Document Type
Conference Proceeding
Publication Date
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.
Recommended Citation
Zhou, Kevin, and Dimah Dera. “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 (VISIGRAPP 2024) - Volume 3: VISAPP, 166–74. Rome, Italy: SciTePress, 2024. https://doi.org/10.5220/0012390400003660
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
First Page
166
Last Page
174
Publication Title
Proceedings of the 19th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2024) - Volume 3: VISAPP
DOI
10.5220/0012390400003660
Comments
Student publication. © 2024 by SCITEPRESS – Science and Technology Publications, Lda.