Herbal Plant Identification Using Deep Learning
From traditional medicine to today’s research in pharmacology, herbal plants are seen as very important. Yet, correctly identifying herbal species is challenging since many species share the same features and must be classified by experienced taxonomists. Technological advances such as deep learning...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English English |
| Published: |
INTI International University
2025
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| Subjects: | |
| Online Access: | http://eprints.intimal.edu.my/2144/ http://eprints.intimal.edu.my/2144/1/jods2025_06.pdf http://eprints.intimal.edu.my/2144/2/687 |
| _version_ | 1848766932212252672 |
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| author | Md., Fouziya Ch., Divija K. Lakshmi, Priyanka G., Swathi N., Revathi |
| author_facet | Md., Fouziya Ch., Divija K. Lakshmi, Priyanka G., Swathi N., Revathi |
| author_sort | Md., Fouziya |
| building | INTI Institutional Repository |
| collection | Online Access |
| description | From traditional medicine to today’s research in pharmacology, herbal plants are seen as very important. Yet, correctly identifying herbal species is challenging since many species share the same features and must be classified by experienced taxonomists. Technological advances such as deep learning have provided a way to automate this work with improved accuracy. The proposed system identifies herbal plants by analyzing their images using Convolution Neural Networks (CNNs), which are known for being effective in computer vision. To ensure the dataset is strong, I used thousands of clear leaf pictures from various herbal plant species that were taken in many environmental settings. Before training, the images were processed in stages by normalizing them, creating variations, and separating important objects. To find the most suitable CNN, VGG16, ResNet50, and MobileNetV2 were assessed based on their accuracy, how efficient they are, and whether they could be used on mobile phones. By using transfer learning, the model could take advantage of previously trained models on huge image collections |
| first_indexed | 2025-11-14T11:59:00Z |
| format | Article |
| id | intimal-2144 |
| institution | INTI International University |
| institution_category | Local University |
| language | English English |
| last_indexed | 2025-11-14T11:59:00Z |
| publishDate | 2025 |
| publisher | INTI International University |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | intimal-21442025-06-20T09:38:13Z http://eprints.intimal.edu.my/2144/ Herbal Plant Identification Using Deep Learning Md., Fouziya Ch., Divija K. Lakshmi, Priyanka G., Swathi N., Revathi QA75 Electronic computers. Computer science RS Pharmacy and materia medica TK Electrical engineering. Electronics Nuclear engineering From traditional medicine to today’s research in pharmacology, herbal plants are seen as very important. Yet, correctly identifying herbal species is challenging since many species share the same features and must be classified by experienced taxonomists. Technological advances such as deep learning have provided a way to automate this work with improved accuracy. The proposed system identifies herbal plants by analyzing their images using Convolution Neural Networks (CNNs), which are known for being effective in computer vision. To ensure the dataset is strong, I used thousands of clear leaf pictures from various herbal plant species that were taken in many environmental settings. Before training, the images were processed in stages by normalizing them, creating variations, and separating important objects. To find the most suitable CNN, VGG16, ResNet50, and MobileNetV2 were assessed based on their accuracy, how efficient they are, and whether they could be used on mobile phones. By using transfer learning, the model could take advantage of previously trained models on huge image collections INTI International University 2025-06 Article PeerReviewed text en cc_by_4 http://eprints.intimal.edu.my/2144/1/jods2025_06.pdf text en cc_by_4 http://eprints.intimal.edu.my/2144/2/687 Md., Fouziya and Ch., Divija and K. Lakshmi, Priyanka and G., Swathi and N., Revathi (2025) Herbal Plant Identification Using Deep Learning. Journal of Data Science, 2025 (06). pp. 1-13. ISSN 2805-5160 http://ipublishing.intimal.edu.my/jods.html |
| spellingShingle | QA75 Electronic computers. Computer science RS Pharmacy and materia medica TK Electrical engineering. Electronics Nuclear engineering Md., Fouziya Ch., Divija K. Lakshmi, Priyanka G., Swathi N., Revathi Herbal Plant Identification Using Deep Learning |
| title | Herbal Plant Identification Using Deep Learning |
| title_full | Herbal Plant Identification Using Deep Learning |
| title_fullStr | Herbal Plant Identification Using Deep Learning |
| title_full_unstemmed | Herbal Plant Identification Using Deep Learning |
| title_short | Herbal Plant Identification Using Deep Learning |
| title_sort | herbal plant identification using deep learning |
| topic | QA75 Electronic computers. Computer science RS Pharmacy and materia medica TK Electrical engineering. Electronics Nuclear engineering |
| url | http://eprints.intimal.edu.my/2144/ http://eprints.intimal.edu.my/2144/ http://eprints.intimal.edu.my/2144/1/jods2025_06.pdf http://eprints.intimal.edu.my/2144/2/687 |