Blended Features Classification of Leaf-Based Cucumber Disease Using Image Processing Techniques
Existing plant leaf disease detection approaches are based on features of extracting algorithms. These algorithms have some limits in feature selection for the diseased portion, but they can be used in conjunction with other image processing methods. Diseases of a plant can be classified from their...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2021-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2021/9736179 |
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| author | Jaweria Kainat Syed Sajid Ullah Fahd S. Alharithi Roobaea Alroobaea Saddam Hussain Shah Nazir |
| author_facet | Jaweria Kainat Syed Sajid Ullah Fahd S. Alharithi Roobaea Alroobaea Saddam Hussain Shah Nazir |
| author_sort | Jaweria Kainat |
| collection | DOAJ |
| description | Existing plant leaf disease detection approaches are based on features of extracting algorithms. These algorithms have some limits in feature selection for the diseased portion, but they can be used in conjunction with other image processing methods. Diseases of a plant can be classified from their symptoms. We proposed a cucumber leaf recognition approach, consisting of five steps: preprocessing, normalization, features extraction, features fusion, and classification. Otsu’s thresholding is implemented in preprocessing and Tan–Triggs normalization is applied for normalizing the dataset. During the features extraction step, texture and shape features are extracted. In addition, increasing the instances improves some characteristics. Through a principal component analysis approach, serial feature fusion is employed to provide a feature score. Fused features can be classified through a support vector machine. The accuracy of the Fine KNN is 94.30%, which is higher than the previous work in past papers. |
| format | Article |
| id | doaj-art-ab9da705a71c42bb81b479166aa0555b |
| institution | Kabale University |
| issn | 1099-0526 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-ab9da705a71c42bb81b479166aa0555b2025-02-03T01:04:21ZengWileyComplexity1099-05262021-01-01202110.1155/2021/9736179Blended Features Classification of Leaf-Based Cucumber Disease Using Image Processing TechniquesJaweria Kainat0Syed Sajid Ullah1Fahd S. Alharithi2Roobaea Alroobaea3Saddam Hussain4Shah Nazir5Department of Computer ScienceDepartment of Electrical and Computer EngineeringDepartment of Computer ScienceDepartment of Computer ScienceSchool of Digital ScienceDepartment of Computer ScienceExisting plant leaf disease detection approaches are based on features of extracting algorithms. These algorithms have some limits in feature selection for the diseased portion, but they can be used in conjunction with other image processing methods. Diseases of a plant can be classified from their symptoms. We proposed a cucumber leaf recognition approach, consisting of five steps: preprocessing, normalization, features extraction, features fusion, and classification. Otsu’s thresholding is implemented in preprocessing and Tan–Triggs normalization is applied for normalizing the dataset. During the features extraction step, texture and shape features are extracted. In addition, increasing the instances improves some characteristics. Through a principal component analysis approach, serial feature fusion is employed to provide a feature score. Fused features can be classified through a support vector machine. The accuracy of the Fine KNN is 94.30%, which is higher than the previous work in past papers.http://dx.doi.org/10.1155/2021/9736179 |
| spellingShingle | Jaweria Kainat Syed Sajid Ullah Fahd S. Alharithi Roobaea Alroobaea Saddam Hussain Shah Nazir Blended Features Classification of Leaf-Based Cucumber Disease Using Image Processing Techniques Complexity |
| title | Blended Features Classification of Leaf-Based Cucumber Disease Using Image Processing Techniques |
| title_full | Blended Features Classification of Leaf-Based Cucumber Disease Using Image Processing Techniques |
| title_fullStr | Blended Features Classification of Leaf-Based Cucumber Disease Using Image Processing Techniques |
| title_full_unstemmed | Blended Features Classification of Leaf-Based Cucumber Disease Using Image Processing Techniques |
| title_short | Blended Features Classification of Leaf-Based Cucumber Disease Using Image Processing Techniques |
| title_sort | blended features classification of leaf based cucumber disease using image processing techniques |
| url | http://dx.doi.org/10.1155/2021/9736179 |
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