Cargando…

Coffee Disease Visualization and Classification

Deep learning architectures are widely used in state-of-the-art image classification tasks. Deep learning has enhanced the ability to automatically detect and classify plant diseases. However, in practice, disease classification problems are treated as black-box methods. Thus, it is difficult to tru...

Descripción completa

Detalles Bibliográficos
Autores principales: Yebasse, Milkisa, Shimelis, Birhanu, Warku, Henok, Ko, Jaepil, Cheoi, Kyung Joo
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8235481/
https://www.ncbi.nlm.nih.gov/pubmed/34205610
http://dx.doi.org/10.3390/plants10061257
_version_ 1783714328680071168
author Yebasse, Milkisa
Shimelis, Birhanu
Warku, Henok
Ko, Jaepil
Cheoi, Kyung Joo
author_facet Yebasse, Milkisa
Shimelis, Birhanu
Warku, Henok
Ko, Jaepil
Cheoi, Kyung Joo
author_sort Yebasse, Milkisa
collection PubMed
description Deep learning architectures are widely used in state-of-the-art image classification tasks. Deep learning has enhanced the ability to automatically detect and classify plant diseases. However, in practice, disease classification problems are treated as black-box methods. Thus, it is difficult to trust the model that it truly identifies the region of the disease in the image; it may simply use unrelated surroundings for classification. Visualization techniques can help determine important areas for the model by highlighting the region responsible for the classification. In this study, we present a methodology for visualizing coffee diseases using different visualization approaches. Our goal is to visualize aspects of a coffee disease to obtain insight into what the model “sees” as it learns to classify healthy and non-healthy images. In addition, visualization helped us identify misclassifications and led us to propose a guided approach for coffee disease classification. The guided approach achieved a classification accuracy of 98% compared to the 77% of naïve approach on the Robusta coffee leaf image dataset. The visualization methods considered in this study were Grad-CAM, Grad-CAM++, and Score-CAM. We also provided a visual comparison of the visualization methods.
format Online
Article
Text
id pubmed-8235481
institution National Center for Biotechnology Information
language English
publishDate 2021
publisher MDPI
record_format MEDLINE/PubMed
spelling pubmed-82354812021-06-27 Coffee Disease Visualization and Classification Yebasse, Milkisa Shimelis, Birhanu Warku, Henok Ko, Jaepil Cheoi, Kyung Joo Plants (Basel) Article Deep learning architectures are widely used in state-of-the-art image classification tasks. Deep learning has enhanced the ability to automatically detect and classify plant diseases. However, in practice, disease classification problems are treated as black-box methods. Thus, it is difficult to trust the model that it truly identifies the region of the disease in the image; it may simply use unrelated surroundings for classification. Visualization techniques can help determine important areas for the model by highlighting the region responsible for the classification. In this study, we present a methodology for visualizing coffee diseases using different visualization approaches. Our goal is to visualize aspects of a coffee disease to obtain insight into what the model “sees” as it learns to classify healthy and non-healthy images. In addition, visualization helped us identify misclassifications and led us to propose a guided approach for coffee disease classification. The guided approach achieved a classification accuracy of 98% compared to the 77% of naïve approach on the Robusta coffee leaf image dataset. The visualization methods considered in this study were Grad-CAM, Grad-CAM++, and Score-CAM. We also provided a visual comparison of the visualization methods. MDPI 2021-06-21 /pmc/articles/PMC8235481/ /pubmed/34205610 http://dx.doi.org/10.3390/plants10061257 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Yebasse, Milkisa
Shimelis, Birhanu
Warku, Henok
Ko, Jaepil
Cheoi, Kyung Joo
Coffee Disease Visualization and Classification
title Coffee Disease Visualization and Classification
title_full Coffee Disease Visualization and Classification
title_fullStr Coffee Disease Visualization and Classification
title_full_unstemmed Coffee Disease Visualization and Classification
title_short Coffee Disease Visualization and Classification
title_sort coffee disease visualization and classification
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8235481/
https://www.ncbi.nlm.nih.gov/pubmed/34205610
http://dx.doi.org/10.3390/plants10061257
work_keys_str_mv AT yebassemilkisa coffeediseasevisualizationandclassification
AT shimelisbirhanu coffeediseasevisualizationandclassification
AT warkuhenok coffeediseasevisualizationandclassification
AT kojaepil coffeediseasevisualizationandclassification
AT cheoikyungjoo coffeediseasevisualizationandclassification