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Rapid Measurement of Soybean Seed Viability Using Kernel-Based Multispectral Image Analysis
Viability is an important quality factor influencing seed germination and crop yield. Current seed-viability testing methods rely on conventional manual inspections, which use destructive, labor-intensive and time-consuming measurements. The aim of this study is to distinguish between viable and non...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6359339/ https://www.ncbi.nlm.nih.gov/pubmed/30641923 http://dx.doi.org/10.3390/s19020271 |
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author | Baek, Insuck Kusumaningrum, Dewi Kandpal, Lalit Mohan Lohumi, Santosh Mo, Changyeun Kim, Moon S. Cho, Byoung-Kwan |
author_facet | Baek, Insuck Kusumaningrum, Dewi Kandpal, Lalit Mohan Lohumi, Santosh Mo, Changyeun Kim, Moon S. Cho, Byoung-Kwan |
author_sort | Baek, Insuck |
collection | PubMed |
description | Viability is an important quality factor influencing seed germination and crop yield. Current seed-viability testing methods rely on conventional manual inspections, which use destructive, labor-intensive and time-consuming measurements. The aim of this study is to distinguish between viable and nonviable soybean seeds, using a near-infrared (NIR) hyperspectral imaging (HSI) technique in a rapid and nondestructive manner. The data extracted from the NIR–HSI of viable and nonviable soybean seeds were analyzed using a partial least-squares discrimination analysis (PLS-DA) technique for classifying the viable and nonviable soybean seeds. Variable importance in projection (VIP) was used as a waveband selection method to develop a multispectral imaging model. Initially, the spectral profile of each pixel in the soybean seed images was subjected to PLS-DA analysis, which yielded a reasonable classification accuracy; however, the pixel-based classification method was not successful for high accuracy detection for nonviable seeds. Another viability detection method was then investigated: a kernel image threshold method with an optimum-detection-rate strategy. The kernel-based classification of seeds showed over 95% accuracy even when using only seven optimal wavebands selected through VIP. The results show that the proposed multispectral NIR imaging method is an effective and accurate nondestructive technique for the discrimination of soybean seed viability. |
format | Online Article Text |
id | pubmed-6359339 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-63593392019-02-06 Rapid Measurement of Soybean Seed Viability Using Kernel-Based Multispectral Image Analysis Baek, Insuck Kusumaningrum, Dewi Kandpal, Lalit Mohan Lohumi, Santosh Mo, Changyeun Kim, Moon S. Cho, Byoung-Kwan Sensors (Basel) Article Viability is an important quality factor influencing seed germination and crop yield. Current seed-viability testing methods rely on conventional manual inspections, which use destructive, labor-intensive and time-consuming measurements. The aim of this study is to distinguish between viable and nonviable soybean seeds, using a near-infrared (NIR) hyperspectral imaging (HSI) technique in a rapid and nondestructive manner. The data extracted from the NIR–HSI of viable and nonviable soybean seeds were analyzed using a partial least-squares discrimination analysis (PLS-DA) technique for classifying the viable and nonviable soybean seeds. Variable importance in projection (VIP) was used as a waveband selection method to develop a multispectral imaging model. Initially, the spectral profile of each pixel in the soybean seed images was subjected to PLS-DA analysis, which yielded a reasonable classification accuracy; however, the pixel-based classification method was not successful for high accuracy detection for nonviable seeds. Another viability detection method was then investigated: a kernel image threshold method with an optimum-detection-rate strategy. The kernel-based classification of seeds showed over 95% accuracy even when using only seven optimal wavebands selected through VIP. The results show that the proposed multispectral NIR imaging method is an effective and accurate nondestructive technique for the discrimination of soybean seed viability. MDPI 2019-01-11 /pmc/articles/PMC6359339/ /pubmed/30641923 http://dx.doi.org/10.3390/s19020271 Text en © 2019 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 Baek, Insuck Kusumaningrum, Dewi Kandpal, Lalit Mohan Lohumi, Santosh Mo, Changyeun Kim, Moon S. Cho, Byoung-Kwan Rapid Measurement of Soybean Seed Viability Using Kernel-Based Multispectral Image Analysis |
title | Rapid Measurement of Soybean Seed Viability Using Kernel-Based Multispectral Image Analysis |
title_full | Rapid Measurement of Soybean Seed Viability Using Kernel-Based Multispectral Image Analysis |
title_fullStr | Rapid Measurement of Soybean Seed Viability Using Kernel-Based Multispectral Image Analysis |
title_full_unstemmed | Rapid Measurement of Soybean Seed Viability Using Kernel-Based Multispectral Image Analysis |
title_short | Rapid Measurement of Soybean Seed Viability Using Kernel-Based Multispectral Image Analysis |
title_sort | rapid measurement of soybean seed viability using kernel-based multispectral image analysis |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6359339/ https://www.ncbi.nlm.nih.gov/pubmed/30641923 http://dx.doi.org/10.3390/s19020271 |
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