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Herbify: an ensemble deep learning framework integrating convolutional neural networks and vision transformers for precise herb identification

dc.contributor.authorSheth, Farhan
dc.contributor.authorChatter, Ishika
dc.contributor.authorJasra, Manvendra
dc.contributor.authorKumar, Gireesh
dc.contributor.authorSharma, Richa
dc.date.accessioned2025-08-10T09:26:28Z
dc.date.available2025-08-10T09:26:28Z
dc.date.issued2025
dc.description.abstractHerbs 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.urihttp://hdl.handle.net/123456789/6572
dc.language.isoenen_US
dc.publisherSpringer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.en_US
dc.subjectHerbify: an ensemble deep learningen_US
dc.titleHerbify: an ensemble deep learning framework integrating convolutional neural networks and vision transformers for precise herb identificationen_US
dc.typeArticleen_US

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