Support Vector Machine (SVM) for Tomato Leaf Disease Detection
Tomatoes rank among the top five most globally demanded crops and serve as a key ingredient in numerous dishes. However, productivity may decline due to challenges such as diseases, pest infestations, and climate change. Therefore, automatic disease detection is essential to identify early signs of...
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| Format: | Article |
| Language: | English |
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Universitas Brawijaya
2025-05-01
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| Series: | AGRIVITA Journal of Agricultural Science |
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| Online Access: | https://agrivita.ub.ac.id/index.php/agrivita/article/view/3746 |
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| author | Shafaf Ibrahim Nur Afiqah Mohd Fuad Nor Azura Md Ghani Raihah Aminuddin Budi Sunarko |
| author_facet | Shafaf Ibrahim Nur Afiqah Mohd Fuad Nor Azura Md Ghani Raihah Aminuddin Budi Sunarko |
| author_sort | Shafaf Ibrahim |
| collection | DOAJ |
| description | Tomatoes rank among the top five most globally demanded crops and serve as a key ingredient in numerous dishes. However, productivity may decline due to challenges such as diseases, pest infestations, and climate change. Therefore, automatic disease detection is essential to identify early signs of illness during the growth period. This study proposes a method for detecting tomato leaf diseases using image processing techniques. The approach involves image enhancement, feature extraction, and classification. Initially, leaf disease images were enhanced using the Contrast Adjustment technique. Subsequently, color and texture features were extracted using Color Moments and the Gray-Level Co-occurrence Matrix (GLCM), respectively. Disease detection was carried out using a Support Vector Machine (SVM). The method was tested on 50 images each for healthy leaves and four types of tomato leaf diseases: Bacterial Spot, Yellow Leaf Curl Virus, Early Blight, and Late Blight. The performance of the disease detection system was evaluated using a confusion matrix, achieving an overall accuracy, sensitivity, and specificity of 96%, 90%, and 97.5%, respectively. These results demonstrate the effectiveness of the proposed SVM-based approach for tomato leaf disease detection. |
| format | Article |
| id | doaj-art-94e8d59293e34965ab223217cd1df034 |
| institution | DOAJ |
| issn | 0126-0537 2477-8516 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Universitas Brawijaya |
| record_format | Article |
| series | AGRIVITA Journal of Agricultural Science |
| spelling | doaj-art-94e8d59293e34965ab223217cd1df0342025-08-20T03:21:55ZengUniversitas BrawijayaAGRIVITA Journal of Agricultural Science0126-05372477-85162025-05-0147233835310.17503/agrivita.v47i2.3746862Support Vector Machine (SVM) for Tomato Leaf Disease DetectionShafaf Ibrahim0Nur Afiqah Mohd Fuad1Nor Azura Md Ghani2Raihah Aminuddin3Budi Sunarko4School of Computing Sciences, College of Computing, Informatics and Mathematics, Universiti Teknologi MARA, Shah Alam, MalaysiaSchool of Computing Sciences, College of Computing, Informatics and Mathematics, Universiti Teknologi MARA, Melaka, MalaysiaSchool of Computing Sciences, College of Computing, Informatics and Mathematics, Universiti Teknologi MARA, Shah Alam, MalaysiaSchool of Computing Sciences, College of Computing, Informatics and Mathematics, Universiti Teknologi MARA, Melaka, MalaysiaDepartment of Electrical Engineering, Universitas Negeri Semarang, Semarang, Central Java, IndonesiaTomatoes rank among the top five most globally demanded crops and serve as a key ingredient in numerous dishes. However, productivity may decline due to challenges such as diseases, pest infestations, and climate change. Therefore, automatic disease detection is essential to identify early signs of illness during the growth period. This study proposes a method for detecting tomato leaf diseases using image processing techniques. The approach involves image enhancement, feature extraction, and classification. Initially, leaf disease images were enhanced using the Contrast Adjustment technique. Subsequently, color and texture features were extracted using Color Moments and the Gray-Level Co-occurrence Matrix (GLCM), respectively. Disease detection was carried out using a Support Vector Machine (SVM). The method was tested on 50 images each for healthy leaves and four types of tomato leaf diseases: Bacterial Spot, Yellow Leaf Curl Virus, Early Blight, and Late Blight. The performance of the disease detection system was evaluated using a confusion matrix, achieving an overall accuracy, sensitivity, and specificity of 96%, 90%, and 97.5%, respectively. These results demonstrate the effectiveness of the proposed SVM-based approach for tomato leaf disease detection.https://agrivita.ub.ac.id/index.php/agrivita/article/view/3746classificationdetectionfeature extractionleaf diseasesupport vector machine (svm) |
| spellingShingle | Shafaf Ibrahim Nur Afiqah Mohd Fuad Nor Azura Md Ghani Raihah Aminuddin Budi Sunarko Support Vector Machine (SVM) for Tomato Leaf Disease Detection AGRIVITA Journal of Agricultural Science classification detection feature extraction leaf disease support vector machine (svm) |
| title | Support Vector Machine (SVM) for Tomato Leaf Disease Detection |
| title_full | Support Vector Machine (SVM) for Tomato Leaf Disease Detection |
| title_fullStr | Support Vector Machine (SVM) for Tomato Leaf Disease Detection |
| title_full_unstemmed | Support Vector Machine (SVM) for Tomato Leaf Disease Detection |
| title_short | Support Vector Machine (SVM) for Tomato Leaf Disease Detection |
| title_sort | support vector machine svm for tomato leaf disease detection |
| topic | classification detection feature extraction leaf disease support vector machine (svm) |
| url | https://agrivita.ub.ac.id/index.php/agrivita/article/view/3746 |
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