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P-ISSN: 2715-2448 | E-ISSN: 2715-7199 
Vol.6 No.2 July 2025 
Buana Information Technology and Computer Sciences (BIT and CS) 

Classification Of Rice Plant Diseases Based on Leaf Images Using 

the Multi Class Support Vector Machine (M-SVM) Method 
 

Febiana Angela Tanesab 1, Rangga Pahlevi Putra 2, Aviv Yuniar Rahman 3 
1 2 3 Department of Informatic Engineering, Universitas Widya Gama, Malang, Indonesia 

E-mail: Email: febytanesab@gmail.ac.id 1, rangga@widyagama.ac.id 2 , aviv@widyagama.ac.id 3  

 
Received: 2025/05/17 | Revised: 2025/06/26 | Accepted: 2025/07/27 

 

Abstract  

 

The rice farming sector plays an important role in the Indonesian economy, considering that rice is the 

main staple food. According to IRRI, rice farmers experience crop losses of up to 37% each year due 

to pests and diseases. This study aims to classify rice plant diseases using the Multi-Class Support 

Vector Machine (M-SVM) method based on leaf images. This study aims to provide education to farmers 

in recognizing and overcoming diseases in rice plant leaves. The types of rice leaf diseases classified 

in this study include Blast, Kresek, and Tungro. The data used in this study amounted to 1200, which 

were divided by varying training and testing data ratios, from 10% training and 90% testing to 90% 

training and 10% testing. Each variation of features and data division was evaluated by calculating the 

model performance parameters. The features used for classification include color (RGB) and texture 

(GLCM) from leaf images. The test results showed that the best accuracy obtained was 85.5% using a 

combination of color and texture features. 

 

Keywords: Accuracy, Disease Classification, GLCM, leaf image, M-SVM, Rice. 

 

 

I. Introduction 

The rice farming sector plays an important role in contributing to the Indonesian economy, because 

rice is one of the largest commodities. Many countries, including Indonesia, make rice their main staple 

food. Therefore, Indonesia needs to continue to innovate so that the rice supply remains abundant and 

stable [1]. Agriculture itself is an activity that utilizes nature to produce food, one of which is rice 

cultivation. However, rice plants are often attacked by various diseases, such as leaf blight (kresek), 

blast, tungro and others [2]. 

The development of digital image processing technology and artificial intelligence (AI) provides 

potential solutions in the agricultural sector, especially in terms of identifying plant diseases. With the 

help of machine learning algorithms, such as Support Vector Machine (SVM), the classification process 

can be carried out [3], Multi-Class Support Vector Machine (MSVM) is a variant of the Support Vector 

Machine (SVM) method used to solve multi-class classification problems. SVM is basically a 

classification algorithm designed to handle two-class problems (binary classification) [4] Based on the 

background of this problem, researchers propose a solution by using the Multi-Class Support Vector 

Machine (M-SVM) method. The use of the M-SVM algorithm allows disease classification based on 

patterns and textures on rice leaves, so that each type of disease can be recognized more quickly and 

accurately. 

II. Methods 

In (Figure 1) it will explain the research stages including several steps carried out systematically 

to achieve the objectives of the research, the research stages include starting, input of rice leaves, 

analysis of the problem identification system, implementation, trial, success. 

mailto:febytanesab@gmail.ac.id
mailto:rangga@widyagama.ac.id
mailto:aviv@widyagama.ac.id


Vol.6, No.2, July 2025 | 67 

 

 

. Figure 1. Research stage flowcart. 

(Source: Personal Preparation) 

 

1. Input data for rice disease leaves: 

 

 

 

 

 

 

 

 

 

         (a). Leaf blight (kresek)                      (b). Blast              (c). Tungro 

    Figure 2. Image of rice leaves 

 

In (Figure 2) we will explain about 3 diseases of rice as follows: 

a. Bacterial leaf blight is a very common disease found in rice fields. The main cause of this disease 

is the bacteria Xanthomonas oryzae. Symptoms of bacterial leaf blight on leaf blades are 

characterized by damage that usually begins a few centimeters from the edge, which appears as 

lines and blisters, then spreads to the wavy edges [ 1]. 

b. Blast disease caused by Pyricularia grisea is an important disease in rice plants in Indonesia, 

especially in upland rice in dry land. grisea infects the leaves and causes disease symptoms in the 

form of diamond-shaped brown spots called leaf blast [5]. 

c. Tungro is a disease caused by a double infection of 2 different types of viruses. The second virus 

in question is Rice Tungro Spherical Virus (RTSV) and Rice Tungro Bacilliform Virus (RTBV). 



 

Vol.6, No.2, July 2025 | 68 

 

Symptoms of tungro disease are that the leaves will turn yellow starting from the tips of the leaves 

that are still in the growth stage [5]. 

 

Rice plants are susceptible to various types of diseases. In this study, we focus on three main types 

of diseases in rice plants, namely tungro disease, leaf blight, and leaf blast. The data used consists of 

1200 leaf images divided into 3 classes, namely 400 Leaf blight (kresek) image data, 400 leaf blast 

image data, 400 tungro image data. Data division is carried out for training data (80%) and test data 

(20%) [3]. 

 

2. Pre-processing 

Pre-processing is a crucial step that is carried out before the image is used for feature extraction or 

classification model development. The main purpose of this stage is to prepare the image so that it is 

more ready for further analysis and can improve accuracy [6]. The disease detection system at this 

processing stage includes: Normalization, Contraction, Cropping, Resize. 

 

3. GLCM and RGB feature extraction  

GLCM (Gray Level Co-occurrence Matrix) and RGB (Red, Green, Blue) feature extraction are 

used to analyse the texture and color of rice leaf images, especially in detecting and classifying diseases 

that attack rice leaves [7]. When rice leaves are infected with disease, both the texture and color of the 

leaves will experience different changes from healthy leaves. Infection can cause color changes, such 

as yellowish or brownish, as well as the appearance of spots with certain intensities, which can be 

analyzed through RGB features [8]. In addition, changes in texture patterns such as spots, lines, or holes 

on leaves can be evaluated using the GLCM method which extracts features such as Contrast and 

Correlation. By combining texture analysis using GLCM and color analysis using RGB, we can obtain 

more complete information about the condition of rice leaves, thereby increasing the accuracy of disease 

identification and classification [9]. 

 

4. M-SVM Classification  

Classification using the Multi-Class SVM (MSVM) method is divided into two stages, namely 

training and testing, where the image dataset goes through a feature extraction process using the GLCM 

and RGB methods. GLCM is used to extract texture information, such as Contrast and Correlation, 

while RGB is used to analyse colour characteristics in rice leaf images. Furthermore, the extracted 

images are classified using Multi-Class SVM. Through this classification process, it can be identified 

whether the input leaves are included in the category of normal leaves or diseased rice leaves, and can 

be separated based on their respective classes by considering a combination of texture and colour 

features to improve identification accuracy [10],[11],[12]. 

 

5. Accuracy Evaluation 

At this stage there are several steps taken, namely: 

a. Confusion Matrix 

Use a confusion matrix to see how well the model classifies diseases. The confusion matrix will 

show the number of correct and incorrect predictions for each disease class (e.g., Leaf Blight, 

Leaf Blast, and Tungro) that exist [9], [13], [14]. 

b. Accuracy 

Accuracy =  
𝑇𝑃+𝑇𝑁

𝑇𝑃+𝑇𝑁+𝐹𝑃+𝐹𝑁
 × 100%       (1) 



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c. Precision 

     Precision = 
𝑇𝑃

𝑇𝑃+𝐹𝑃
 × 100%                     (2) 

 

d. Recall 

      Recall =
𝑇𝑃

𝑇𝑃+𝐹𝑁
 × 100%                      (3) 

 

The research will be conducted at Jl. Sudimoro, behind the Sawah cafe, and is targeted to take 

place from November 2024 to February 2025. This extended period will provide sufficient time for the 

researcher to make necessary preparations, such as understanding the research problems, objectives, 

methods, and the tools required for the research process [15]. 

 

III. Results and Discussions 

a. Feature extraction using Gray Level Co-occurrence Matrix (GLCM) and RGB 

Feature extraction using the Gray Level Co-occurrence Matrix (GLCM) method is a technique in 

image processing used to obtain texture information from images. GLCM analyzes the spatial 

relationship between pixels based on gray levels to form a co-occurrence matrix, from which texture 

features such as Contrast, Correlation can be calculated. At this stage, the method is used to detect rice 

leaf images. In addition, the basic color values of the image are also used by taking RGB (Red, Green, 

Blue) values directly from each pixel as additional features that represent image color information. 

 

Figure 3. Blast image capture interface design 

In (Figure 3.) to convert data from image form into numeric form, a data conversion process design 

is needed that utilizes a graphical interface (GUI) using the Matlab programming language. This process 

is important for the purposes of texture analysis with the M-SVM method. The blast, kresek and tungro 

data values obtained from the feature extraction results consist of several fields that can be displayed in 

(Table 1). 

Tabel 1. Blast, Kresek and Tungro Image Datasets 

Project_ID Contrast Correlation Red Greend Blue Results 

blast111.jpg 
  0.091126 

 

0.96395 
 

0.7276 
 

0.74142 
 

0.75599 
 

blast 

blast144.jpg 0.09572 0.95864 0.74756 0.72766 0.69713 blast 



 

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kresek11.jpg 0.15015 
 

0.93488 
 

0.56585 
 

0.62566 
 

0.76428 
 

plastic bag 

kresek109.jpg 0.1193 0.95152 0.74032 0.72572 0.71702 plastic bag 

tungro1.jpg    0.073608 
 

   0.96713 
 

  0.7847 
 

  0.72117 
 

  0.66278 
 

tungro 

tungro100.jpg 0.053928 0.97586 0.79111 0.73136 0.65979 tungro 

 

b. Training Data Multiclass Support Vector Machine (M-SVM) method 

 

Table 2. Results of Polynomial M-SVM Training Evaluation 

M-SVM(POLYNOMIAL) 

Split ratio 
Accuracy Precision Recall 

Data 

Training Testing Train Test 

10% 90% 88% 83.3751% 83.3333% 120 1080 

20% 80% 89.7222% 84.5861% 84.5833% 240 960 

30% 70% 85.1852% 78.8981% 77.7778% 360 840 

40% 60% 85.5556% 78.7160% 78.3333% 480 720 

50% 50% 85.7778% 78.5025% 78.6667% 600 600 

60% 40% 85.2778% 77.8825% 77.9167% 720 480 

70% 30% 85.2381% 79.2707% 77.8571% 840 360 

80% 20% 84.5833% 77.1485% 76.8750% 960 240 

90% 10% 85.1852% 77.9073% 77.7778% 1080 120 

 
In (Table 2) it is explained that the results of the training data evaluation using the M-SVM 

Polynomial method obtained a high accuracy score, namely at a split ratio of 20:80, the number of 

training data is 240 with an accuracy score of 89.7222%. Precision 84.5861% and Recall 84.5833. and 

the results of the M-SVM Polynomial graph and the calculation of the confusion matrix with the highest 

value at a split ratio of 20:80, can be seen in (Figure 4) and (Figure 5) below. 

        Figure 4. M-SVM Polynomial 20:80 graph              Figure 5. CM M-SVM Polynomial 20:80 

 

 

 



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Table 3. Linear M-SVM Training Evaluation Results 

M-SVM(LINEAR) 

Split ratio 
Accuracy Precision Recall 

Data 

Training Testing Train Test 

10% 90% 87.2222% 81.2963% 80.8333% 120 1080 

20% 80% 89.4444% 84.1667% 84.1667% 240 960 

30% 70% 84.8148% 77.3130% 77.2222% 360 840 

40% 60% 86.2500% 79.5291% 79.3750% 480 720 

50% 50% 84.4444% 76.7066% 76.6667% 600 600 

60% 40% 84.9074% 77.2398% 77.3611% 720 480 

70% 30% 84.2063% 76.4328% 76.3095% 840 360 

80% 20% 84.5833% 76.7444% 76.8750% 960 240 

90% 10% 84.3827% 76.7453% 76.5741% 1080 120 

 

In (Table 3), it can be seen that the M-SVM Linear method shows performance variations at various 

split ratios of training and testing data. At a split ratio of 20:80, the M-SVM Linear model obtained very 

good results with the highest accuracy score of 89.4444%, followed by a precision score of 84.1667% 

and a recall of 84.1667%. and the results of the M-SVM Linear graph and the calculation of the 

confusion matrix with the highest value at a split ratio of 20:80, can be seen in (Figure 6) and (Figure 

7) below. 

 

       Figure 6. M-SVM Linear graph 20:80            Figure 7. CM M-SVM linear 20:80 

Table 4. Results of Gausian M-SVM Performance Evaluation 

M-SVM(POLYNOMIAL) 

Split ratio 
Accuracy Precision Recall 

Data 

Training Testing Train Test 

10% 90% 80% 77.5360% 76.6667% 120 1080 

20% 80% 79.2125% 75.7534% 75.8333% 240 960 

30% 70% 79% 69.3777% 70% 360 840 

40% 60% 77.2222% 65.0255% 65.8333% 480 720 



 

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50% 50% 76% 62.9947% 64% 600 600 

60% 40% 74.6296% 60.8490% 59.5833% 720 480 

70% 30% 71.1905% 55.6062% 56.7857% 840 360 

80% 20% 71.5278% 54.3445% 57.2917% 960 240 

90% 10% 71.2346% 53.6462% 56.8519% 1080 120 

 

In (Table 4) it is explained that the Gaussian M-SVM method on training data obtained the highest 

accuracy at a split ratio of 10:90, with a total of 120 data. At this split ratio, the accuracy obtained was 

80%, precision 75.7534%, and recall 75.8333%. The Gaussian M-SVM graph and confusion matrix 

calculation for a split ratio of 10:90 can be seen in (Figure 8) and (Figure 9). 

           Figure 8 Gaussian M-SVM graph 10:90                 Figure 9 Gaussian CM M-SVM 10:90 

c. Test data for the Multiclass Support Vector Machine (M-SVM) method 

Table 5. Results of the M-SVM Polynomial Test Evaluation 

M-SVM(POLYNOMIAL) 

Split ratio 
Accuracy Precision Recall 

Data 

Training Testing Train Test 

10% 90% 71.2346% 53.6462% 56.8519% 1080 120 

20% 80% 71.5278% 54.3445% 57.2917% 240 960 

30% 70% 71.7460% 53.2519% 57.6190% 360 840 

40% 60% 75.7407% 62.1921% 63.6111% 480 720 

50% 50% 77.4444% 64.9680% 66.1667% 600 600 

60% 40% 78.7500% 67.4113% 68.1250% 720 480 

70% 30% 80.7407% 70.9712% 71.1111% 840 360 

80% 20% 85.5556% 77.9906% 78.3333% 960 240 

90% 10% 85% 77.5126% 77.5% 120 1080 

 

In (Table 5) explains that the results of the evaluation of test data using the M-SVM Polynomial 

method obtained the highest accuracy score, namely at a split ratio of 80:20 with an accuracy score = 

85.5556%, precision = 78.9906%, and recall = 78.3333%. and the results of the calculation of the 

confusion matrix M-SVM polynomial with the highest value can be seen in (Figure 10). 

 



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Figure 10. CM M-SVM polynomial 80:20 

Table 6. M-SVM Linear Test Evaluation Results 

M-SVM(LINEAR) 

Split ratio 
Accuracy Precision Recall 

Data 

Training Testing test Lati 

10% 90% 71% 52.5762% 56.6219% 1080 120 

20% 80% 71.1078% 54.4445% 57% 960 240 

30% 70% 71.1905% 55.6062% 56.7857% 840 360 

40% 60% 73.0556% 58.3109% 59.5833% 720 480 

50% 50% 72.8889% 57.9054% 59.3333% 600 600 

60% 40% 77.7778% 66.9101% 66.6667% 480 720 

70% 30% 80% 69.8135% 70% 360 840 

80% 20% 84.4444% 76.5757% 76.6667% 240 960 

90% 10% 83.8889% 76.2121% 75.8333% 120 1080 

 

In (Table 6) it is explained that the results of the evaluation of the test data using the M-SVM 

Linear method obtained the highest accuracy score, namely at a split ratio of 80:20 with an accuracy 

score = 84.4444%, precision = 76.5757%, and recall = 76.6667%. High accuracy, precision, and recall 

at a ratio of 80:20 occur because the model has enough data for training (80% of data for training). and 

the results of the calculation of the M-SVM Linear confusion matrix with the highest value can be seen 

in (Figure 11). 

Figure 11 CM M-SVM linear 80:20 



 

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Table 7 Gaussian M-SVM Test Evaluation Results 

M-SVM(GAUSIAN) 

Split ratio 
Accuracy Precision Recall 

Data 

Training Testing test Lati 

10% 90% 61.7778% 45.8540% 42.6667% 1080 120 

20% 80% 61.7778% 45.9603% 46.7% 960 240 

30% 70% 63.4286% 47.992% 45.1429% 840 360 

40% 60% 67.4074% 50.0876% 51.1111% 720 480 

50% 50% 67.4667% 49.7449% 51.2% 600 600 

60% 40% 67.7778% 49.9405% 51.6667% 480 720 

70% 30% 73.2593% 55.5506% 56.8889% 360 840 

80% 20% 76.8889% 58.8763% 59.3333% 240 960 

90% 10% 79.8889% 64.7186% 65.3333% 120 1080 

 

In (Table 7) it is explained that the results of the evaluation of the test data using the Gausian M-

SVM method obtained the highest accuracy score, namely at a split ratio of 90:10 with an accuracy 

score = 69.7778%, precision = 52.5284%, and recall = 54.6667%. and the results of the calculation of 

the Gausian M-SVM confusion matrix with the highest value at a split ratio of 90:10 can be seen in 

(Figure 12) 

Figure 12. CM M-SVM Gaussian 90:10 

 

d. Results of comparison of training accuracy of Multiclass Support Vector Machine (M-SVM) 

The accuracy results of the Multiclass Support Vector Machine (M-SVM) method training are 

shown in the comparison in (Table 8). The table provides an overview of how effective the M-SVM 

method is in classifying data, and shows the variation in performance based on the composition of the 

data used. This allows for evaluating the advantages and disadvantages of the method in different 

contexts. 

 

 

 

 

 

 



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Table 8 Results of Training Accuracy Data Comparison 

split ratio 
Accuracy 

polynomial linear Gaussian 

10;90 88% 87.2222% 80% 

20;80 89.7222% 89.4444% 79.2125% 

30;70 85.1852% 84.8148% 79% 

40;60 85.5556% 86.2500% 77.2222% 

50;50 85.7778% 84.4444% 76% 

60;40 85.2778% 84.9074% 74.6296% 

70;30 85.2381% 84.2063% 71.1905% 

80;20 84.5833% 84.5833% 71.5278% 

90;10 85.1852% 84.3827% 71.2346% 

 

Based on the results of the accuracy comparison in (Table 8), it can be concluded that the method 

with the polynomial kernel shows the highest accuracy value of 89.7222% at a ratio of 20:80, and in 

general the performance of the polynomial kernel is superior to the linear and gaussian kernels. The 

linear kernel recorded the highest accuracy value of 88.4444% at a ratio of 20:80, while the gaussian 

kernel had the lowest performance, with the highest accuracy of only 80% at a ratio of 10:90. 

Overall, polynomial kernels are more effective in handling larger training data, while linear kernels 

show better results at more balanced data ratios between training and testing data. Gaussian kernels, 

although inferior, still provide good performance at more dominant testing data ratios, but not as high 

as polynomial and linear kernels. 

e. Results of comparative accuracy of Multiclass Support Vector Machine (M-SVM) testing 

The accuracy results of the testing data from the Multiclass Support Vector Machine (M-SVM) 

method are shown in the comparison results in (Table 9). The table provides an overview of how 

effective the M-SVM method is in classifying data, and shows variations in performance based on the 

composition of the data used. 

Table 9 Results of Comparison of Test Accuracy Data 

split ratio 
Accuracy 

polynomial linear Gaussian 

10;90 71.2346% 71% 61.7778% 

20;80 71.5278% 71.1078% 61.7778% 

30;70 71.7460% 71.1905% 63.4286% 

40;60 75.7407% 73.0556% 67.4074% 

50;50 77.4444% 72.8889% 67.4667% 

60;40 78.7500% 77.7778% 67.7778% 

70;30 80.7407% 80% 73.2593% 

80;20 85.5556% 84.4444% 76.8889% 

90;10 85% 83.8889% 79.8889% 



 

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Based on the results of the accuracy comparison in (Table 9), it can be concluded that the methods 

with polynomial kernel and linear kernel show better performance compared to the Gaussian kernel. 

The polynomial kernel has the highest accuracy value of 85.5556% at a ratio of 80:20, while the linear 

kernel achieves the highest accuracy value of 84.4444% at a ratio of 80:20. Meanwhile, the Gaussian 

kernel is recorded with the highest accuracy value of 79.8889% at a ratio of 90:10. 

Overall, the polynomial kernel tends to be more effective in handling larger test data, with more 

stable accuracy across data ratios. The linear kernel, although slightly lower, still shows consistent 

results, while the gaussian kernel produces lower accuracy across almost all test data ratios. This 

suggests that the polynomial and linear kernels are more suitable for this rice leaf disease classification 

than the gaussian kernel 

IV. Conclusions 

This study successfully built a rice leaf disease classification system using the Multi-Class Support 

Vector Machine (M-SVM) method. The test results showed that the polynomial kernel provided the 

highest accuracy of 85.56% at a training and testing ratio of 80:20, followed by the linear kernel 

(84.44%) and Gaussian (79.89%). GLCM and RGB-based feature extraction proved effective in 

supporting model performance through leaf texture and color analysis. 

 

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