Improved Support Vector Machine and Image Processing Enabled Methodology for Detection and Classification of Grape Leaf Disease
In recent years, agricultural image processing research has been a key emphasis. Image processing techniques are used by computers to analyze images. New advancements in image capture and data processing have simplified the resolution of a wide range of agricultural concerns. Crop disease classifica...
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| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2022-01-01
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| Series: | Journal of Food Quality |
| Online Access: | http://dx.doi.org/10.1155/2022/9502475 |
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| author | Arshiya S. Ansari Malik Jawarneh Mahyudin Ritonga Pragti Jamwal Mohammad Sajid Mohammadi Ravi Kishore Veluri Virendra Kumar Mohd Asif Shah |
| author_facet | Arshiya S. Ansari Malik Jawarneh Mahyudin Ritonga Pragti Jamwal Mohammad Sajid Mohammadi Ravi Kishore Veluri Virendra Kumar Mohd Asif Shah |
| author_sort | Arshiya S. Ansari |
| collection | DOAJ |
| description | In recent years, agricultural image processing research has been a key emphasis. Image processing techniques are used by computers to analyze images. New advancements in image capture and data processing have simplified the resolution of a wide range of agricultural concerns. Crop disease classification and identification are crucial for the agricultural industry’s technical and commercial well-being. In agriculture, image processing begins with a digital color picture of a diseased leaf. Plant health and disease detection must be monitored on a regular basis in property agriculture. Plant diseases have had a tremendous impact on civilization and the Earth as a whole. Extensions of detection strategies and classification methods try to identify and categorize each ailment that affects the plant rather than focusing on a single disease among several illnesses and symptoms. This article describes a new support vector machine and image processing-enabled approach for detecting and classifying grape leaf disease. The given architecture includes steps for image capture, denoising, enhancement, segmentation, feature extraction, classification, and detection. Image denoising is conducted using the mean function, image enhancement is performed using the CLAHE method, pictures are segmented using the fuzzy C Means algorithm, features are retrieved using PCA, and images are eventually classed using the PSO SVM, BPNN, and random forest algorithms. The accuracy of PSO SVM is higher in performing classification and detection of grape leaf diseases. |
| format | Article |
| id | doaj-art-14c7f884058b4cb4b99ab1fa96978036 |
| institution | Kabale University |
| issn | 1745-4557 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Food Quality |
| spelling | doaj-art-14c7f884058b4cb4b99ab1fa969780362025-02-03T05:51:01ZengWileyJournal of Food Quality1745-45572022-01-01202210.1155/2022/9502475Improved Support Vector Machine and Image Processing Enabled Methodology for Detection and Classification of Grape Leaf DiseaseArshiya S. Ansari0Malik Jawarneh1Mahyudin Ritonga2Pragti Jamwal3Mohammad Sajid Mohammadi4Ravi Kishore Veluri5Virendra Kumar6Mohd Asif Shah7Department of Information TechnologyFaculty of Computing SciencesUniversitas Muhammadiyah Sumatera BaratModel Institute of Engineering and TechnologyDepartment of Information TechnologyAditya Engineering College (A)Department of Plant ScienceBakhtar UniversityIn recent years, agricultural image processing research has been a key emphasis. Image processing techniques are used by computers to analyze images. New advancements in image capture and data processing have simplified the resolution of a wide range of agricultural concerns. Crop disease classification and identification are crucial for the agricultural industry’s technical and commercial well-being. In agriculture, image processing begins with a digital color picture of a diseased leaf. Plant health and disease detection must be monitored on a regular basis in property agriculture. Plant diseases have had a tremendous impact on civilization and the Earth as a whole. Extensions of detection strategies and classification methods try to identify and categorize each ailment that affects the plant rather than focusing on a single disease among several illnesses and symptoms. This article describes a new support vector machine and image processing-enabled approach for detecting and classifying grape leaf disease. The given architecture includes steps for image capture, denoising, enhancement, segmentation, feature extraction, classification, and detection. Image denoising is conducted using the mean function, image enhancement is performed using the CLAHE method, pictures are segmented using the fuzzy C Means algorithm, features are retrieved using PCA, and images are eventually classed using the PSO SVM, BPNN, and random forest algorithms. The accuracy of PSO SVM is higher in performing classification and detection of grape leaf diseases.http://dx.doi.org/10.1155/2022/9502475 |
| spellingShingle | Arshiya S. Ansari Malik Jawarneh Mahyudin Ritonga Pragti Jamwal Mohammad Sajid Mohammadi Ravi Kishore Veluri Virendra Kumar Mohd Asif Shah Improved Support Vector Machine and Image Processing Enabled Methodology for Detection and Classification of Grape Leaf Disease Journal of Food Quality |
| title | Improved Support Vector Machine and Image Processing Enabled Methodology for Detection and Classification of Grape Leaf Disease |
| title_full | Improved Support Vector Machine and Image Processing Enabled Methodology for Detection and Classification of Grape Leaf Disease |
| title_fullStr | Improved Support Vector Machine and Image Processing Enabled Methodology for Detection and Classification of Grape Leaf Disease |
| title_full_unstemmed | Improved Support Vector Machine and Image Processing Enabled Methodology for Detection and Classification of Grape Leaf Disease |
| title_short | Improved Support Vector Machine and Image Processing Enabled Methodology for Detection and Classification of Grape Leaf Disease |
| title_sort | improved support vector machine and image processing enabled methodology for detection and classification of grape leaf disease |
| url | http://dx.doi.org/10.1155/2022/9502475 |
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