Enhanced classification of medicinal plants using deep learning and optimized CNN architectures
This work highlights the medicinal flora, which is very essential for the conservation of biodiversity and the improvement of health throughout the world. More specifically, it underlines the need for accurate classification of medicinal plant species for their effective conservation and proper use....
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| Format: | Article |
| Language: | English |
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Elsevier
2025-02-01
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| Series: | Heliyon |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2405844025007650 |
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| author | Hicham Bouakkaz Mustapha Bouakkaz Chaker Abdelaziz Kerrache Sahraoui Dhelim |
| author_facet | Hicham Bouakkaz Mustapha Bouakkaz Chaker Abdelaziz Kerrache Sahraoui Dhelim |
| author_sort | Hicham Bouakkaz |
| collection | DOAJ |
| description | This work highlights the medicinal flora, which is very essential for the conservation of biodiversity and the improvement of health throughout the world. More specifically, it underlines the need for accurate classification of medicinal plant species for their effective conservation and proper use. The complexity of plant features and a lack of annotated datasets make them difficult for traditional classification methods. To address this issue, a deep learning-based framework is proposed in the research for classifying images related to medicinal plants using convolutional neural networks (CNNs). In this framework, a CNN architecture with residual and inverted residual block configurations is selected, and a set of data augmentation is applied to improve the dataset. Concerning feature selection, it adopts Binary Chimp Optimization and serial feature fusion regarding accuracy and speed. Experiments show that the proposed framework significantly outperforms conventional methods in the accurate classification of medicinal flora, and it suggests possible extensions for the identification of other plant species. This study provides evidence of the potential that deep learning models have in improving and automating identification and classification procedures for medicinal plants when integrated with botanical studies. |
| format | Article |
| id | doaj-art-24c7223fa0ab4be2b19c1d1db4ac45ad |
| institution | Kabale University |
| issn | 2405-8440 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Heliyon |
| spelling | doaj-art-24c7223fa0ab4be2b19c1d1db4ac45ad2025-02-05T04:32:19ZengElsevierHeliyon2405-84402025-02-01113e42385Enhanced classification of medicinal plants using deep learning and optimized CNN architecturesHicham Bouakkaz0Mustapha Bouakkaz1Chaker Abdelaziz Kerrache2Sahraoui Dhelim3Fondamental Sciences Laboratory, Université Amar Telidji de Laghouat, Laghouat, AlgeriaLaboratoire d'Informatique et de Mathématiques, Université Amar Telidji de Laghouat, Laghouat, AlgeriaLaboratoire d'Informatique et de Mathématiques, Université Amar Telidji de Laghouat, Laghouat, AlgeriaSchool of Computing, Dublin City University, Dublin, Ireland; Corresponding author.This work highlights the medicinal flora, which is very essential for the conservation of biodiversity and the improvement of health throughout the world. More specifically, it underlines the need for accurate classification of medicinal plant species for their effective conservation and proper use. The complexity of plant features and a lack of annotated datasets make them difficult for traditional classification methods. To address this issue, a deep learning-based framework is proposed in the research for classifying images related to medicinal plants using convolutional neural networks (CNNs). In this framework, a CNN architecture with residual and inverted residual block configurations is selected, and a set of data augmentation is applied to improve the dataset. Concerning feature selection, it adopts Binary Chimp Optimization and serial feature fusion regarding accuracy and speed. Experiments show that the proposed framework significantly outperforms conventional methods in the accurate classification of medicinal flora, and it suggests possible extensions for the identification of other plant species. This study provides evidence of the potential that deep learning models have in improving and automating identification and classification procedures for medicinal plants when integrated with botanical studies.http://www.sciencedirect.com/science/article/pii/S2405844025007650Medicinal plantClassificationCNNChimp optimizationFeature fusion |
| spellingShingle | Hicham Bouakkaz Mustapha Bouakkaz Chaker Abdelaziz Kerrache Sahraoui Dhelim Enhanced classification of medicinal plants using deep learning and optimized CNN architectures Heliyon Medicinal plant Classification CNN Chimp optimization Feature fusion |
| title | Enhanced classification of medicinal plants using deep learning and optimized CNN architectures |
| title_full | Enhanced classification of medicinal plants using deep learning and optimized CNN architectures |
| title_fullStr | Enhanced classification of medicinal plants using deep learning and optimized CNN architectures |
| title_full_unstemmed | Enhanced classification of medicinal plants using deep learning and optimized CNN architectures |
| title_short | Enhanced classification of medicinal plants using deep learning and optimized CNN architectures |
| title_sort | enhanced classification of medicinal plants using deep learning and optimized cnn architectures |
| topic | Medicinal plant Classification CNN Chimp optimization Feature fusion |
| url | http://www.sciencedirect.com/science/article/pii/S2405844025007650 |
| work_keys_str_mv | AT hichambouakkaz enhancedclassificationofmedicinalplantsusingdeeplearningandoptimizedcnnarchitectures AT mustaphabouakkaz enhancedclassificationofmedicinalplantsusingdeeplearningandoptimizedcnnarchitectures AT chakerabdelazizkerrache enhancedclassificationofmedicinalplantsusingdeeplearningandoptimizedcnnarchitectures AT sahraouidhelim enhancedclassificationofmedicinalplantsusingdeeplearningandoptimizedcnnarchitectures |