A Classification and Detection of Cotton Leaf Disease Using Lightweight CNN Architecture

Swetha G, Veena V, Kruthi T C, Shwetha A B, Selvarani S, Jhansi K

2025

Abstract

The rapid and accurate detection of diseases in cotton leaves is crucial for ensuring high agricultural productivity and minimizing economic losses. Traditional methods of disease detection, relying heavily on manual inspection, are time-consuming and prone to errors. To address these challenges, we propose a lightweight Convolutional Neural Network (CNN) architecture designed specifically for the classification and detection of cotton leaf diseases. Our model focuses on optimizing computational efficiency and accuracy, making it suitable for deployment on edge devices with limited processing power. The proposed CNN architecture employs a series of convolutional layers with reduced parameters, leveraging techniques such as depth-wise separable convolutions and global average pooling to maintain high performance while minimizing computational costs. We trained and validated our model on a comprehensive dataset comprising various cotton leaf diseases, including bacterial blight, leaf spot, and mildew. Experimental results demonstrate that our lightweight CNN achieves a high classification accuracy, outperforming several existing models in terms of both speed and precision. Furthermore, our model's ability to generalize across different disease types highlights its potential for real-world applications. By enabling early and accurate detection of cotton leaf diseases, our approach promises to significantly enhance crop management practices, reduce the reliance on chemical treatments, and contribute to sustainable agricultural practices.

Paper Citation

in Harvard Style

G S., V V., T C K., A B S., S S. and K J. (2025). A Classification and Detection of Cotton Leaf Disease Using Lightweight CNN Architecture. In Proceedings of the 3rd International Conference on Futuristic Technology - Volume 3: INCOFT; ISBN 978-989-758-763-4, SciTePress, pages 120-125. DOI: 10.5220/0013609700004664

in Bibtex Style

@conference{incoft25,
author={Swetha G and Veena V and Kruthi T C and Shwetha A B and Selvarani S and Jhansi K},
title={A Classification and Detection of Cotton Leaf Disease Using Lightweight CNN Architecture},
booktitle={Proceedings of the 3rd International Conference on Futuristic Technology - Volume 3: INCOFT},
year={2025},
pages={120-125},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013609700004664},
isbn={978-989-758-763-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 3rd International Conference on Futuristic Technology - Volume 3: INCOFT
TI - A Classification and Detection of Cotton Leaf Disease Using Lightweight CNN Architecture
SN - 978-989-758-763-4
AU - G S.
AU - V V.
AU - T C K.
AU - A B S.
AU - S S.
AU - K J.
PY - 2025
SP - 120
EP - 125
DO - 10.5220/0013609700004664
PB - SciTePress