Identification of Medicinal Plants using Machine Learning Algorithms

DOI

10.2991/978-94-6239-628-9_5How to use a DOI?

Keywords

Medicinal plants; image processing; machine learning; plant identification; Stochastic Gradient Descent (SGD); ResNet50; Flask; computer vision; raw material classification; herbal authentication; Ayurvedic medicine

Abstract

India has a staggering diversity in flora, which helps to provide a vast number of plants that could be used traditionally in the preparation of medicines in the fields of Ayurvedic or Unani medicines. But it has been a big problem to identify the plants and their materials because of their close resemblance among each other in the natural flora group. These are some of the reasons for adulteration, substitution, and reduction of confidence regarding Ayurvedic formulations. Based on this, the present study proposes a completely developed system for the automation and improvement of the identification process of medicinal plants by using image processing techniques combined with advanced machine learning algorithms. High-quality images of the leaf and raw material are passed through feature extraction methods and classified using ResNet50 as a feature extractor along with the Stochastic Gradient Descent (SGD) classifier and comparative studies involving Support Vector Machines (SVM) and Random Forest (RF). The AI model so trained achieved an accuracy of 97% ensuring the reliable identification of medicinal plant species. The system also integrates Flask-a backend web framework, acting as a bridge between the pre-trained AI model and an interactive web-based front end, which thus enables real-time, seamless processing of user-uploaded images via RESTful APIs, returning immediate identification results. The scalable and easy-to-use platform developed in the current study has practical applicability throughout the herbal value chain to as-sure authenticity, minimize identification errors, and support quality assurance measures in the Ayurveda pharmaceutical industry.

Copyright

© 2026 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Saurabh Sarkar
AU  - Rishabh Singh
AU  - Subhasish Mondal
AU  - Sogi Manasa
PY  - 2026
DA  - 2026/03/31
TI  - Identification of Medicinal Plants using Machine Learning Algorithms
BT  - Proceedings of the International Conference on Recent Trends in Intelligent Computing, Manufacturing, and Electronics (rTIME 2025)
PB  - Atlantis Press
SP  - 39
EP  - 46
SN  - 2352-5401
UR  - https://doi.org/10.2991/978-94-6239-628-9_5
DO  - 10.2991/978-94-6239-628-9_5
ID  - Sarkar2026
ER  -