A Leaf Disease Detection Using Machine Learning and Deep Learning: Comparative Study

Mooad Al-shalout, Mohamed Elleuch, Ali Douik

2025

Abstract

This study aims to provide innovative methods and additional suggestions for detecting plant diseases using deep learning techniques. The study focused on identifying diseases affecting major daily consumed plants, such as tomatoes, corn, and potatoes. The detected diseases included rust, early and late spots, mildew, and bacterial spots. The study relied on machine learning and deep learning algorithms, such as Support Vector Machine and VGG19 algorithm, to detect plant diseases. SIFT and Gabor filters were also incorporated into the work and tested using SVM algorithm. The study reached highly accurate results, as the accuracy rate reached 98% using SVM, and 97% using VGG19 algorithm, which are satisfactory results compared to previous studies, confirming the effectiveness of the methods used in detecting plant diseases.

Paper Citation

in Harvard Style

Al-shalout M., Elleuch M. and Douik A. (2025). A Leaf Disease Detection Using Machine Learning and Deep Learning: Comparative Study. In Proceedings of the 17th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART; ISBN 978-989-758-737-5, SciTePress, pages 940-947. DOI: 10.5220/0013240400003890

in Bibtex Style

@conference{icaart25,
author={Mooad Al-shalout and Mohamed Elleuch and Ali Douik},
title={A Leaf Disease Detection Using Machine Learning and Deep Learning: Comparative Study},
booktitle={Proceedings of the 17th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART},
year={2025},
pages={940-947},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013240400003890},
isbn={978-989-758-737-5},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 17th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART
TI - A Leaf Disease Detection Using Machine Learning and Deep Learning: Comparative Study
SN - 978-989-758-737-5
AU - Al-shalout M.
AU - Elleuch M.
AU - Douik A.
PY - 2025
SP - 940
EP - 947
DO - 10.5220/0013240400003890
PB - SciTePress