MICROSCOPIC IDENTIFICATION OF BRAZILIAN COMMERCIAL WOOD SPECIES VIA MACHINE-LEARNING
Autor(a) principal: | |
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Data de Publicação: | 2022 |
Outros Autores: | , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Cerne (Online) |
Texto Completo: | https://cerne.ufla.br/site/index.php/CERNE/article/view/2978 |
Resumo: | Background: Multiple challenges are faced by industry and certification agencies when commercializing tropical species. Anatomical similarities of tropical hardwoods impair identification. Deep learning models can facilitate microscopic identification of wood by using sophisticated techniques such as deep convolutional networks (DCNN). Our objective was to microscopically identify 23 wood species using a custom DCNN model. Results: Photographs from microscopic slides of each wood species were processed, and the final data set contained 2,448 images. We applied stratified k-fold cross-validation technique during training to increase model’s robustness and trustworthiness. Thus, the dataset was divided into approximately 80% training (1,958 images) and 20% validation (490 images) for each fold. A series of augmentations were performed only on training data to include variations in rotation, zoom, and perspective. Image augmentation was performed on-the-fly. The network consisted of convolutions, max pooling, global average pooling, and fully connected layers. We tested the performance of the DCNN against accuracy, precision, recall, and F1-score on the validation set for each fold. Conclusion: The machine learned custom model accuracy was considered excellent (>0.90). The model’s worst performance was identified in distinguishing between Toona ciliata and Khaya ivorensis, which was due more to wood variability than to a machine learning deficiency. Future studies should focus on integration, verification/monitoring, and updating of current models for end user manipulation, trust, ethics, and security. |
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oai:cerne.ufla.br:article/2978 |
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Cerne (Online) |
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MICROSCOPIC IDENTIFICATION OF BRAZILIAN COMMERCIAL WOOD SPECIES VIA MACHINE-LEARNINGconvolutional neural networks (CNN)deep learningtropical specieswood anatomywood identificationBackground: Multiple challenges are faced by industry and certification agencies when commercializing tropical species. Anatomical similarities of tropical hardwoods impair identification. Deep learning models can facilitate microscopic identification of wood by using sophisticated techniques such as deep convolutional networks (DCNN). Our objective was to microscopically identify 23 wood species using a custom DCNN model. Results: Photographs from microscopic slides of each wood species were processed, and the final data set contained 2,448 images. We applied stratified k-fold cross-validation technique during training to increase model’s robustness and trustworthiness. Thus, the dataset was divided into approximately 80% training (1,958 images) and 20% validation (490 images) for each fold. A series of augmentations were performed only on training data to include variations in rotation, zoom, and perspective. Image augmentation was performed on-the-fly. The network consisted of convolutions, max pooling, global average pooling, and fully connected layers. We tested the performance of the DCNN against accuracy, precision, recall, and F1-score on the validation set for each fold. Conclusion: The machine learned custom model accuracy was considered excellent (>0.90). The model’s worst performance was identified in distinguishing between Toona ciliata and Khaya ivorensis, which was due more to wood variability than to a machine learning deficiency. Future studies should focus on integration, verification/monitoring, and updating of current models for end user manipulation, trust, ethics, and security.CERNECERNE2022-08-05info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfhttps://cerne.ufla.br/site/index.php/CERNE/article/view/2978CERNE; Vol 28 No 1 (2022); e-102978CERNE; Vol 28 No 1 (2022); e-1029782317-63420104-7760reponame:Cerne (Online)instname:Universidade Federal de Lavras (UFLA)instacron:UFLAenghttps://cerne.ufla.br/site/index.php/CERNE/article/view/2978/1295http://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccessMoulin, Jordão CabralLopes, Dercilio Junior VerlyMulin, Lucas BragaBobadilha, Gabrielly dos SantosOliveira, Ramon Ferreir2022-08-25T17:35:35Zoai:cerne.ufla.br:article/2978Revistahttps://cerne.ufla.br/site/index.php/CERNEPUBhttps://cerne.ufla.br/site/index.php/CERNE/oaicerne@dcf.ufla.br||cerne@dcf.ufla.br2317-63420104-7760opendoar:2024-05-21T19:54:48.225515Cerne (Online) - Universidade Federal de Lavras (UFLA)true |
dc.title.none.fl_str_mv |
MICROSCOPIC IDENTIFICATION OF BRAZILIAN COMMERCIAL WOOD SPECIES VIA MACHINE-LEARNING |
title |
MICROSCOPIC IDENTIFICATION OF BRAZILIAN COMMERCIAL WOOD SPECIES VIA MACHINE-LEARNING |
spellingShingle |
MICROSCOPIC IDENTIFICATION OF BRAZILIAN COMMERCIAL WOOD SPECIES VIA MACHINE-LEARNING Moulin, Jordão Cabral convolutional neural networks (CNN) deep learning tropical species wood anatomy wood identification |
title_short |
MICROSCOPIC IDENTIFICATION OF BRAZILIAN COMMERCIAL WOOD SPECIES VIA MACHINE-LEARNING |
title_full |
MICROSCOPIC IDENTIFICATION OF BRAZILIAN COMMERCIAL WOOD SPECIES VIA MACHINE-LEARNING |
title_fullStr |
MICROSCOPIC IDENTIFICATION OF BRAZILIAN COMMERCIAL WOOD SPECIES VIA MACHINE-LEARNING |
title_full_unstemmed |
MICROSCOPIC IDENTIFICATION OF BRAZILIAN COMMERCIAL WOOD SPECIES VIA MACHINE-LEARNING |
title_sort |
MICROSCOPIC IDENTIFICATION OF BRAZILIAN COMMERCIAL WOOD SPECIES VIA MACHINE-LEARNING |
author |
Moulin, Jordão Cabral |
author_facet |
Moulin, Jordão Cabral Lopes, Dercilio Junior Verly Mulin, Lucas Braga Bobadilha, Gabrielly dos Santos Oliveira, Ramon Ferreir |
author_role |
author |
author2 |
Lopes, Dercilio Junior Verly Mulin, Lucas Braga Bobadilha, Gabrielly dos Santos Oliveira, Ramon Ferreir |
author2_role |
author author author author |
dc.contributor.author.fl_str_mv |
Moulin, Jordão Cabral Lopes, Dercilio Junior Verly Mulin, Lucas Braga Bobadilha, Gabrielly dos Santos Oliveira, Ramon Ferreir |
dc.subject.por.fl_str_mv |
convolutional neural networks (CNN) deep learning tropical species wood anatomy wood identification |
topic |
convolutional neural networks (CNN) deep learning tropical species wood anatomy wood identification |
description |
Background: Multiple challenges are faced by industry and certification agencies when commercializing tropical species. Anatomical similarities of tropical hardwoods impair identification. Deep learning models can facilitate microscopic identification of wood by using sophisticated techniques such as deep convolutional networks (DCNN). Our objective was to microscopically identify 23 wood species using a custom DCNN model. Results: Photographs from microscopic slides of each wood species were processed, and the final data set contained 2,448 images. We applied stratified k-fold cross-validation technique during training to increase model’s robustness and trustworthiness. Thus, the dataset was divided into approximately 80% training (1,958 images) and 20% validation (490 images) for each fold. A series of augmentations were performed only on training data to include variations in rotation, zoom, and perspective. Image augmentation was performed on-the-fly. The network consisted of convolutions, max pooling, global average pooling, and fully connected layers. We tested the performance of the DCNN against accuracy, precision, recall, and F1-score on the validation set for each fold. Conclusion: The machine learned custom model accuracy was considered excellent (>0.90). The model’s worst performance was identified in distinguishing between Toona ciliata and Khaya ivorensis, which was due more to wood variability than to a machine learning deficiency. Future studies should focus on integration, verification/monitoring, and updating of current models for end user manipulation, trust, ethics, and security. |
publishDate |
2022 |
dc.date.none.fl_str_mv |
2022-08-05 |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
https://cerne.ufla.br/site/index.php/CERNE/article/view/2978 |
url |
https://cerne.ufla.br/site/index.php/CERNE/article/view/2978 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
https://cerne.ufla.br/site/index.php/CERNE/article/view/2978/1295 |
dc.rights.driver.fl_str_mv |
http://creativecommons.org/licenses/by/4.0/ info:eu-repo/semantics/openAccess |
rights_invalid_str_mv |
http://creativecommons.org/licenses/by/4.0/ |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.publisher.none.fl_str_mv |
CERNE CERNE |
publisher.none.fl_str_mv |
CERNE CERNE |
dc.source.none.fl_str_mv |
CERNE; Vol 28 No 1 (2022); e-102978 CERNE; Vol 28 No 1 (2022); e-102978 2317-6342 0104-7760 reponame:Cerne (Online) instname:Universidade Federal de Lavras (UFLA) instacron:UFLA |
instname_str |
Universidade Federal de Lavras (UFLA) |
instacron_str |
UFLA |
institution |
UFLA |
reponame_str |
Cerne (Online) |
collection |
Cerne (Online) |
repository.name.fl_str_mv |
Cerne (Online) - Universidade Federal de Lavras (UFLA) |
repository.mail.fl_str_mv |
cerne@dcf.ufla.br||cerne@dcf.ufla.br |
_version_ |
1799874944275841024 |