Using Machine Learning to Identify Crop Diseases with ResNet-18

Rihan Rahman

2025

Abstract

Plant diseases are a highly prevalent issue in agriculture, causing countless farmers annually to face career threatening damages such as diminished profits and crop yields and environmental damages. Consequently, it is imperative that these diseases are quickly detected and treated against. An increasingly effective solution is to train convolutional neural networks (CNNs) using deep learning (DL). DL has several effective applications in a variety of major fields such as healthcare and fraud detection and has a high potential to solve issues of global significance. This research’s goal is to create a machine learning (ML) model with DL to identify plants’ diseases using photos of infected leaves. Many farmers in rural areas struggle to treat blights due to limited access to technology and information regarding them. Therefore, an ML model which can automatically identify these diseases would be highly useful for these people. After sourcing a comprehensive dataset with images of 88 types of plants and diseases, I used it to train a CNN model using several data augmentation techniques. With the model architecture ResNet-18, while evaluating its performance with a validation dataset, the model achieved a loss of 4.541%. This value demonstrates ResNet-18’s applicability to the task of identifying plant diseases and illustrates the potential for classification-based DL networks to support rural farmers and the field of agriculture. If a superior model is created to identify blights more accurately, it should be used to help the billions of farmers who would greatly benefit from such technology.

Paper Citation

in Harvard Style

Rahman R. (2025). Using Machine Learning to Identify Crop Diseases with ResNet-18. In Proceedings of the 18th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 1: BIOIMAGING; ISBN 978-989-758-731-3, SciTePress, pages 311-315. DOI: 10.5220/0013123500003911

in Bibtex Style

@conference{bioimaging25,
author={Rihan Rahman},
title={Using Machine Learning to Identify Crop Diseases with ResNet-18},
booktitle={Proceedings of the 18th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 1: BIOIMAGING},
year={2025},
pages={311-315},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013123500003911},
isbn={978-989-758-731-3},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 18th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 1: BIOIMAGING
TI - Using Machine Learning to Identify Crop Diseases with ResNet-18
SN - 978-989-758-731-3
AU - Rahman R.
PY - 2025
SP - 311
EP - 315
DO - 10.5220/0013123500003911
PB - SciTePress