loading
Papers Papers/2022 Papers Papers/2022

Research.Publish.Connect.

Paper

Authors: Kevin Zhou 1 and Dimah Dera 2

Affiliations: 1 Electrical and Computer Engineering, The University of Texas Rio Grande Valley, Brownsville, TX 78520 U.S.A. ; 2 Chester F. Carlson Center for Imaging Science, Rochester Institute of Technology, Rochester, NY 14623 U.S.A.

Keyword(s): Plant Disease Detection, DenseNet Image Classification, Robust Machine Learning, Denoising Neural Networks.

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 i mages compared to a single classification model and can also successfully defend against adversarial noises in images. (More)

CC BY-NC-ND 4.0

Sign In Guest: Register as new SciTePress user now for free.

Sign In SciTePress user: please login.

PDF ImageMy Papers

You are not signed in, therefore limits apply to your IP address 3.14.130.241

In the current month:
Recent papers: 100 available of 100 total
2+ years older papers: 200 available of 200 total

Paper citation in several formats:
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; ISSN 2184-4321, SciTePress, pages 166-174. DOI: 10.5220/0012390400003660

@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},
issn={2184-4321},
}

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
IS - 2184-4321
AU - Zhou, K.
AU - Dera, D.
PY - 2024
SP - 166
EP - 174
DO - 10.5220/0012390400003660
PB - SciTePress

- Science and Technology Publications, Lda.
RESOURCES

Proceedings

Papers

Authors

Ontology

CONTACTS

Science and Technology Publications, Lda
Avenida de S. Francisco Xavier, Lote 7 Cv. C,
2900-616 Setúbal, Portugal.

Phone: +351 265 520 185 (National fixed network call)
Fax: +351 265 520 186
Email: info@scitepress.org

EXTERNAL LINKS

PRIMORIS

INSTICC

SCITEVENTS

CROSSREF

PROCEEDINGS SUBMITTED FOR INDEXATION BY:

dblp

Ei Compendex

SCOPUS

Semantic Scholar

Google Scholar

Microsoft Academic