Classification Of Rice Plant Diseases Based on Leaf Images Using the Multi Class Support Vector Machine (M-SVM) Method
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 Sup...
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| Format: | Article |
| Language: | English |
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Universitas Buana Perjuangan Karawang
2025-07-01
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| Series: | Buana Information Technology and Computer Sciences |
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| Online Access: | https://journal.ubpkarawang.ac.id/index.php/bit-cs/article/view/10164 |
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| author | Febiana Angela tanesab Rangga Pahlevi Putra Aviv Yuniar Rahman |
| author_facet | Febiana Angela tanesab Rangga Pahlevi Putra Aviv Yuniar Rahman |
| author_sort | Febiana Angela tanesab |
| collection | DOAJ |
| description | 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 |
| format | Article |
| id | doaj-art-4cd5fbfc5a154d1f82671d9cbdda8f5a |
| institution | Matheson Library |
| issn | 2715-2448 2715-7199 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Universitas Buana Perjuangan Karawang |
| record_format | Article |
| series | Buana Information Technology and Computer Sciences |
| spelling | doaj-art-4cd5fbfc5a154d1f82671d9cbdda8f5a2025-07-30T15:25:03ZengUniversitas Buana Perjuangan KarawangBuana Information Technology and Computer Sciences2715-24482715-71992025-07-0162667710.36805/bitcs.v6i2.101648866Classification Of Rice Plant Diseases Based on Leaf Images Using the Multi Class Support Vector Machine (M-SVM) MethodFebiana Angela tanesab0Rangga Pahlevi Putra1Aviv Yuniar Rahman2Universitas Widya Gama MalangUniversitas Widya Gama MalangUniversitas Widya Gama MalangThe 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 featureshttps://journal.ubpkarawang.ac.id/index.php/bit-cs/article/view/10164accuracydisease classificationglcmleaf imagem-svmrice |
| spellingShingle | Febiana Angela tanesab Rangga Pahlevi Putra Aviv Yuniar Rahman Classification Of Rice Plant Diseases Based on Leaf Images Using the Multi Class Support Vector Machine (M-SVM) Method Buana Information Technology and Computer Sciences accuracy disease classification glcm leaf image m-svm rice |
| title | Classification Of Rice Plant Diseases Based on Leaf Images Using the Multi Class Support Vector Machine (M-SVM) Method |
| title_full | Classification Of Rice Plant Diseases Based on Leaf Images Using the Multi Class Support Vector Machine (M-SVM) Method |
| title_fullStr | Classification Of Rice Plant Diseases Based on Leaf Images Using the Multi Class Support Vector Machine (M-SVM) Method |
| title_full_unstemmed | Classification Of Rice Plant Diseases Based on Leaf Images Using the Multi Class Support Vector Machine (M-SVM) Method |
| title_short | Classification Of Rice Plant Diseases Based on Leaf Images Using the Multi Class Support Vector Machine (M-SVM) Method |
| title_sort | classification of rice plant diseases based on leaf images using the multi class support vector machine m svm method |
| topic | accuracy disease classification glcm leaf image m-svm rice |
| url | https://journal.ubpkarawang.ac.id/index.php/bit-cs/article/view/10164 |
| work_keys_str_mv | AT febianaangelatanesab classificationofriceplantdiseasesbasedonleafimagesusingthemulticlasssupportvectormachinemsvmmethod AT ranggapahleviputra classificationofriceplantdiseasesbasedonleafimagesusingthemulticlasssupportvectormachinemsvmmethod AT avivyuniarrahman classificationofriceplantdiseasesbasedonleafimagesusingthemulticlasssupportvectormachinemsvmmethod |