- Title
- Maize seed variety identification model using image processing and deep learning
- Creator
- Gebeyehu, Seffi, Shibeshi, Zelalem S
- Date
- 2024
- Type
- text
- Type
- article
- Identifier
- http://hdl.handle.net/10962/429191
- Identifier
- vital:72566
- Identifier
- DOI: 10.11591/ijeecs.v33.i2.pp990-9985
- Description
- Maize is Ethiopia’s dominant cereal crop regarding area coverage and production level. There are different varieties of maize in Ethiopia. Maize varieties are classified based on morphological features such as shape and size. Due to the nature of maize seed and its rotation variant, studies are still needed to identify Ethiopian maize seed varieties. With expert eyes, identification of maize seed varieties is difficult due to their similar morphological features and visual similarities. We proposed a hybrid feature-based maize variety identification model to solve this problem. For training and testing the model, images of each maize variety were collected from the adet agriculture and research center (AARC), Ethiopia. A multi-class support vector machine (MCSVM) classifier was employed on a hybrid of handcrafted (ie, gabor and histogram of oriented gradients) and convolutional neural network (CNN)-based feature selection techniques and achieved an overall classification accuracy of 99%.
- Format
- 8 pages, pdf
- Language
- English
- Relation
- Indonesian Journal of Electrical Engineering and Computer Science, Gebeyehu, S. and Shibeshi, Z.S., 2024. Maize seed variety identification model using image processing and deep learning. Indonesian Journal of Electrical Engineering and Computer Science, 33(2), pp.990-998, Indonesian Journal of Electrical Engineering and Computer Science volume 33 number 2 990 998 2024 2502-4760
- Rights
- Publisher
- Rights
- Use of this resource is governed by the terms and conditions of Indonesian Journal of Electrical Engineering and Computer Science Statement (https://ijeecs.iaescore.com/index.php/IJEECS/about/editorialPolicies#openAccessPolicy)
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Collections
Shibeshi, Zelalem Sintayehu (Dr)
RU Department of Computer Science
SDG 09. Industry, Innovation and Infrastructure
SDG 15. Life On Land
| Thumbnail | File | Description | Size | Format | |||
|---|---|---|---|---|---|---|---|
| View Details | SOURCE1 | Maize seed variety identification model using image processing and deep learning.pdf | 274 KB | Adobe Acrobat PDF | View Details |