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Wood identification of Cyclobalanopsis (Endl.) Oerst based on microscopic features and CTGAN-enhanced explainable machine learning models

INTRODUCTION: Accurate and fast identification of wood at the species level is critical for protecting and conserving tree species resources. The current identification methods are inefficient, costly, and complex METHODS: A wood species identification model based on wood anatomy and using the Cyclo...

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Autores principales: Zhan, Weihui, Chen, Bowen, Wu, Xiaolian, Yang, Zhen, Lin, Che, Lin, Jinguo, Guan, Xin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10361066/
https://www.ncbi.nlm.nih.gov/pubmed/37484454
http://dx.doi.org/10.3389/fpls.2023.1203836
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author Zhan, Weihui
Chen, Bowen
Wu, Xiaolian
Yang, Zhen
Lin, Che
Lin, Jinguo
Guan, Xin
author_facet Zhan, Weihui
Chen, Bowen
Wu, Xiaolian
Yang, Zhen
Lin, Che
Lin, Jinguo
Guan, Xin
author_sort Zhan, Weihui
collection PubMed
description INTRODUCTION: Accurate and fast identification of wood at the species level is critical for protecting and conserving tree species resources. The current identification methods are inefficient, costly, and complex METHODS: A wood species identification model based on wood anatomy and using the Cyclobalanopsis genus wood cell geometric dataset was proposed. The model was enhanced by the CTGAN deep learning algorithm and used a simulated cell geometric feature dataset. The machine learning models BPNN and SVM were trained respectively for recognition of three Cyclobalanopsis species with simulated vessel cells and simulated wood fiber cells. RESULTS: The SVM model and BPNN model achieved recognition accuracy of 96.4% and 99.6%, respectively, on the real dataset, using the CTGAN-generated vessel dataset. The BPNN model and SVM model achieved recognition accuracy of 75.5% and 77.9% on real dataset, respectively, using the CTGAN-generated wood fiber dataset. DISCUSSION: The machine learning model trained based on the enhanced cell geometric feature data by CTGAN achieved good recognition of Cyclobalanopsis, with the SVM model having a higher prediction accuracy than BPNN. The machine learning models were interpreted based on LIME to explore how they identify tree species based on wood cell geometric features. This proposed model can be used for efficient and cost-effective identification of wood species in industrial applications.
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spelling pubmed-103610662023-07-22 Wood identification of Cyclobalanopsis (Endl.) Oerst based on microscopic features and CTGAN-enhanced explainable machine learning models Zhan, Weihui Chen, Bowen Wu, Xiaolian Yang, Zhen Lin, Che Lin, Jinguo Guan, Xin Front Plant Sci Plant Science INTRODUCTION: Accurate and fast identification of wood at the species level is critical for protecting and conserving tree species resources. The current identification methods are inefficient, costly, and complex METHODS: A wood species identification model based on wood anatomy and using the Cyclobalanopsis genus wood cell geometric dataset was proposed. The model was enhanced by the CTGAN deep learning algorithm and used a simulated cell geometric feature dataset. The machine learning models BPNN and SVM were trained respectively for recognition of three Cyclobalanopsis species with simulated vessel cells and simulated wood fiber cells. RESULTS: The SVM model and BPNN model achieved recognition accuracy of 96.4% and 99.6%, respectively, on the real dataset, using the CTGAN-generated vessel dataset. The BPNN model and SVM model achieved recognition accuracy of 75.5% and 77.9% on real dataset, respectively, using the CTGAN-generated wood fiber dataset. DISCUSSION: The machine learning model trained based on the enhanced cell geometric feature data by CTGAN achieved good recognition of Cyclobalanopsis, with the SVM model having a higher prediction accuracy than BPNN. The machine learning models were interpreted based on LIME to explore how they identify tree species based on wood cell geometric features. This proposed model can be used for efficient and cost-effective identification of wood species in industrial applications. Frontiers Media S.A. 2023-07-07 /pmc/articles/PMC10361066/ /pubmed/37484454 http://dx.doi.org/10.3389/fpls.2023.1203836 Text en Copyright © 2023 Zhan, Chen, Wu, Yang, Lin, Lin and Guan https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Plant Science
Zhan, Weihui
Chen, Bowen
Wu, Xiaolian
Yang, Zhen
Lin, Che
Lin, Jinguo
Guan, Xin
Wood identification of Cyclobalanopsis (Endl.) Oerst based on microscopic features and CTGAN-enhanced explainable machine learning models
title Wood identification of Cyclobalanopsis (Endl.) Oerst based on microscopic features and CTGAN-enhanced explainable machine learning models
title_full Wood identification of Cyclobalanopsis (Endl.) Oerst based on microscopic features and CTGAN-enhanced explainable machine learning models
title_fullStr Wood identification of Cyclobalanopsis (Endl.) Oerst based on microscopic features and CTGAN-enhanced explainable machine learning models
title_full_unstemmed Wood identification of Cyclobalanopsis (Endl.) Oerst based on microscopic features and CTGAN-enhanced explainable machine learning models
title_short Wood identification of Cyclobalanopsis (Endl.) Oerst based on microscopic features and CTGAN-enhanced explainable machine learning models
title_sort wood identification of cyclobalanopsis (endl.) oerst based on microscopic features and ctgan-enhanced explainable machine learning models
topic Plant Science
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10361066/
https://www.ncbi.nlm.nih.gov/pubmed/37484454
http://dx.doi.org/10.3389/fpls.2023.1203836
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