Automated Tomato Leaf Disease Detection Using Image Processing: An SVM-Based Approach with GLCM and SIFT Features
Tomato cultivation is increasingly widespread, yet it faces significant challenges, particularly from plant diseases caused by fungi, bacteria, and insects. Addressing these diseases is crucial for ensuring the quality and yield of tomato crops. To support specialists in accurately identifying and m...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2024-01-01
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| Series: | Journal of Engineering |
| Online Access: | http://dx.doi.org/10.1155/2024/9918296 |
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| _version_ | 1841561341883580416 |
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| author | Rashid Khan Nasir Ud Din Asim Zaman Bingding Huang |
| author_facet | Rashid Khan Nasir Ud Din Asim Zaman Bingding Huang |
| author_sort | Rashid Khan |
| collection | DOAJ |
| description | Tomato cultivation is increasingly widespread, yet it faces significant challenges, particularly from plant diseases caused by fungi, bacteria, and insects. Addressing these diseases is crucial for ensuring the quality and yield of tomato crops. To support specialists in accurately identifying and managing these diseases, we propose an advanced automatic system for detecting and identifying tomato leaf diseases using sophisticated image processing techniques. Therefore, we proposed an approach that employs robust feature extraction methods, including the gray level co-occurrence matrix (GLCM) and scale-invariant feature transform (SIFT), coupled with a support vector machine (SVM) for adequate classification. We curated an extensive dataset of 2700 tomato leaf images, with a minimum of 300 images for each of the nine distinct disease classes. This comprehensive dataset facilitated the training and testing of various machine learning and deep learning models. The experimental results highlight our proposed approach’s exceptional accuracy and reliability, significantly improving the detection and classification of tomato leaf diseases. A thorough comparative analysis with contemporary state-of-the-art techniques further validates the superiority of our system. Our findings suggest that this framework can significantly benefit tomato cultivation by enabling timely and precise disease management. Future research can explore integrating advanced deep learning algorithms to enhance the system’s accuracy in this multiclass classification challenge. |
| format | Article |
| id | doaj-art-7363adf6ed3646a0a7631c8f5297155e |
| institution | Kabale University |
| issn | 2314-4912 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Engineering |
| spelling | doaj-art-7363adf6ed3646a0a7631c8f5297155e2025-01-03T01:45:41ZengWileyJournal of Engineering2314-49122024-01-01202410.1155/2024/9918296Automated Tomato Leaf Disease Detection Using Image Processing: An SVM-Based Approach with GLCM and SIFT FeaturesRashid Khan0Nasir Ud Din1Asim Zaman2Bingding Huang3College of Big Data and InternetCollege of Electronics and Information EngineeringCollege of Health Science and Environmental EngineeringCollege of Big Data and InternetTomato cultivation is increasingly widespread, yet it faces significant challenges, particularly from plant diseases caused by fungi, bacteria, and insects. Addressing these diseases is crucial for ensuring the quality and yield of tomato crops. To support specialists in accurately identifying and managing these diseases, we propose an advanced automatic system for detecting and identifying tomato leaf diseases using sophisticated image processing techniques. Therefore, we proposed an approach that employs robust feature extraction methods, including the gray level co-occurrence matrix (GLCM) and scale-invariant feature transform (SIFT), coupled with a support vector machine (SVM) for adequate classification. We curated an extensive dataset of 2700 tomato leaf images, with a minimum of 300 images for each of the nine distinct disease classes. This comprehensive dataset facilitated the training and testing of various machine learning and deep learning models. The experimental results highlight our proposed approach’s exceptional accuracy and reliability, significantly improving the detection and classification of tomato leaf diseases. A thorough comparative analysis with contemporary state-of-the-art techniques further validates the superiority of our system. Our findings suggest that this framework can significantly benefit tomato cultivation by enabling timely and precise disease management. Future research can explore integrating advanced deep learning algorithms to enhance the system’s accuracy in this multiclass classification challenge.http://dx.doi.org/10.1155/2024/9918296 |
| spellingShingle | Rashid Khan Nasir Ud Din Asim Zaman Bingding Huang Automated Tomato Leaf Disease Detection Using Image Processing: An SVM-Based Approach with GLCM and SIFT Features Journal of Engineering |
| title | Automated Tomato Leaf Disease Detection Using Image Processing: An SVM-Based Approach with GLCM and SIFT Features |
| title_full | Automated Tomato Leaf Disease Detection Using Image Processing: An SVM-Based Approach with GLCM and SIFT Features |
| title_fullStr | Automated Tomato Leaf Disease Detection Using Image Processing: An SVM-Based Approach with GLCM and SIFT Features |
| title_full_unstemmed | Automated Tomato Leaf Disease Detection Using Image Processing: An SVM-Based Approach with GLCM and SIFT Features |
| title_short | Automated Tomato Leaf Disease Detection Using Image Processing: An SVM-Based Approach with GLCM and SIFT Features |
| title_sort | automated tomato leaf disease detection using image processing an svm based approach with glcm and sift features |
| url | http://dx.doi.org/10.1155/2024/9918296 |
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