600 manufactured by Carl Zeiss which uses the finest photodiode array where the range is from 190 to 2200nm. It uses an OFK30 type measurement head for obtaining the reflectance value [7]. Figure 4 depicts the spectrometer used in this research and Figure 5 illustrates the measurement process.

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Fig. 4. MCS600 Carl Zeiss Spectrometer.

Fig. 5. OFK 30 head placed on top of the sample during measurement.

The following Figure 6 shows an example of reflectance measurement result obtain from the spectrometer. This software adapted Aspect Plus software from Carl Zeiss.

Fig. 6. Reflectance of sample using Aspect Plus.

D. Data Conversion The format of acquired reflectance index is in .DAT

format. The data was saved in the software Aspect Plus before being converted into ASCII code file. In ASCII, the data from .DAT would be converted into .EXE where in Microsoft Excel. Hence, the data is used to be analyzed in SPSS software.

E. Analyze using SPSS In this software analysis being done to observe the

comparison between stages and with respect to its reflectance index by using normality tests. Then, the data is used to generate the error plot, box plot and comparison analysis.

F. Fuzzy Logic Input from SPSS after being analyzed is used as input to

the fuzzy logic. In this project two parts be selected to be used as an input to the fuzzy simulation. The inputs are main vein and vein. This simulation been done by using Fuzzy Toolbox in MATLAB [8]. Figure 7 shows some element that must have if fuzzy logic being use. The elements are FIS Editor, membership function editor, rule editor, rule viewer, and surface viewer.

Fig. 7. Fuzzy inference system.

III. RESULTS AND DISCUSSIONS

A. Reflectance Plot Figure 8 illustrates an example of overlay reflectance plot

for the stages. The green spectral region is in between 495nm and 570nm [9]. The reflectance measurement was based on the Green LED available at market which is 525+/- 4nm [10]. All samples at this reflectance are used for further statistical analysis utilizing SPSS.

Fig. 8. Overlay plot for three stages of sample.

B. Normality Test Result from normality test can be used to see the

significant value between variable and condition applied [11]. Ho = There is no significant differences in condition

sample (not normal).

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H1 = There is significant differences in condition sample (normal).

Table I tabulates the normal distribution between all variable and condition which are p>0.05. This normal test is based on Kolmogorov - Smirnova on significant column. One measures a goodness of fit of a normal model to the data – if the fit is poor then the data are not well modelled in that respect by a normal distribution. Thus, in this table H1 is applied. If the normality test proves that the obtain data is normal, the next following method will be used is the parametric test. Otherwise, non-parametric methods need to be used. Parametric method is a method that easy to use and simple algorithm can be classified. If non parametric method needs to be employed, the complex algorithm will be applied.

TABLE I. NORMALITY TEST

C. Error Bar

Figure 9 depicts the error bar for all stages for each vein, main vein and peteolute. This can be concluded that there is significant different between all stages where the selection of the variable based shows no redundancy of reflectance value.

Fig. 9. Error bar from SPSS analysis.

D. Independent T-test Table II shows the comparison for all variables and

conditions between each other with regards to mean difference and significant values. The result shows that the entire variables significant are below than 0.05 except for peteolute variable where healthy and medium exceed the 0.05. This tells

that not all evidence is proved to assume all the condition is significantly different with other.

TABLE II. MULTIPLE COMPARISON FOR EACH VARIABLE AND CONDITION

E. Box Plot Figure 10 shows box plot where the initial data being

analyzed. Box plot shows all data in every variable. Each of variables will determine the minimum, maximum and mean value to be inserted into membership function editor in fuzzy tool box.

Fig. 10. Box plot for RRIM 2004

Table III shows the range of selected variable to be chosen as input for fuzzy system.

TABLE III. RANGE OBTAIN FROM BOX PLOT

Variable Condition Min Mean Max

Main vein

Healthy 0.038 4.298 8.166 Medium 1.808 6.462 11.902 Worse 5.019 9.149 12.295

Vein

Healthy 0.955 2.463 3.905 Medium 1.218 3.512 6.527 Worse 2.987 4.888 7.051

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F. Fuzzy Logic The following Figure 11 – 13 depicts the outputs from

fuzzy system. FIS Editor as shown in Figure 11 displays some information about the fuzzy inference system [8].

Fig. 11. Fuzzy input.

Figure 12 displays the Membership Function Editor is the tool for editing all of the membership functions associated with all of the input and output variables for the entire fuzzy inference system [8].

Fig. 12. Fuzzy membership function.

Figure 13 shows a sample of a roadmap of Rule Viewer for RRIM 2004 of the whole fuzzy inference process. It is illustrates on how fuzzy system operates until generating the output for the system. For example in rule viewer shows stages produced 0.637 which is in medium range if the input [10.47 3.977].

Fig. 13. Fuzzy rules viewer.

While Table IV tabulates the number of samples utilize in testing the fuzzy system. There are 300 samples being tested manually. The testing is based on the same stages for both inputs. For example, if main vein is healthy, vein also in healthy stage. Same goes to medium and worse. From the table, out of hundred samples for healthy, only 65 samples shows true value and 35 samples are false. As for medium case, only 99 samples are true and only 1 sample is false. Finally for the worse stage, 71 samples are true while 29 samples are false. The overall accuracy is computed as 78.33%.

TABLE IV. TESTING SET FOR THE SYSTEM

Actual

Predicted

Healthy Medium Worse Healthy 65 1 0 Medium 35 99 29 Worse 0 0 71

100 100 100

IV. CONCLUSION The three stages of white root rubber tree disease are

healthy, medium and worse are being tested in order to observe the differences among the stages in terms of reflectance index.

From the normality table all of the stages are differ from each other except for peteolute where healthy and medium are collapse to each other. Since there had collapse between the stages then not all data is considered as normal. The data <0.05 can be classify as significantly different. The acquired reflectance indexes are referring to available Green LED at the market which is 525+/-4nm which is suitable for developing reflectance sensor system. The result from normality test, only vein and main vein can be used as the input for the fuzzy system since all condition in these two variables can be differentiate.

As a conclusion, from fuzzification system, the outcome of this work has shown that combination of two variables which are vein and main vein has accuracy of 78.33% to distinguish healthy, medium and worse respectively.

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V. FUTURE DEVELOPMENT Based on the result, the experiment has produced several

promising result. Investigation to include other commercial’s clone can be extended. This experiment produced specification for required sensor to be developed such as using available sensor at market used for designing the hardware in order to detect stages of white root disease. The uses of fuzzy logic technology in hardware development could be a good prospect for commercialization.

ACKNOWLEDGMENT The authors would like to give their appreciation to Dr.

Mohd Nasaruddin B. Mohd Aris, Miss Nurmi Rohayu Abdul Hamid, miss Azimah Bt. Izhar, Mr. Segar a/l Meenah @ Raman and Mr. Soni B. Othman from Rubber Research Institute Malaysia (RRIM) for sharing their knowledge and providing the samples that need to be analyzed in this project. And not to forget to Faculty of Electrical Engineering, Universiti Teknologi MARA Shah Alam and also to Research Management Institute (RMI), UiTM and Kementerian Pengajian Tinggi (KPT) for funding our research work under RAGS grant code (22/2012).

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Rubber Research Institute of Malaysia, 1975. [2] N.E. Abdullah, A. A. Rahim. H. Hashim. M. M. Kamal,

"Classification of Rubber Tree Leaf Disease Using Multilayer Perception Neural Network," in Research and Development,

2007. SCORED 2007. 5th Student Conference 12 - 11 Dec 2007. PP 1 - 6., 2007.

[3] F. N. Osman. H. Hashim and et. al, "An Intelligent Classification Model for Rubber Seed Clones Based on Shape Features Throught Imaging Technique," in International Conference on Intelligent Systems, Modelling and Simulation, 2010.

[4] M. A. Haron, F. N. Osman, S. A. M. A. Junid,S. H. Hashim, "Classification of Rubber Tree Leaf Disease Using Spectrometer," in Fourth Asia International Conference on Mathematical / Analytical Modelling and Computer Simulation, 2010.

[5] Zhiming Wang and et al., "Study on Positioning Control of Transfer Robot with Solar Cell," in The Ninth International Conference on Electronic Measurement & Instruments ICEMI 2009.

[6] M. H. Zhongze Fan, "Fuzzy Rule Set Based Engine Fault Diagnosis," in Power and Energy Engineering Conference, APPEEC 2009. Asia - Pacific, 2009.

[7] C. Zeiss, Multi Channel Spectrometer System MCS 600 Model MCS 621 VIS II/611 NIR 1.7/CLH 600/OFK 30 (manual book), Zeiss, June 2008.

[8] Fuzzy Logic Toolbox for use with MATLAB, Works, The Math, 2002.

[9] P. D. S. Thomas J. Bruno, CRC handbook of Fundamental Spectroscopic Correlation Charts, CRC Press, 2005.

[10] "Agilent T - 1 3/4 (5mm) Precision Optical Peformance InGaN Blue and Green Lamps Data Sheet," Agilent Technologies, 2005.

[11] I. cooperation, SPSS Statistis Base 17.0, IBM.

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