Image-based oil palm leaf disease detection using convolutional neural network
Over the years, numerous studies have been conducted on the integration of computer vision and machine learning in plant disease detection. However, these conventional machine learning methods often require the contour segmentation of the infected region from the entire leaf region and the m...
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| Format: | Article |
| Language: | English |
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Universiti Utara Malaysia, UUM Press
2022
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| Subjects: | |
| Online Access: | http://eprints.uthm.edu.my/7718/ http://eprints.uthm.edu.my/7718/1/J14647_75bf33fb383ebf452acf5832c780a7bd.pdf |
| _version_ | 1848889186523807744 |
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| author | Jia, Heng Ong Pauline Ong, Pauline Ong Woon, Kiow Lee |
| author_facet | Jia, Heng Ong Pauline Ong, Pauline Ong Woon, Kiow Lee |
| author_sort | Jia, Heng Ong |
| building | UTHM Institutional Repository |
| collection | Online Access |
| description | Over the years, numerous studies have been conducted on the
integration of computer vision and machine learning in plant disease
detection. However, these conventional machine learning methods
often require the contour segmentation of the infected region from the
entire leaf region and the manual extraction of different discriminative
features before the classification models can be developed. In this
study, deep learning models, specifically, the AlexNet convolutional
neural network (CNN) and the combination of AlexNet and support
vector machine (AlexNet-SVM), which overcome the limitation
of handcrafting of feature representation were implemented for oil
palm leaf disease identification. The images of healthy and infected
leaf samples were collected, resized, and renamed before the model
training. These images were directly used to fit the classification models, without the need for segmentation and feature extraction as in
the conventional machine learning methods. The optimal architecture
of AlexNet CNN and AlexNet-SVM models were then determined
and subsequently applied for the oil palm leaf disease identification.
Comparative studies showed that the overall performance of the
AlexNet CNN model outperformed AlexNet-SVM-based classifier. |
| first_indexed | 2025-11-15T20:22:10Z |
| format | Article |
| id | uthm-7718 |
| institution | Universiti Tun Hussein Onn Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T20:22:10Z |
| publishDate | 2022 |
| publisher | Universiti Utara Malaysia, UUM Press |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | uthm-77182022-09-22T07:12:44Z http://eprints.uthm.edu.my/7718/ Image-based oil palm leaf disease detection using convolutional neural network Jia, Heng Ong Pauline Ong, Pauline Ong Woon, Kiow Lee T Technology (General) Over the years, numerous studies have been conducted on the integration of computer vision and machine learning in plant disease detection. However, these conventional machine learning methods often require the contour segmentation of the infected region from the entire leaf region and the manual extraction of different discriminative features before the classification models can be developed. In this study, deep learning models, specifically, the AlexNet convolutional neural network (CNN) and the combination of AlexNet and support vector machine (AlexNet-SVM), which overcome the limitation of handcrafting of feature representation were implemented for oil palm leaf disease identification. The images of healthy and infected leaf samples were collected, resized, and renamed before the model training. These images were directly used to fit the classification models, without the need for segmentation and feature extraction as in the conventional machine learning methods. The optimal architecture of AlexNet CNN and AlexNet-SVM models were then determined and subsequently applied for the oil palm leaf disease identification. Comparative studies showed that the overall performance of the AlexNet CNN model outperformed AlexNet-SVM-based classifier. Universiti Utara Malaysia, UUM Press 2022 Article PeerReviewed text en http://eprints.uthm.edu.my/7718/1/J14647_75bf33fb383ebf452acf5832c780a7bd.pdf Jia, Heng Ong and Pauline Ong, Pauline Ong and Woon, Kiow Lee (2022) Image-based oil palm leaf disease detection using convolutional neural network. Journal of Information and Communication Technology (JICT), 21 (3). pp. 383-410. ISSN 1675-414X https://doi.org/10.32890/jict2022.21.4 |
| spellingShingle | T Technology (General) Jia, Heng Ong Pauline Ong, Pauline Ong Woon, Kiow Lee Image-based oil palm leaf disease detection using convolutional neural network |
| title | Image-based oil palm leaf disease detection using
convolutional neural network |
| title_full | Image-based oil palm leaf disease detection using
convolutional neural network |
| title_fullStr | Image-based oil palm leaf disease detection using
convolutional neural network |
| title_full_unstemmed | Image-based oil palm leaf disease detection using
convolutional neural network |
| title_short | Image-based oil palm leaf disease detection using
convolutional neural network |
| title_sort | image-based oil palm leaf disease detection using
convolutional neural network |
| topic | T Technology (General) |
| url | http://eprints.uthm.edu.my/7718/ http://eprints.uthm.edu.my/7718/ http://eprints.uthm.edu.my/7718/1/J14647_75bf33fb383ebf452acf5832c780a7bd.pdf |