Rice Diseases Classification by Residual Network 50 (RESNET50) and Support Vector Machine (SVM) Modeling
The rice crop is one of the most important food crops that depend on it globally. Therefore, farmers must preserve the production of this crop from infection with pests and diseases that lead to its destruction through artificial intelligence and deep learning techniques. A hybrid model combining a...
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| Format: | Article |
| Language: | English |
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Faculty of Computer Science and Mathematics, University of Kufa
2023-03-01
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| Series: | Journal of Kufa for Mathematics and Computer |
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| Online Access: | https://journal.uokufa.edu.iq/index.php/jkmc/article/view/11320 |
| _version_ | 1827879166312185856 |
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| author | douaa S. Alwan Mohammed.H. Naji |
| author_facet | douaa S. Alwan Mohammed.H. Naji |
| author_sort | douaa S. Alwan |
| collection | DOAJ |
| description |
The rice crop is one of the most important food crops that depend on it globally. Therefore, farmers must preserve the production of this crop from infection with pests and diseases that lead to its destruction through artificial intelligence and deep learning techniques. A hybrid model combining a Residual Network 50 (ResNet50) deep convolutional neural network (CNN) and a support vector machine (SVM) developed diagnoses rice diseases. Farmers or people working in agriculture could use this model to quickly and accurately identify the diseases in their crops and treat them, increasing crop yield and reducing the need for costly and time-consuming manual inspection. ResNet50, a deep learning model effective at image classification tasks, was used to extract features from images of rice plants. SVM was then used to classify the diseases based on these features. The ResNet50 was able to capture complex patterns in the images, while the SVM was able to use these patterns to make accurate classification decisions. This hybrid model allowed for high precision in rice disease diagnosis, achieving an accuracy of approximately 99%.
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| first_indexed | 2024-03-12T18:02:53Z |
| format | Article |
| id | doaj.art-de15411c0d16433fad497d4610fbbe46 |
| institution | Directory Open Access Journal |
| issn | 2076-1171 2518-0010 |
| language | English |
| last_indexed | 2024-03-12T18:02:53Z |
| publishDate | 2023-03-01 |
| publisher | Faculty of Computer Science and Mathematics, University of Kufa |
| record_format | Article |
| series | Journal of Kufa for Mathematics and Computer |
| spelling | doaj.art-de15411c0d16433fad497d4610fbbe462023-08-02T09:35:42ZengFaculty of Computer Science and Mathematics, University of KufaJournal of Kufa for Mathematics and Computer2076-11712518-00102023-03-0110110.31642/JoKMC/2018/100114Rice Diseases Classification by Residual Network 50 (RESNET50) and Support Vector Machine (SVM) Modelingdouaa S. Alwan0https://orcid.org/0000-0002-5872-6256Mohammed.H. Naji1University of KufaUniversity of Kufa The rice crop is one of the most important food crops that depend on it globally. Therefore, farmers must preserve the production of this crop from infection with pests and diseases that lead to its destruction through artificial intelligence and deep learning techniques. A hybrid model combining a Residual Network 50 (ResNet50) deep convolutional neural network (CNN) and a support vector machine (SVM) developed diagnoses rice diseases. Farmers or people working in agriculture could use this model to quickly and accurately identify the diseases in their crops and treat them, increasing crop yield and reducing the need for costly and time-consuming manual inspection. ResNet50, a deep learning model effective at image classification tasks, was used to extract features from images of rice plants. SVM was then used to classify the diseases based on these features. The ResNet50 was able to capture complex patterns in the images, while the SVM was able to use these patterns to make accurate classification decisions. This hybrid model allowed for high precision in rice disease diagnosis, achieving an accuracy of approximately 99%. https://journal.uokufa.edu.iq/index.php/jkmc/article/view/11320Convolutional Neural NetworkDeep Learning ResNet50Rice DiseasesSupport Vector Machine SVM. |
| spellingShingle | douaa S. Alwan Mohammed.H. Naji Rice Diseases Classification by Residual Network 50 (RESNET50) and Support Vector Machine (SVM) Modeling Journal of Kufa for Mathematics and Computer Convolutional Neural Network Deep Learning ResNet50 Rice Diseases Support Vector Machine SVM. |
| title | Rice Diseases Classification by Residual Network 50 (RESNET50) and Support Vector Machine (SVM) Modeling |
| title_full | Rice Diseases Classification by Residual Network 50 (RESNET50) and Support Vector Machine (SVM) Modeling |
| title_fullStr | Rice Diseases Classification by Residual Network 50 (RESNET50) and Support Vector Machine (SVM) Modeling |
| title_full_unstemmed | Rice Diseases Classification by Residual Network 50 (RESNET50) and Support Vector Machine (SVM) Modeling |
| title_short | Rice Diseases Classification by Residual Network 50 (RESNET50) and Support Vector Machine (SVM) Modeling |
| title_sort | rice diseases classification by residual network 50 resnet50 and support vector machine svm modeling |
| topic | Convolutional Neural Network Deep Learning ResNet50 Rice Diseases Support Vector Machine SVM. |
| url | https://journal.uokufa.edu.iq/index.php/jkmc/article/view/11320 |
| work_keys_str_mv | AT douaasalwan ricediseasesclassificationbyresidualnetwork50resnet50andsupportvectormachinesvmmodeling AT mohammedhnaji ricediseasesclassificationbyresidualnetwork50resnet50andsupportvectormachinesvmmodeling |