Enhanced Tomato Leaf Disease Detection Using DenseNet201 with Channel and Spatial Attention Mechanisms

Ramana S, Prathiksha V S, Ragul M

2025

Abstract

This project presents a comprehensive system for tomato leaf disease detection, utilizing the DenseNet201 architecture enhanced with channel and spatial attention mechanisms. The system is designed to improve the accuracy and reliability of disease classification, addressing limitations in traditional Convolutional Neural Networks (CNNs), which previously achieved an accuracy of 95%. By incorporating attention mechanisms, the proposed approach focuses on critical image features, boosting classification accuracy to 98.07%. The model was trained on a dataset of 23,896 tomato leaf images across 10 distinct disease classes. The system architecture also includes data augmentation techniques and robust optimization methods, ensuring the model's generalization capability and performance. This project represents a significant step toward practical applications in agriculture, offering an advanced tool for early disease detection, which can aid in more effective crop management.

Paper Citation

in Harvard Style

S R., V S P. and M R. (2025). Enhanced Tomato Leaf Disease Detection Using DenseNet201 with Channel and Spatial Attention Mechanisms. In Proceedings of the 3rd International Conference on Futuristic Technology - Volume 3: INCOFT; ISBN 978-989-758-763-4, SciTePress, pages 749-756. DOI: 10.5220/0013652000004664

in Bibtex Style

@conference{incoft25,
author={Ramana S and Prathiksha V S and Ragul M},
title={Enhanced Tomato Leaf Disease Detection Using DenseNet201 with Channel and Spatial Attention Mechanisms},
booktitle={Proceedings of the 3rd International Conference on Futuristic Technology - Volume 3: INCOFT},
year={2025},
pages={749-756},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013652000004664},
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 - Enhanced Tomato Leaf Disease Detection Using DenseNet201 with Channel and Spatial Attention Mechanisms
SN - 978-989-758-763-4
AU - S R.
AU - V S P.
AU - M R.
PY - 2025
SP - 749
EP - 756
DO - 10.5220/0013652000004664
PB - SciTePress