Local Herb Identification Using Transfer Learning: A CNN-Powered Mobile Application for Nepalese Flora
Fuente:
arXiv
Guardado en:
| Autores principales: | , , , |
|---|---|
| Formato: | Preprint |
| Publicado: |
2025
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| _version_ | 1866918008707874816 |
|---|---|
| author | Thapa, Prajwal Sharma, Mridul Nyachhyon, Jinu Pandeya, Yagya Raj |
| author_facet | Thapa, Prajwal Sharma, Mridul Nyachhyon, Jinu Pandeya, Yagya Raj |
| contents | Herb classification presents a critical challenge in botanical research, particularly in regions with rich biodiversity such as Nepal. This study introduces a novel deep learning approach for classifying 60 different herb species using Convolutional Neural Networks (CNNs) and transfer learning techniques. Using a manually curated dataset of 12,000 herb images, we developed a robust machine learning model that addresses existing limitations in herb recognition methodologies. Our research employed multiple model architectures, including DenseNet121, 50-layer Residual Network (ResNet50), 16-layer Visual Geometry Group Network (VGG16), InceptionV3, EfficientNetV2, and Vision Transformer (VIT), with DenseNet121 ultimately demonstrating superior performance. Data augmentation and regularization techniques were applied to mitigate overfitting and enhance the generalizability of the model. This work advances herb classification techniques, preserving traditional botanical knowledge and promoting sustainable herb utilization. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_02147 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Local Herb Identification Using Transfer Learning: A CNN-Powered Mobile Application for Nepalese Flora Thapa, Prajwal Sharma, Mridul Nyachhyon, Jinu Pandeya, Yagya Raj Machine Learning Computer Vision and Pattern Recognition I.4.9 Herb classification presents a critical challenge in botanical research, particularly in regions with rich biodiversity such as Nepal. This study introduces a novel deep learning approach for classifying 60 different herb species using Convolutional Neural Networks (CNNs) and transfer learning techniques. Using a manually curated dataset of 12,000 herb images, we developed a robust machine learning model that addresses existing limitations in herb recognition methodologies. Our research employed multiple model architectures, including DenseNet121, 50-layer Residual Network (ResNet50), 16-layer Visual Geometry Group Network (VGG16), InceptionV3, EfficientNetV2, and Vision Transformer (VIT), with DenseNet121 ultimately demonstrating superior performance. Data augmentation and regularization techniques were applied to mitigate overfitting and enhance the generalizability of the model. This work advances herb classification techniques, preserving traditional botanical knowledge and promoting sustainable herb utilization. |
| title | Local Herb Identification Using Transfer Learning: A CNN-Powered Mobile Application for Nepalese Flora |
| topic | Machine Learning Computer Vision and Pattern Recognition I.4.9 |
| url | https://arxiv.org/abs/2505.02147 |