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Two new methods for severity assessment of wheat stripe rust caused by Puccinia striiformis f. sp. tritici
Accurate severity assessment of wheat stripe rust caused by Puccinia striiformis f. sp. tritici is of great significance for phenotypic determination, prediction, and control of the disease. To achieve accurate severity assessment of the disease based on the actual percentages of lesion areas in the...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Frontiers Media S.A.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9808611/ https://www.ncbi.nlm.nih.gov/pubmed/36605952 http://dx.doi.org/10.3389/fpls.2022.1002627 |
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author | Jiang, Qian Wang, Hongli Wang, Haiguang |
author_facet | Jiang, Qian Wang, Hongli Wang, Haiguang |
author_sort | Jiang, Qian |
collection | PubMed |
description | Accurate severity assessment of wheat stripe rust caused by Puccinia striiformis f. sp. tritici is of great significance for phenotypic determination, prediction, and control of the disease. To achieve accurate severity assessment of the disease based on the actual percentages of lesion areas in the areas of the corresponding whole diseased leaves, two new methods were proposed for severity assessment of the disease. In the Adobe Photoshop 2022 software, the acquired images of single diseased leaves of each severity class of the disease were manually segmented, and the numbers of the leaf region pixels and lesion pixels of each diseased leaf were obtained by pixel statistics. After calculation of the actual percentages of lesion areas in the areas of the corresponding whole diseased leaves based on the obtained pixel numbers, the training sets and testing sets were constructed for each severity class by using the system sampling method with two sampling ratios of 4:1 and 3:2. Then the mean and standard deviation of the actual percentages of lesion areas contained in each training set were calculated, respectively. For each sampling ratio, two methods, one based on the midpoint value of the means of the actual percentages of lesion areas corresponding to two adjacent severity classes and the other based on the distribution range of most of the actual percentages of lesion areas, were used to determine the midpoint-of-two-adjacent-means-based actual percentage reference range and the 90%, 95%, and 99% reference ranges of the actual percentages of lesion areas for each severity class. According to the determined reference ranges, the severity of each diseased leaf in the training sets and testing sets was assessed. The results showed that high assessment accuracies (not lower than 85%) for the training sets and testing sets were achieved, demonstrating that the proposed methods could be used to conduct severity assessment of wheat stripe rust based on the actual percentages of lesion areas. This study provides a reference for accurate severity assessments of plant diseases. |
format | Online Article Text |
id | pubmed-9808611 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-98086112023-01-04 Two new methods for severity assessment of wheat stripe rust caused by Puccinia striiformis f. sp. tritici Jiang, Qian Wang, Hongli Wang, Haiguang Front Plant Sci Plant Science Accurate severity assessment of wheat stripe rust caused by Puccinia striiformis f. sp. tritici is of great significance for phenotypic determination, prediction, and control of the disease. To achieve accurate severity assessment of the disease based on the actual percentages of lesion areas in the areas of the corresponding whole diseased leaves, two new methods were proposed for severity assessment of the disease. In the Adobe Photoshop 2022 software, the acquired images of single diseased leaves of each severity class of the disease were manually segmented, and the numbers of the leaf region pixels and lesion pixels of each diseased leaf were obtained by pixel statistics. After calculation of the actual percentages of lesion areas in the areas of the corresponding whole diseased leaves based on the obtained pixel numbers, the training sets and testing sets were constructed for each severity class by using the system sampling method with two sampling ratios of 4:1 and 3:2. Then the mean and standard deviation of the actual percentages of lesion areas contained in each training set were calculated, respectively. For each sampling ratio, two methods, one based on the midpoint value of the means of the actual percentages of lesion areas corresponding to two adjacent severity classes and the other based on the distribution range of most of the actual percentages of lesion areas, were used to determine the midpoint-of-two-adjacent-means-based actual percentage reference range and the 90%, 95%, and 99% reference ranges of the actual percentages of lesion areas for each severity class. According to the determined reference ranges, the severity of each diseased leaf in the training sets and testing sets was assessed. The results showed that high assessment accuracies (not lower than 85%) for the training sets and testing sets were achieved, demonstrating that the proposed methods could be used to conduct severity assessment of wheat stripe rust based on the actual percentages of lesion areas. This study provides a reference for accurate severity assessments of plant diseases. Frontiers Media S.A. 2022-10-03 /pmc/articles/PMC9808611/ /pubmed/36605952 http://dx.doi.org/10.3389/fpls.2022.1002627 Text en Copyright © 2022 Jiang, Wang and Wang https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Plant Science Jiang, Qian Wang, Hongli Wang, Haiguang Two new methods for severity assessment of wheat stripe rust caused by Puccinia striiformis f. sp. tritici |
title | Two new methods for severity assessment of wheat stripe rust caused by Puccinia striiformis f. sp. tritici
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title_full | Two new methods for severity assessment of wheat stripe rust caused by Puccinia striiformis f. sp. tritici
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title_fullStr | Two new methods for severity assessment of wheat stripe rust caused by Puccinia striiformis f. sp. tritici
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title_full_unstemmed | Two new methods for severity assessment of wheat stripe rust caused by Puccinia striiformis f. sp. tritici
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title_short | Two new methods for severity assessment of wheat stripe rust caused by Puccinia striiformis f. sp. tritici
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title_sort | two new methods for severity assessment of wheat stripe rust caused by puccinia striiformis f. sp. tritici |
topic | Plant Science |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9808611/ https://www.ncbi.nlm.nih.gov/pubmed/36605952 http://dx.doi.org/10.3389/fpls.2022.1002627 |
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