An Effective Convolutional Learning Model with Fine-Tuning for Medicinal Plant Leaf Identification

Chaitrashree R, Shashikala S V, Sharathkumar Y H

2025

Abstract

In this work has successfully presented the custom CNN model and AYURNet approach for Classification. We used a convolution neural network (CNN) to solve this problem of Multi-Class Classification of Plant Leaves of some Andhra Ayurvedic Plants. The convolution layer part of CNN was used for feature extraction and the fully connected dense layer part of CNN was used for Multiclass Classification. Using the known best practices, a very simple and elegant CNN model was designed and built using Keras to solve the Plant leaf Multi-Class Classification problem. The model was trained on the training dataset for 200 epochs. Using the weight parameters obtained in each epoch, the model was tested against the validation dataset. Training accuracy and Validation accuracy were compared at each epoch and the model with the best weight parameters was chosen. The logic used to choose the AYUR-Best model was high accuracy above the chosen threshold of 99% and the least possible difference between training accuracy and validation accuracy. This was to ensure that the accuracy of the model was very high while ensuring that there was no overfitting. The model chosen using this method performed very well on the test dataset too and it resulted in an accuracy of 99.88%. Similar high accuracies were achieved by leveraging popular pre-trained models like DenseNet169, EfficientNetB6, InceptionResNetV2, ResNet152V2, VGG16 and Exception, but it is seen that the respective models are heavy with a large number of parameters when compared to the custom CNN model described in this work. Due to the small size of the Custom CNN model, it is suitable for the development of the mobile application for Ayurvedic plant species identification based on the respective leaf images..

Paper Citation

in Harvard Style

R C., S V S. and Y H S. (2025). An Effective Convolutional Learning Model with Fine-Tuning for Medicinal Plant Leaf Identification. In Proceedings of the 3rd International Conference on Futuristic Technology - Volume 2: INCOFT; ISBN 978-989-758-763-4, SciTePress, pages 212-222. DOI: 10.5220/0013589700004664

in Bibtex Style

@conference{incoft25,
author={Chaitrashree R and Shashikala S V and Sharathkumar Y H},
title={An Effective Convolutional Learning Model with Fine-Tuning for Medicinal Plant Leaf Identification},
booktitle={Proceedings of the 3rd International Conference on Futuristic Technology - Volume 2: INCOFT},
year={2025},
pages={212-222},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013589700004664},
isbn={978-989-758-763-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 3rd International Conference on Futuristic Technology - Volume 2: INCOFT
TI - An Effective Convolutional Learning Model with Fine-Tuning for Medicinal Plant Leaf Identification
SN - 978-989-758-763-4
AU - R C.
AU - S V S.
AU - Y H S.
PY - 2025
SP - 212
EP - 222
DO - 10.5220/0013589700004664
PB - SciTePress