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An automated method for the assessment of the rice grain germination rate

The germination rate of rice grain is recognized as one of the most significant indicators of seed quality assessment. Currently, grain germination rate is generally determined manually by experienced researchers, which is time-consuming and labor-intensive. In this paper, a new method is proposed f...

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Detalles Bibliográficos
Autores principales: Zhang, Yongzhong, Huang, Hexiao, Xiong, Binbin, Ma, Yan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9810190/
https://www.ncbi.nlm.nih.gov/pubmed/36595528
http://dx.doi.org/10.1371/journal.pone.0279934
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author Zhang, Yongzhong
Huang, Hexiao
Xiong, Binbin
Ma, Yan
author_facet Zhang, Yongzhong
Huang, Hexiao
Xiong, Binbin
Ma, Yan
author_sort Zhang, Yongzhong
collection PubMed
description The germination rate of rice grain is recognized as one of the most significant indicators of seed quality assessment. Currently, grain germination rate is generally determined manually by experienced researchers, which is time-consuming and labor-intensive. In this paper, a new method is proposed for counting the number of grains and germinated grains. In the coarse segmentation process, the k-means clustering algorithm is applied to obtain rough grain-connected regions. We further refine the segmentation results obtained by the k-means algorithm using a one-dimensional Gaussian filter and a fifth-degree polynomial. Next, the optimal single grain area is determined based on the area distribution curve. Accordingly, the number of grains contained in the connected region is equal to the area of the connected region divided by the optimal single grain area. Finally, a novel algorithm is proposed for counting germinated grains. This algorithm is based on the idea that the length of the intersection between the germ and the grain is less than the circumference of the germ. The experimental results show that the mean absolute error of the proposed method for germination rate is 2.7%. And the performance of the proposed method is robust to changes in grain number, grain varieties, scale, illumination, and rotation.
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spelling pubmed-98101902023-01-04 An automated method for the assessment of the rice grain germination rate Zhang, Yongzhong Huang, Hexiao Xiong, Binbin Ma, Yan PLoS One Research Article The germination rate of rice grain is recognized as one of the most significant indicators of seed quality assessment. Currently, grain germination rate is generally determined manually by experienced researchers, which is time-consuming and labor-intensive. In this paper, a new method is proposed for counting the number of grains and germinated grains. In the coarse segmentation process, the k-means clustering algorithm is applied to obtain rough grain-connected regions. We further refine the segmentation results obtained by the k-means algorithm using a one-dimensional Gaussian filter and a fifth-degree polynomial. Next, the optimal single grain area is determined based on the area distribution curve. Accordingly, the number of grains contained in the connected region is equal to the area of the connected region divided by the optimal single grain area. Finally, a novel algorithm is proposed for counting germinated grains. This algorithm is based on the idea that the length of the intersection between the germ and the grain is less than the circumference of the germ. The experimental results show that the mean absolute error of the proposed method for germination rate is 2.7%. And the performance of the proposed method is robust to changes in grain number, grain varieties, scale, illumination, and rotation. Public Library of Science 2023-01-03 /pmc/articles/PMC9810190/ /pubmed/36595528 http://dx.doi.org/10.1371/journal.pone.0279934 Text en © 2023 Zhang et al 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
Zhang, Yongzhong
Huang, Hexiao
Xiong, Binbin
Ma, Yan
An automated method for the assessment of the rice grain germination rate
title An automated method for the assessment of the rice grain germination rate
title_full An automated method for the assessment of the rice grain germination rate
title_fullStr An automated method for the assessment of the rice grain germination rate
title_full_unstemmed An automated method for the assessment of the rice grain germination rate
title_short An automated method for the assessment of the rice grain germination rate
title_sort automated method for the assessment of the rice grain germination rate
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9810190/
https://www.ncbi.nlm.nih.gov/pubmed/36595528
http://dx.doi.org/10.1371/journal.pone.0279934
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