ALBULARYO: Advanced Learning for Botanical Understanding, Leveraging Artificial Recognition using Convolutional Neural Networks


Authors : Johani D. Basaula; David Austin L. Aguilar; Charletsone Maru; Jireh Joshua Pablo; Katsuya Shiong Suzuki; Arnel Balasta

Volume/Issue : Volume 9 - 2024, Issue 5 - May

Google Scholar : https://tinyurl.com/m7h2cx39

Scribd : https://tinyurl.com/2p9se4f9

DOI : https://doi.org/10.38124/ijisrt/IJISRT24MAY2456

Abstract : This study aims to develop and implement an herbal medicine scanner. HerbID, with its approach, has sparked a growing interest in remedies for their healing properties in today's world. This state of the art technology introduces an identification system that is reshaping how we engage with medicine. By combining a repository of solutions, with advanced scanning capabilities HerbID ensures a seamless user experience. Through a scan users can precisely recognize herbs empowering them to make informed choices regarding their health and overall wellness. HerbID offers more than identifying herbs – it offers in depth insights into the benefits, potential advantages and usage tips for each herb. With HerbID you can gain the knowledge and confidence to make the most of medicine whether you're a beginner or well versed in natural healing practices. Explore a range of remedies, with HerbID your go to herbal medicine companion.

References :

  1. Raclariu-Manolică, A. C., Mauvisseau, Q., & de Boer, H. J. (2023). Horizon scan of DNA-based methods for quality control and monitoring of herbal preparations. Frontiers in Pharmacology, 14, 1179099
  2. Klein-Junior, L. C., de Souza, M. R., Viaene, J., Bresolin, T. M., de Gasper, A. L., Henriques, A. T., & Vander Heyden, Y. (2021). Quality control of herbal medicines:  From  traditional  techniques  to state-of-the-art approaches. Planta medica, 87(12/13), 964-988.
  3. Cui, X., Song, L., Sun, J., & Zhou, H. (2024, March). Research on the application of intelligent Chinese herbal medicine identification technology. In International Conference on Computer Graphics, Artificial Intelligence, and Data Processing (ICCAID 2023) (Vol. 13105, pp. 571-577). SPIE.
  4. Kaur, P. P., Singh, S., & Pathak, M. (2021, April). Review of machine learning herbal plant recognition system. In Proceedings of the International Conference on Innovative Computing & Communications (ICICC) 2020, Proceedings of the International Conference on Innovative Computing & Communication (ICICC).
  5. Sharma, S., Naman, S., Dwivedi, J., & Baldi, A. (2023). Artificial Intelligence-Based Smart Identification System Using Herbal Images. Applications of Optimization and Machine Learning in Image Processing and IoT.
  6. https://medium.com/@yafonia/agile-in-a-nutshell-7725674ee31e

This study aims to develop and implement an herbal medicine scanner. HerbID, with its approach, has sparked a growing interest in remedies for their healing properties in today's world. This state of the art technology introduces an identification system that is reshaping how we engage with medicine. By combining a repository of solutions, with advanced scanning capabilities HerbID ensures a seamless user experience. Through a scan users can precisely recognize herbs empowering them to make informed choices regarding their health and overall wellness. HerbID offers more than identifying herbs – it offers in depth insights into the benefits, potential advantages and usage tips for each herb. With HerbID you can gain the knowledge and confidence to make the most of medicine whether you're a beginner or well versed in natural healing practices. Explore a range of remedies, with HerbID your go to herbal medicine companion.

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