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Advancing Rice Leaf Disease Detection using Vision Transformer on Real Datasets from Bangladesh

by Apurbo Deb Nath, Mohammad Shoaib Rahman, Md. Shahrear Ahmed Shuvon, Boby Rani Das, Nayebul Jannath Chowdhury, Md. Jalal Uddin Chowdhury, Sadia Afrin Rimi
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 187 - Number 76
Year of Publication: 2026
Authors: Apurbo Deb Nath, Mohammad Shoaib Rahman, Md. Shahrear Ahmed Shuvon, Boby Rani Das, Nayebul Jannath Chowdhury, Md. Jalal Uddin Chowdhury, Sadia Afrin Rimi
10.5120/ijca2026926320

Apurbo Deb Nath, Mohammad Shoaib Rahman, Md. Shahrear Ahmed Shuvon, Boby Rani Das, Nayebul Jannath Chowdhury, Md. Jalal Uddin Chowdhury, Sadia Afrin Rimi . Advancing Rice Leaf Disease Detection using Vision Transformer on Real Datasets from Bangladesh. International Journal of Computer Applications. 187, 76 ( Jan 2026), 1-6. DOI=10.5120/ijca2026926320

@article{ 10.5120/ijca2026926320,
author = { Apurbo Deb Nath, Mohammad Shoaib Rahman, Md. Shahrear Ahmed Shuvon, Boby Rani Das, Nayebul Jannath Chowdhury, Md. Jalal Uddin Chowdhury, Sadia Afrin Rimi },
title = { Advancing Rice Leaf Disease Detection using Vision Transformer on Real Datasets from Bangladesh },
journal = { International Journal of Computer Applications },
issue_date = { Jan 2026 },
volume = { 187 },
number = { 76 },
month = { Jan },
year = { 2026 },
issn = { 0975-8887 },
pages = { 1-6 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume187/number76/advancing-rice-leaf-disease-detection-using-vision-transformer-on-real-datasets-from-bangladesh/ },
doi = { 10.5120/ijca2026926320 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2026-01-20T22:56:38.094221+05:30
%A Apurbo Deb Nath
%A Mohammad Shoaib Rahman
%A Md. Shahrear Ahmed Shuvon
%A Boby Rani Das
%A Nayebul Jannath Chowdhury
%A Md. Jalal Uddin Chowdhury
%A Sadia Afrin Rimi
%T Advancing Rice Leaf Disease Detection using Vision Transformer on Real Datasets from Bangladesh
%J International Journal of Computer Applications
%@ 0975-8887
%V 187
%N 76
%P 1-6
%D 2026
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Rice leaf diseases pose a significant threat to our food security as they can reduce crop yields, cause plant deaths or even complete destruction in some cases, and result in shortfalls for both farmers and global agricultural production. Typically, farmers and other agricultural experts identify these diseases merely through visual examination, which leads to laboriousness, undesirable subjectivity, and faulty diagnosis. The main aim of this study is to provide farmers with accurate visual information so that they can protect their crops on time. Results: To accurately model a disease identification, we first propose our RiceLeafBD dataset. This dataset has been the subject of several studies, but our approach applies a model to it for the first time. We employed a proposed framework, achieving a superior accuracy of 92.75%. Notably, when assessing the performance on the tungro virus class, the model demonstrated exceptional precision, recall, and F1-score values of 100%, 98%, and 99%, respectively. The proposed framework does better than current convolutional neural network (CNN) and hybrid CNNtransfer learning models, according to the results of experiments. It has the highest accuracy and the least amount of model complexity that has been seen so far.

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Index Terms

Computer Science
Information Sciences

Keywords

Rice Leaf Disease RiceLeafBD Dataset Proposed Framework Vision Transformer

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