Convolutional Neural Networks-Based Potato Leaf Disease Classification

Ravinder Kaur, Sonam Khattar

2025

Abstract

The potato has risen to the position of fourth most eaten staple food in the world, among many others. Additionally, the world's population is the primary driver of the dramatic increase in potato consumption. But the main reason why the crop isn't as good as it might be because of potato illnesses. Things will become much worse for the plants if the illness is misclassified and is discovered too late. Fortunately, leaf conditions may be used to identify a number of illnesses in potato plants. Thus, this study introduces a method that uses deep learning convolutional neural network architectural model to accurately diagnose the four kinds of potato plant illnesses based on leaf conditions. The experiment has shown that the deep neural network based VGG19 technique is able to produce significant results, with an average accuracy of 99.07%.

Paper Citation

in Harvard Style

Kaur R. and Khattar S. (2025). Convolutional Neural Networks-Based Potato Leaf Disease Classification. In Proceedings of the 3rd International Conference on Futuristic Technology - Volume 2: INCOFT; ISBN 978-989-758-763-4, SciTePress, pages 493-497. DOI: 10.5220/0013595500004664

in Bibtex Style

@conference{incoft25,
author={Ravinder Kaur and Sonam Khattar},
title={Convolutional Neural Networks-Based Potato Leaf Disease Classification},
booktitle={Proceedings of the 3rd International Conference on Futuristic Technology - Volume 2: INCOFT},
year={2025},
pages={493-497},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013595500004664},
isbn={978-989-758-763-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 3rd International Conference on Futuristic Technology - Volume 2: INCOFT
TI - Convolutional Neural Networks-Based Potato Leaf Disease Classification
SN - 978-989-758-763-4
AU - Kaur R.
AU - Khattar S.
PY - 2025
SP - 493
EP - 497
DO - 10.5220/0013595500004664
PB - SciTePress