Bridging AI and Ethnobotany: A Deep Learning Approach for Medicinal Plant Identification and Real-World Deployment

DOI

10.2991/978-94-6239-664-7_57How to use a DOI?

Keywords

Medicinal Plant Classification; Deep Learning; Convolutional Neural Networks; Computer Vision; EfficientNet; Web-Based Deployment

Abstract

Precise identification of medicinal plants is relevant to pharmacological studies and proper use of species, but most currently used image-based methods are tested on small-scale data and do not provide much information on the extrapolation of models. The paper examines how deep learning can be used to classify ten medicinal plant species, which are commonly used in rural and semi-urban areas in Bangladesh. An augmented subset of 5,000 images was augmented to 10,000 and divided into training, validation and test subsets. A variety of convolutional neural networks models, such as EfficientNetB3, InceptionV3, MobileNetV2, and VGG19 were trained and compared. The highest accuracy (99.00%) was attained by Efficient- NetB3 as compared to the other models. Nevertheless, such high accuracy shows that it is necessary to further validate it, including cross-validation and external dataset testing, to determine how well it works in the real world. A web-based prototype that was lightweight was created as well to illustrate that it can be used in practice. Comprehensively, the paper gives a comparative review of the current CNN models and explains the capabilities and their constraints to automated recognition of medicinal plants.

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  - Md. Sohag
AU  - Md. Naimul Islam Nuhash
AU  - Md. Jobayer Ahmed
AU  - Md. Sadi Al Huda
AU  - Tahmid Enam Shrestha
AU  - Syamimi Mardiah Shaharum
PY  - 2026
DA  - 2026/06/08
TI  - Bridging AI and Ethnobotany: A Deep Learning Approach for Medicinal Plant Identification and Real-World Deployment
BT  - Proceedings of the International Conference on Intelligent Data Analysis and Applications (IDAA 2025)
PB  - Atlantis Press
SP  - 827
EP  - 841
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6239-664-7_57
DO  - 10.2991/978-94-6239-664-7_57
ID  - Sohag2026
ER  -