Medicinal Plant Recognition Using Shallow Convolutional Neural Network

Ramar Ahila Priyadharshini; M. Arun

doi:10.47852/bonviewAIA52022956

Authors

Ramar Ahila Priyadharshini Department of ECE, Mepco Schlenk Engineering College, India https://orcid.org/0000-0001-9265-4842
M. Arun Department of ECE, Mepco Schlenk Engineering College, India

DOI:

https://doi.org/10.47852/bonviewAIA52022956

Keywords:

deep learning, CNN, medicinal plant

Abstract

Ayurvedic medicine plays an essential role in the overall care that is provided for the physical and mental wellbeing of people. It is vital to correctly identify and categorize medicinal herbs to be able to provide better therapy. Medicinal herbs come in a wide variety of forms. It is a challenging task that requires a significant amount of professional medical experience to correctly name and classify the many distinct kinds of medicinal plants. Because of this, having an approach to the identification of medicinal plants that is completely automated is something that is highly desirable. In this study, a straightforward four-layer shallow Convolutional Neural Network (S-CNN) is proposed for the aim of classifying medicinal herbs. The potential utility of S-CNN is evaluated with the help of four distinct leaf datasets such as the Swedish Leaf, Flavia Leaf, MepcoTropicalLeaf Dataset, and Medicinal Leaf Dataset. Our model is capable of achieving a level of classification accuracy of 98.22%, 96.18% and 92.89% on Swedish, Flavia and Medicinal Leaf datasets respectively and that is comparable to that of other state-of-art methodologies in this field.

Received: 28 March 2024 | Revised: 13 March 2025 | Accepted: 6 June 2025

Conflicts of Interest

The authors declare that they have no conflicts of interest to this work.

Data Availability Statement

The data that support the findings of this study are openly available in Swedish Leaf Dataset at https://www.cvl.isy.liu.se/en/research/datasets/swedish-leaf/, reference number [47]; in Medicinal Leaf Dataset at https://data.mendeley.com/datasets/nnytj2v3n5/1, reference number [48].

Author Contribution Statement

Ramar Ahila Priyadharshini: Conceptualization, Methodology, Software, Validation, Investigation, Data curation, Writing - original draft, Visualization, Supervision, Project administration. M. Arun: Software, Formal analysis, Data curation, Writing - review & editing, Visualization.