Proceedings of the International Conference on Intelligent Systems and Digital Transformation (ICISD 2025)

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Tomato Leaves Disease Classification Using Vision Transformers and EfficientNet

Authors
P. Durgadevi1, Hitesh Reddy Murikinati2, Manaswini Zagabathuni3, *, Sai Mohit Sikhakolli4
1Assistant Professor, Department of Computer Science and Engineering, SRM Institute of Science and Technology, Vadapalani, Chennai, 600026, Tamil Nadu, India
2Student, Department of Computer Science and Engineering, SRM Institute of Science and Technology, Vadapalani, Chennai, 600026, Tamil Nadu, India
3Student, Department of Computer Science and Engineering, SRM Institute of Science and Technology, Vadapalani, Chennai, 600026, Tamil Nadu, India
4Student, Department of Computer Science and Engineering, SRM Institute of Science and Technology, Vadapalani, Chennai, 600026, Tamil Nadu, India
*Corresponding author. Email: zm3088@srmist.edu.in
Corresponding Author
Manaswini Zagabathuni
Available Online 31 October 2025.
DOI
10.2991/978-94-6463-866-0_26How to use a DOI?
Keywords
Deep Learning; Tomato Leaf Disease; Vision Transformers; EfficientNet; Ensemble Model
Abstract

Timely identification of tomato leaf diseases is vital for ensuring agricultural productivity and sustainability. This study presents an innovative ensemble deep learning framework that integrates EfficientNet and Vision Transformer (ViT) to achieve precise and scalable classification of tomato leaf diseases. By combining EfficientNet’s efficient local feature derivation with ViT’s global con- textual analysis through Multi-Head Attention, the model attains an exceptional accuracy of 99.85% on a dataset comprising 18,835 images across 10 disease categories. Compared to standalone convolutional neural networks such as Res-Net-50 (96.8%) and DenseNet-121 (98.1%), the ensemble model demonstrates superior precision and adaptability under varying conditions. Its optimized architecture facilitates real-time deployment on edge devices, enhancing its utility in precision agriculture. Future efforts will focus on extending the model to other crops and improving interpretability to support broader agricultural applications.

Copyright
© 2025 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 and Digital Transformation (ICISD 2025)
Series
Atlantis Highlights in Intelligent Systems
Publication Date
31 October 2025
ISBN
978-94-6463-866-0
ISSN
2589-4919
DOI
10.2991/978-94-6463-866-0_26How to use a DOI?
Copyright
© 2025 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  - P. Durgadevi
AU  - Hitesh Reddy Murikinati
AU  - Manaswini Zagabathuni
AU  - Sai Mohit Sikhakolli
PY  - 2025
DA  - 2025/10/31
TI  - Tomato Leaves Disease Classification Using Vision Transformers and EfficientNet
BT  - Proceedings of the International Conference on Intelligent Systems and Digital Transformation (ICISD 2025)
PB  - Atlantis Press
SP  - 308
EP  - 320
SN  - 2589-4919
UR  - https://doi.org/10.2991/978-94-6463-866-0_26
DO  - 10.2991/978-94-6463-866-0_26
ID  - Durgadevi2025
ER  -