Medicinal Plant Identification via ResNet50 Transfer Learning
Vadlapatla Bhavana
, Mendu Priya Spandana , Manda Sowmya , Rayapati Venkata Sudhakar
ResNet50, medicinal plants, transfer learning, deep learning
Traditional methods of identifying medicinal herbs present significant challenges to various stakeholders involved in herbal medicine. Herb collectors often rely on experience and expertise passed down through generations, which can be subjective and inconsistent. Researchers face hurdles in accurately cataloging and studying medicinal plant species due to manual identification processes' laborious and time-consuming nature. Medicinal Plant Identification project merges state-of-the-art technology with age-old botanical wisdom to offer a holistic herb identification and usage recommendation solution. ResNet50 model was trained for the project and an intuitive web interface was developed using Flask, users can effortlessly upload images of medicinal plants and receive precise predictions.
"Medicinal Plant Identification via ResNet50 Transfer Learning", IJSDR - International Journal of Scientific Development and Research (www.IJSDR.org), ISSN:2455-2631, Vol.9, Issue 4, page no.1217 - 1221, April-2024, Available :https://ijsdr.org/papers/IJSDR2404176.pdf
Volume 9
Issue 4,
April-2024
Pages : 1217 - 1221
Paper Reg. ID: IJSDR_210985
Published Paper Id: IJSDR2404176
Downloads: 000347301
Research Area: Computer Science & Technology
Country: Hyderabad, Telangana, India
ISSN: 2455-2631 | IMPACT FACTOR: 9.15 Calculated By Google Scholar | ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 9.15 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator
Publisher: IJSDR(IJ Publication) Janvi Wave
