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Feasibility Study of Combining Hyperspectral Imaging with Deep Learning for Chestnut-Quality Detection
The rapid detection of chestnut quality is a critical aspect of chestnut processing. However, traditional imaging methods pose a challenge for chestnut-quality detection due to the absence of visible epidermis symptoms. This study aims to develop a quick and efficient detection method using hyperspe...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10217303/ https://www.ncbi.nlm.nih.gov/pubmed/37238907 http://dx.doi.org/10.3390/foods12102089 |
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author | Zhong, Qiongda Zhang, Hu Tang, Shuqi Li, Peng Lin, Caixia Zhang, Ling Zhong, Nan |
author_facet | Zhong, Qiongda Zhang, Hu Tang, Shuqi Li, Peng Lin, Caixia Zhang, Ling Zhong, Nan |
author_sort | Zhong, Qiongda |
collection | PubMed |
description | The rapid detection of chestnut quality is a critical aspect of chestnut processing. However, traditional imaging methods pose a challenge for chestnut-quality detection due to the absence of visible epidermis symptoms. This study aims to develop a quick and efficient detection method using hyperspectral imaging (HSI, 935–1720 nm) and deep learning modeling for qualitative and quantitative identification of chestnut quality. Firstly, we used principal component analysis (PCA) to visualize the qualitative analysis of chestnut quality, followed by the application of three pre-processing methods to the spectra. To compare the accuracy of different models for chestnut-quality detection, traditional machine learning models and deep learning models were constructed. Results showed that deep learning models were more accurate, with FD-LSTM achieving the highest accuracy of 99.72%. Moreover, the study identified important wavelengths for chestnut-quality detection at around 1000, 1400 and 1600 nm, to improve the efficiency of the model. The FD-UVE-CNN model achieved the highest accuracy of 97.33% after incorporating the important wavelength identification process. By using the important wavelengths as input for the deep learning network model, recognition time decreased on average by 39 s. After a comprehensive analysis, FD-UVE-CNN was deter-mined to be the most effective model for chestnut-quality detection. This study suggests that deep learning combined with HSI has potential for chestnut-quality detection, and the results are encouraging. |
format | Online Article Text |
id | pubmed-10217303 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-102173032023-05-27 Feasibility Study of Combining Hyperspectral Imaging with Deep Learning for Chestnut-Quality Detection Zhong, Qiongda Zhang, Hu Tang, Shuqi Li, Peng Lin, Caixia Zhang, Ling Zhong, Nan Foods Article The rapid detection of chestnut quality is a critical aspect of chestnut processing. However, traditional imaging methods pose a challenge for chestnut-quality detection due to the absence of visible epidermis symptoms. This study aims to develop a quick and efficient detection method using hyperspectral imaging (HSI, 935–1720 nm) and deep learning modeling for qualitative and quantitative identification of chestnut quality. Firstly, we used principal component analysis (PCA) to visualize the qualitative analysis of chestnut quality, followed by the application of three pre-processing methods to the spectra. To compare the accuracy of different models for chestnut-quality detection, traditional machine learning models and deep learning models were constructed. Results showed that deep learning models were more accurate, with FD-LSTM achieving the highest accuracy of 99.72%. Moreover, the study identified important wavelengths for chestnut-quality detection at around 1000, 1400 and 1600 nm, to improve the efficiency of the model. The FD-UVE-CNN model achieved the highest accuracy of 97.33% after incorporating the important wavelength identification process. By using the important wavelengths as input for the deep learning network model, recognition time decreased on average by 39 s. After a comprehensive analysis, FD-UVE-CNN was deter-mined to be the most effective model for chestnut-quality detection. This study suggests that deep learning combined with HSI has potential for chestnut-quality detection, and the results are encouraging. MDPI 2023-05-22 /pmc/articles/PMC10217303/ /pubmed/37238907 http://dx.doi.org/10.3390/foods12102089 Text en © 2023 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 Zhong, Qiongda Zhang, Hu Tang, Shuqi Li, Peng Lin, Caixia Zhang, Ling Zhong, Nan Feasibility Study of Combining Hyperspectral Imaging with Deep Learning for Chestnut-Quality Detection |
title | Feasibility Study of Combining Hyperspectral Imaging with Deep Learning for Chestnut-Quality Detection |
title_full | Feasibility Study of Combining Hyperspectral Imaging with Deep Learning for Chestnut-Quality Detection |
title_fullStr | Feasibility Study of Combining Hyperspectral Imaging with Deep Learning for Chestnut-Quality Detection |
title_full_unstemmed | Feasibility Study of Combining Hyperspectral Imaging with Deep Learning for Chestnut-Quality Detection |
title_short | Feasibility Study of Combining Hyperspectral Imaging with Deep Learning for Chestnut-Quality Detection |
title_sort | feasibility study of combining hyperspectral imaging with deep learning for chestnut-quality detection |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10217303/ https://www.ncbi.nlm.nih.gov/pubmed/37238907 http://dx.doi.org/10.3390/foods12102089 |
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