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Tobacco shred varieties classification using Multi-Scale-X-ResNet network and machine vision
The primary task in calculating the tobacco shred blending ratio is identifying the four tobacco shred types: expanded tobacco silk, cut stem, tobacco silk, and reconstituted tobacco shred. The classification precision directly affects the subsequent determination of tobacco shred components. Howeve...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9433752/ https://www.ncbi.nlm.nih.gov/pubmed/36061766 http://dx.doi.org/10.3389/fpls.2022.962664 |
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author | Niu, Qunfeng Liu, Jiangpeng Jin, Yi Chen, Xia Zhu, Wenkui Yuan, Qiang |
author_facet | Niu, Qunfeng Liu, Jiangpeng Jin, Yi Chen, Xia Zhu, Wenkui Yuan, Qiang |
author_sort | Niu, Qunfeng |
collection | PubMed |
description | The primary task in calculating the tobacco shred blending ratio is identifying the four tobacco shred types: expanded tobacco silk, cut stem, tobacco silk, and reconstituted tobacco shred. The classification precision directly affects the subsequent determination of tobacco shred components. However, the tobacco shred types, especially expanded tobacco silk and tobacco silk, have no apparent differences in macro-scale characteristics. The tobacco shreds have small size and irregular shape characteristics, creating significant challenges in their recognition and classification based on machine vision. This study provides a complete set of solutions aimed at this problem for screening tobacco shred samples, taking images, image preprocessing, establishing datasets, and identifying types. A block threshold binarization method is used for image preprocessing. Parameter setting and method performance are researched to obtain the maximum number of complete samples with acceptable execution time. ResNet50 is used as the primary classification and recognition network structure. By increasing the multi-scale structure and optimizing the number of blocks and loss function, a new tobacco shred image classification method is proposed based on the MS-X-ResNet (Multi-Scale-X-ResNet) network. Specifically, the MS-ResNet network is obtained by fusing the multi-scale Stage 3 low-dimensional and Stage 4 high-dimensional features to reduce the overfitting risk. The number of blocks in Stages 1–4 are adjusted from the original 3:4:6:3 to 3:4:N:3 (A-ResNet) and 3:3:N:3 (B-ResNet) to obtain the X-ResNet network, which improves the model’s classification performance with lower complexity. The focal loss function is selected to reduce the impact of identification difficulty for different sample types on the network and improve its performance. The experimental results show that the final classification accuracy of the network on a tobacco shred dataset is 96.56%. The image recognition of a single tobacco shred requires 103 ms, achieving high classification accuracy and efficiency. The image preprocessing and deep learning algorithms for tobacco shred classification and identification proposed in this study provide a new implementation approach for the actual production and quality detection of tobacco and a new way for online real-time type identification of other agricultural products. |
format | Online Article Text |
id | pubmed-9433752 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-94337522022-09-02 Tobacco shred varieties classification using Multi-Scale-X-ResNet network and machine vision Niu, Qunfeng Liu, Jiangpeng Jin, Yi Chen, Xia Zhu, Wenkui Yuan, Qiang Front Plant Sci Plant Science The primary task in calculating the tobacco shred blending ratio is identifying the four tobacco shred types: expanded tobacco silk, cut stem, tobacco silk, and reconstituted tobacco shred. The classification precision directly affects the subsequent determination of tobacco shred components. However, the tobacco shred types, especially expanded tobacco silk and tobacco silk, have no apparent differences in macro-scale characteristics. The tobacco shreds have small size and irregular shape characteristics, creating significant challenges in their recognition and classification based on machine vision. This study provides a complete set of solutions aimed at this problem for screening tobacco shred samples, taking images, image preprocessing, establishing datasets, and identifying types. A block threshold binarization method is used for image preprocessing. Parameter setting and method performance are researched to obtain the maximum number of complete samples with acceptable execution time. ResNet50 is used as the primary classification and recognition network structure. By increasing the multi-scale structure and optimizing the number of blocks and loss function, a new tobacco shred image classification method is proposed based on the MS-X-ResNet (Multi-Scale-X-ResNet) network. Specifically, the MS-ResNet network is obtained by fusing the multi-scale Stage 3 low-dimensional and Stage 4 high-dimensional features to reduce the overfitting risk. The number of blocks in Stages 1–4 are adjusted from the original 3:4:6:3 to 3:4:N:3 (A-ResNet) and 3:3:N:3 (B-ResNet) to obtain the X-ResNet network, which improves the model’s classification performance with lower complexity. The focal loss function is selected to reduce the impact of identification difficulty for different sample types on the network and improve its performance. The experimental results show that the final classification accuracy of the network on a tobacco shred dataset is 96.56%. The image recognition of a single tobacco shred requires 103 ms, achieving high classification accuracy and efficiency. The image preprocessing and deep learning algorithms for tobacco shred classification and identification proposed in this study provide a new implementation approach for the actual production and quality detection of tobacco and a new way for online real-time type identification of other agricultural products. Frontiers Media S.A. 2022-08-18 /pmc/articles/PMC9433752/ /pubmed/36061766 http://dx.doi.org/10.3389/fpls.2022.962664 Text en Copyright © 2022 Niu, Liu, Jin, Chen, Zhu and Yuan. 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 Niu, Qunfeng Liu, Jiangpeng Jin, Yi Chen, Xia Zhu, Wenkui Yuan, Qiang Tobacco shred varieties classification using Multi-Scale-X-ResNet network and machine vision |
title | Tobacco shred varieties classification using Multi-Scale-X-ResNet network and machine vision |
title_full | Tobacco shred varieties classification using Multi-Scale-X-ResNet network and machine vision |
title_fullStr | Tobacco shred varieties classification using Multi-Scale-X-ResNet network and machine vision |
title_full_unstemmed | Tobacco shred varieties classification using Multi-Scale-X-ResNet network and machine vision |
title_short | Tobacco shred varieties classification using Multi-Scale-X-ResNet network and machine vision |
title_sort | tobacco shred varieties classification using multi-scale-x-resnet network and machine vision |
topic | Plant Science |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9433752/ https://www.ncbi.nlm.nih.gov/pubmed/36061766 http://dx.doi.org/10.3389/fpls.2022.962664 |
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