Proceedings of the International Conference on Intelligent Systems for a Sustainable Future (ISSF 2026)

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Efficiency Evaluation of CNN and Vision Transformer Architectures in Leaf Image Analysis

Authors
S. Prakasam1, *, T. S. Mohan2, P. Shanmugapriya3
1Associate Professor, Department of Computer Science and Applications, Sri Chandrasekharendra Saraswathi Viswa Mahavidyalaya, Kanchipuram, India
2Research Scholar, Department of Computer Science and Applications, Sri Chandrasekharendra Saraswathi Viswa Mahavidyalaya, Kanchipuram, 631561, India
3Associate Professor, Department of Computer Science and Engineering, Sri Chandrasekharendra Saraswathi Viswa Mahavidyalaya, Kanchipuram, Tamil Nadu, 631561, India
*Corresponding author. Email: sp@kanchiuniv.ac.in
Corresponding Author
S. Prakasam
Available Online 16 June 2026.
DOI
10.2991/978-94-6239-693-7_53How to use a DOI?
Keywords
Convolutional Neural Networks (CNN); Vision Transformers (ViTs); Deep Learning; F1-score; Apple Leaf Disease Detection; etc.
Abstract

In agriculture sector maintaining and improving the leaf health is having important role. This task is obtained by using deep learning techniques and it provides more no of models for analyzing the leaf’s patterns and also it extracts the features from image based leaf data sets. There are two models such as Convolutional Neural Network and Vision Transformation (ViTs) are frequently used in analyzing the leaf disease patterns and leaf classifications. This paper provides an efficient evaluation of both models with same datasets of different classes. These two model’s performance was calculated by using performance metrics such as accuracy, recision, recall, F1-score and complexity. Through this evaluation the unknown fact about the models has been framed and the analyses for knowing better performance as well as to find disease patterns form various classes of images. Finally based on the model’s performance decision were made.

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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Volume Title
Proceedings of the International Conference on Intelligent Systems for a Sustainable Future (ISSF 2026)
Series
Atlantis Highlights in Intelligent Systems
Publication Date
16 June 2026
ISBN
978-94-6239-693-7
ISSN
2589-4919
DOI
10.2991/978-94-6239-693-7_53How to use a DOI?
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.

Cite this article

risenwbib
TY  - CONF
AU  - S. Prakasam
AU  - T. S. Mohan
AU  - P. Shanmugapriya
PY  - 2026
DA  - 2026/06/16
TI  - Efficiency Evaluation of CNN and Vision Transformer Architectures in Leaf Image Analysis
BT  - Proceedings of the International Conference on Intelligent Systems for a Sustainable Future (ISSF 2026)
PB  - Atlantis Press
SP  - 539
EP  - 546
SN  - 2589-4919
UR  - https://doi.org/10.2991/978-94-6239-693-7_53
DO  - 10.2991/978-94-6239-693-7_53
ID  - Prakasam2026
ER  -