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Prediction approach of larch wood density from visible–near-infrared spectroscopy based on parameter calibrating and transfer learning

Wood density, as a key indicator to measure wood properties, is of weighty significance in enhancing wood utilization and modifying wood properties in sustainable forest management. Visible–near-infrared (Vis-NIR) spectroscopy provides a feasible and efficient solution for obtaining wood density by...

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Autores principales: Zhang, Zheyu, Li, Yaoxiang, Li, Ying
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9577256/
https://www.ncbi.nlm.nih.gov/pubmed/36267936
http://dx.doi.org/10.3389/fpls.2022.1006292
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author Zhang, Zheyu
Li, Yaoxiang
Li, Ying
author_facet Zhang, Zheyu
Li, Yaoxiang
Li, Ying
author_sort Zhang, Zheyu
collection PubMed
description Wood density, as a key indicator to measure wood properties, is of weighty significance in enhancing wood utilization and modifying wood properties in sustainable forest management. Visible–near-infrared (Vis-NIR) spectroscopy provides a feasible and efficient solution for obtaining wood density by the advantages of its efficiency and non-destructiveness. However, the spectral responses are different in wood products with different moisture content conditions, and changes in external factors may cause the regression model to fail. Although some calibration transfer methods and convolutional neural network (CNN)-based deep transfer learning methods have been proposed, the generalization ability and prediction accuracy of the models still need to be improved. For the prediction problem of Vis-NIR wood density in different moisture contents, a deep transfer learning hybrid method with automatic calibration capability (Resnet1D-SVR-TrAdaBoost.R2) was proposed in this study. The disadvantage of overfitting was avoided when CNN processes small sample data, which considered the complex exterior factors in actual production to enhance feature extraction and migration between samples. Density prediction of the method was performed on a larch dataset with different moisture content conditions, and the hybrid method was found to achieve the best prediction results under the calibration samples with different target domain calibration samples and moisture contents, and the performance of models was better than that of the traditional calibration transfer and migration learning methods. In particular, the hybrid model has achieved an improvement of about 0.1 in both R (2) and root mean square error (RMSE) values compared to the support vector regression model transferred by piecewise direct standardization method (SVR+PDS), which has the best performance among traditional calibration methods. To further ascertain the generalizability of the hybrid model, the model was validated with samples collected from mixed moisture contents as the target domain. Various experiments demonstrated that the Resnet1D-SVR-TrAdaBoost.R2 model could predict larch wood density with a high generalization ability and accuracy effectively but was computation consuming. It showed the potential to be extended to predict other metrics of wood.
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spelling pubmed-95772562022-10-19 Prediction approach of larch wood density from visible–near-infrared spectroscopy based on parameter calibrating and transfer learning Zhang, Zheyu Li, Yaoxiang Li, Ying Front Plant Sci Plant Science Wood density, as a key indicator to measure wood properties, is of weighty significance in enhancing wood utilization and modifying wood properties in sustainable forest management. Visible–near-infrared (Vis-NIR) spectroscopy provides a feasible and efficient solution for obtaining wood density by the advantages of its efficiency and non-destructiveness. However, the spectral responses are different in wood products with different moisture content conditions, and changes in external factors may cause the regression model to fail. Although some calibration transfer methods and convolutional neural network (CNN)-based deep transfer learning methods have been proposed, the generalization ability and prediction accuracy of the models still need to be improved. For the prediction problem of Vis-NIR wood density in different moisture contents, a deep transfer learning hybrid method with automatic calibration capability (Resnet1D-SVR-TrAdaBoost.R2) was proposed in this study. The disadvantage of overfitting was avoided when CNN processes small sample data, which considered the complex exterior factors in actual production to enhance feature extraction and migration between samples. Density prediction of the method was performed on a larch dataset with different moisture content conditions, and the hybrid method was found to achieve the best prediction results under the calibration samples with different target domain calibration samples and moisture contents, and the performance of models was better than that of the traditional calibration transfer and migration learning methods. In particular, the hybrid model has achieved an improvement of about 0.1 in both R (2) and root mean square error (RMSE) values compared to the support vector regression model transferred by piecewise direct standardization method (SVR+PDS), which has the best performance among traditional calibration methods. To further ascertain the generalizability of the hybrid model, the model was validated with samples collected from mixed moisture contents as the target domain. Various experiments demonstrated that the Resnet1D-SVR-TrAdaBoost.R2 model could predict larch wood density with a high generalization ability and accuracy effectively but was computation consuming. It showed the potential to be extended to predict other metrics of wood. Frontiers Media S.A. 2022-10-04 /pmc/articles/PMC9577256/ /pubmed/36267936 http://dx.doi.org/10.3389/fpls.2022.1006292 Text en Copyright © 2022 Zhang, Li and Li 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
Zhang, Zheyu
Li, Yaoxiang
Li, Ying
Prediction approach of larch wood density from visible–near-infrared spectroscopy based on parameter calibrating and transfer learning
title Prediction approach of larch wood density from visible–near-infrared spectroscopy based on parameter calibrating and transfer learning
title_full Prediction approach of larch wood density from visible–near-infrared spectroscopy based on parameter calibrating and transfer learning
title_fullStr Prediction approach of larch wood density from visible–near-infrared spectroscopy based on parameter calibrating and transfer learning
title_full_unstemmed Prediction approach of larch wood density from visible–near-infrared spectroscopy based on parameter calibrating and transfer learning
title_short Prediction approach of larch wood density from visible–near-infrared spectroscopy based on parameter calibrating and transfer learning
title_sort prediction approach of larch wood density from visible–near-infrared spectroscopy based on parameter calibrating and transfer learning
topic Plant Science
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9577256/
https://www.ncbi.nlm.nih.gov/pubmed/36267936
http://dx.doi.org/10.3389/fpls.2022.1006292
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