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Identification of Apple Varieties Using a Multichannel Hyperspectral Imaging System

This paper reports the nondestructive detection of apple varieties using a multichannel hyperspectral imaging system consisting of an illumination fiber and 30 detection fibers arranged at source–detector distances of 1.5–36 mm over the spectral range of 550–1650 nm. Spatially resolved (SR) spectra...

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Autores principales: Huang, Yuping, Yang, Yutu, Sun, Ye, Zhou, Haiyan, Chen, Kunjie
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7571201/
https://www.ncbi.nlm.nih.gov/pubmed/32911790
http://dx.doi.org/10.3390/s20185120
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author Huang, Yuping
Yang, Yutu
Sun, Ye
Zhou, Haiyan
Chen, Kunjie
author_facet Huang, Yuping
Yang, Yutu
Sun, Ye
Zhou, Haiyan
Chen, Kunjie
author_sort Huang, Yuping
collection PubMed
description This paper reports the nondestructive detection of apple varieties using a multichannel hyperspectral imaging system consisting of an illumination fiber and 30 detection fibers arranged at source–detector distances of 1.5–36 mm over the spectral range of 550–1650 nm. Spatially resolved (SR) spectra were obtained for 1500 apples, 500 each of three varieties from the same orchard to avoid environmental and geographical influences. Partial least squares discriminant analysis (PLSDA) models were developed for single SR spectra and spectral combinations to compare their performance of variety detection. To evaluate the effect of spectral range on variety detection, three types of spectra (i.e., visible region: 550–780 nm, near-infrared region: 780–1650 nm, full region: 550–1650 nm) were analyzed and compared. The results showed that the single SR spectra presented a different accuracy for apple variety classification, and the optimal SR spectra varied with spectral types. Spectral combinations had better accuracies for variety detection with best overall classifications of 99.4% for both spectral ranges in the NIR and full regions; however, the spectral combination could not improve the results over the optimal single SR spectra in the visible region. Moreover, the recognition of golden delicious (GD) was better than those of the other two varieties, with the best classification accuracy of 100% for three types of spectra. Overall, the multichannel hyperspectral imaging system provides more spatial-spectral information for the apples, and the results demonstrate that the technique gave excellent classifications, which suggests that the multichannel hyperspectral imaging system has potential for apple variety detection.
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spelling pubmed-75712012020-10-28 Identification of Apple Varieties Using a Multichannel Hyperspectral Imaging System Huang, Yuping Yang, Yutu Sun, Ye Zhou, Haiyan Chen, Kunjie Sensors (Basel) Article This paper reports the nondestructive detection of apple varieties using a multichannel hyperspectral imaging system consisting of an illumination fiber and 30 detection fibers arranged at source–detector distances of 1.5–36 mm over the spectral range of 550–1650 nm. Spatially resolved (SR) spectra were obtained for 1500 apples, 500 each of three varieties from the same orchard to avoid environmental and geographical influences. Partial least squares discriminant analysis (PLSDA) models were developed for single SR spectra and spectral combinations to compare their performance of variety detection. To evaluate the effect of spectral range on variety detection, three types of spectra (i.e., visible region: 550–780 nm, near-infrared region: 780–1650 nm, full region: 550–1650 nm) were analyzed and compared. The results showed that the single SR spectra presented a different accuracy for apple variety classification, and the optimal SR spectra varied with spectral types. Spectral combinations had better accuracies for variety detection with best overall classifications of 99.4% for both spectral ranges in the NIR and full regions; however, the spectral combination could not improve the results over the optimal single SR spectra in the visible region. Moreover, the recognition of golden delicious (GD) was better than those of the other two varieties, with the best classification accuracy of 100% for three types of spectra. Overall, the multichannel hyperspectral imaging system provides more spatial-spectral information for the apples, and the results demonstrate that the technique gave excellent classifications, which suggests that the multichannel hyperspectral imaging system has potential for apple variety detection. MDPI 2020-09-08 /pmc/articles/PMC7571201/ /pubmed/32911790 http://dx.doi.org/10.3390/s20185120 Text en © 2020 by the authors. 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 (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Huang, Yuping
Yang, Yutu
Sun, Ye
Zhou, Haiyan
Chen, Kunjie
Identification of Apple Varieties Using a Multichannel Hyperspectral Imaging System
title Identification of Apple Varieties Using a Multichannel Hyperspectral Imaging System
title_full Identification of Apple Varieties Using a Multichannel Hyperspectral Imaging System
title_fullStr Identification of Apple Varieties Using a Multichannel Hyperspectral Imaging System
title_full_unstemmed Identification of Apple Varieties Using a Multichannel Hyperspectral Imaging System
title_short Identification of Apple Varieties Using a Multichannel Hyperspectral Imaging System
title_sort identification of apple varieties using a multichannel hyperspectral imaging system
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7571201/
https://www.ncbi.nlm.nih.gov/pubmed/32911790
http://dx.doi.org/10.3390/s20185120
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