- Title
- Selected medicinal plants leaves identification: a computer vision approach
- Creator
- Deyi, Avuya
- ThesisAdvisor
- Atemkeng, Marcellin, 1986-
- ThesisAdvisor
- Fadja, Arnaud Nguembang
- ThesisAdvisor
- Siwe-Noundou, Xavier
- Subject
- Deep learning (Machine learning)
- Subject
- Machine learning
- Subject
- Convolutional neural network
- Subject
- Computer vision in medicine
- Subject
- Medicinal plants
- Date
- 2023-10-13
- Type
- Academic theses
- Type
- Master's theses
- Type
- text
- Identifier
- http://hdl.handle.net/10962/424552
- Identifier
- vital:72163
- Description
- Identifying and classifying medicinal plants are valuable and essential skills during drug manufacturing because several active pharmaceutical ingredients (API) are sourced from medicinal plants. For many years, identifying and classifying medicinal plants have been exclusively done by experts in the domain, such as botanists, and herbarium curators. Recently, powerful computer vision technologies, using machine learning and deep convolutional neural networks, have been developed for classifying or identifying objects on images. A convolutional neural network is a deep learning architecture that outperforms previous advanced approaches in image classification and object detection based on its efficient features extraction on images. In this thesis, we investigate different convolutional neural networks and machine learning algorithms for identifying and classifying leaves of three species of the genus Brachylaena. The three species considered are Brachylaena discolor, Brachylaena ilicifolia and Brachylaena elliptica. All three species are used medicinally by people in South Africa to treat diseases like diabetes. From 1259 labelled images of those plants species (at least 400 for each species) split into training, evaluation and test sets, we trained and evaluated different deep convolutional neural networks and machine learning models. The VGG model achieved the best results with 98.26% accuracy from cross-validation.
- Description
- Thesis (MSc) -- Faculty of Science, Mathematics, 2023
- Format
- computer, online resource, application/pdf, 1 online resource (80 pages), pdf
- Publisher
- Rhodes University, Faculty of Science, Mathematics
- Language
- English
- Rights
- Deyi, Avuya
- Rights
- Use of this resource is governed by the terms and conditions of the Creative Commons "Attribution-NonCommercial-ShareAlike" License (http://creativecommons.org/licenses/by-nc-sa/2.0/)
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RU Department of Mathematics (Pure and Applied)
| Thumbnail | File | Description | Size | Format | |||
|---|---|---|---|---|---|---|---|
| View Details Download | SOURCE1 | DEYI-MSC-TR23-147.pdf | 1 MB | Adobe Acrobat PDF | View Details Download |