Robust Denoising and DenseNet Classification Framework for Plant Disease Detection

Kevin Zhou, Dimah Dera

2024

Abstract

Plant disease is one of many obstacles encountered in the field of agriculture. Machine learning models have been used to classify and detect diseases among plants by analyzing and extracting features from plant images. However, a common problem for many models is that they are trained on clean laboratory images and do not exemplify real conditions where noise can be present. In addition, the emergence of adversarial noise that can mislead models into wrong predictions poses a severe challenge to developing preserved models against noisy environments. In this paper, we propose an end-to-end robust plant disease detection framework that combines a DenseNet-based classification with a vigorous deep learning denoising model. We validate a variety of deep learning denoising models and adopt the Real Image Denoising network (RIDnet). The experiments have shown that the proposed denoising classification framework for plant disease detection is more robust against noisy or corrupted input images compared to a single classification model and can also successfully defend against adversarial noises in images.

Paper Citation

in Harvard Style

Zhou K. and Dera D. (2024). Robust Denoising and DenseNet Classification Framework for Plant Disease Detection. In Proceedings of the 19th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3: VISAPP; ISBN 978-989-758-679-8, SciTePress, pages 166-174. DOI: 10.5220/0012390400003660

in Bibtex Style

@conference{visapp24,
author={Kevin Zhou and Dimah Dera},
title={Robust Denoising and DenseNet Classification Framework for Plant Disease Detection},
booktitle={Proceedings of the 19th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3: VISAPP},
year={2024},
pages={166-174},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012390400003660},
isbn={978-989-758-679-8},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 19th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3: VISAPP
TI - Robust Denoising and DenseNet Classification Framework for Plant Disease Detection
SN - 978-989-758-679-8
AU - Zhou K.
AU - Dera D.
PY - 2024
SP - 166
EP - 174
DO - 10.5220/0012390400003660
PB - SciTePress