Herbify: an ensemble deep learning framework integrating convolutional neural networks and vision transformers for precise herb identification
| dc.contributor.author | Sheth, Farhan | |
| dc.contributor.author | Chatter, Ishika | |
| dc.contributor.author | Jasra, Manvendra | |
| dc.contributor.author | Kumar, Gireesh | |
| dc.contributor.author | Sharma, Richa | |
| dc.date.accessioned | 2025-08-10T09:26:28Z | |
| dc.date.available | 2025-08-10T09:26:28Z | |
| dc.date.issued | 2025 | |
| dc.description.abstract | Herbs have historically been central to medicinal practices, representing one of the earliest forms of therapeutic intervention. While synthetic drugs are often highly effective in treating acute conditions, their use is frequently accompanied by adverse side effects. In addition, the growing dependence on synthetic pharmaceuticals has raised concerns regarding affordability, thereby fostering a renewed interest in herbal medicine as a cost-effective and holistic alternative. | en_US |
| dc.identifier.uri | http://hdl.handle.net/123456789/6572 | |
| dc.language.iso | en | en_US |
| dc.publisher | Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. | en_US |
| dc.subject | Herbify: an ensemble deep learning | en_US |
| dc.title | Herbify: an ensemble deep learning framework integrating convolutional neural networks and vision transformers for precise herb identification | en_US |
| dc.type | Article | en_US |
