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Last-percent improvement in eligibility rates of crop seeds based on quality evaluation using near-infrared imaging spectrometry

As the world population continues to grow, the need for high-quality crop seeds that promise stable food production is increasing. Conversely, excessive demand for high quality is causing “seed loss and waste” due to slight shortfalls in eligibility rates. In this study, we applied near-infrared ima...

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Detalles Bibliográficos
Autores principales: Matsuda, Osamu, Ohara, Yoshinori
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10511137/
https://www.ncbi.nlm.nih.gov/pubmed/37729130
http://dx.doi.org/10.1371/journal.pone.0291105
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author Matsuda, Osamu
Ohara, Yoshinori
author_facet Matsuda, Osamu
Ohara, Yoshinori
author_sort Matsuda, Osamu
collection PubMed
description As the world population continues to grow, the need for high-quality crop seeds that promise stable food production is increasing. Conversely, excessive demand for high quality is causing “seed loss and waste” due to slight shortfalls in eligibility rates. In this study, we applied near-infrared imaging spectrometry combined with machine learning techniques to evaluate germinability and paternal haplotype in crop seeds from 6 species and 8 cultivars. Candidate discriminants for quality evaluation were derived by linear sparse modeling using the seed reflectance spectra as explanatory variables. To systematically proceed with model selection, we defined the sorting condition where the recovery rate of seeds matches the initial eligibility rate (iP) as “standard condition”. How much the eligibility rate after sorting (P) increases from iP under this condition offers a reasonable criterion for ranking candidate models. Moreover, the model performance under conditions with adjusted discrimination strength was verified using a metric “relative precision” (rP) defined as (P–iP)/(1–iP). Because rP, compared to precision (= P), is less dependent on iP in relation to recall (R), i.e., recovery rate of eligible seeds, the rP-R curve and area under the curve also offer useful criteria for spotting better discriminant models. We confirmed that the batches of seeds given higher discriminant scores by the models selected with reference to these criteria were more enriched with eligible seeds. The method presented can be readily implemented in developing a sorting device that enables “last-percent improvement” in eligibility rates of crop seeds.
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spelling pubmed-105111372023-09-21 Last-percent improvement in eligibility rates of crop seeds based on quality evaluation using near-infrared imaging spectrometry Matsuda, Osamu Ohara, Yoshinori PLoS One Research Article As the world population continues to grow, the need for high-quality crop seeds that promise stable food production is increasing. Conversely, excessive demand for high quality is causing “seed loss and waste” due to slight shortfalls in eligibility rates. In this study, we applied near-infrared imaging spectrometry combined with machine learning techniques to evaluate germinability and paternal haplotype in crop seeds from 6 species and 8 cultivars. Candidate discriminants for quality evaluation were derived by linear sparse modeling using the seed reflectance spectra as explanatory variables. To systematically proceed with model selection, we defined the sorting condition where the recovery rate of seeds matches the initial eligibility rate (iP) as “standard condition”. How much the eligibility rate after sorting (P) increases from iP under this condition offers a reasonable criterion for ranking candidate models. Moreover, the model performance under conditions with adjusted discrimination strength was verified using a metric “relative precision” (rP) defined as (P–iP)/(1–iP). Because rP, compared to precision (= P), is less dependent on iP in relation to recall (R), i.e., recovery rate of eligible seeds, the rP-R curve and area under the curve also offer useful criteria for spotting better discriminant models. We confirmed that the batches of seeds given higher discriminant scores by the models selected with reference to these criteria were more enriched with eligible seeds. The method presented can be readily implemented in developing a sorting device that enables “last-percent improvement” in eligibility rates of crop seeds. Public Library of Science 2023-09-20 /pmc/articles/PMC10511137/ /pubmed/37729130 http://dx.doi.org/10.1371/journal.pone.0291105 Text en © 2023 Matsuda, Ohara https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Matsuda, Osamu
Ohara, Yoshinori
Last-percent improvement in eligibility rates of crop seeds based on quality evaluation using near-infrared imaging spectrometry
title Last-percent improvement in eligibility rates of crop seeds based on quality evaluation using near-infrared imaging spectrometry
title_full Last-percent improvement in eligibility rates of crop seeds based on quality evaluation using near-infrared imaging spectrometry
title_fullStr Last-percent improvement in eligibility rates of crop seeds based on quality evaluation using near-infrared imaging spectrometry
title_full_unstemmed Last-percent improvement in eligibility rates of crop seeds based on quality evaluation using near-infrared imaging spectrometry
title_short Last-percent improvement in eligibility rates of crop seeds based on quality evaluation using near-infrared imaging spectrometry
title_sort last-percent improvement in eligibility rates of crop seeds based on quality evaluation using near-infrared imaging spectrometry
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10511137/
https://www.ncbi.nlm.nih.gov/pubmed/37729130
http://dx.doi.org/10.1371/journal.pone.0291105
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