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Automatic detection and counting of urediniospores of Puccinia striiformis f. sp. tritici using spore traps and image processing

The quantitative monitoring of airborne urediniospores of Puccinia striiformis f. sp. tritici (Pst) using spore trap devices in wheat fields is an important process for devising strategies early and effectively controlling wheat stripe rust. The traditional microscopic spore counting method mainly r...

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Autores principales: Lei, Yu, Yao, Zhifeng, He, Dongjian
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6134082/
https://www.ncbi.nlm.nih.gov/pubmed/30206343
http://dx.doi.org/10.1038/s41598-018-31899-0
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author Lei, Yu
Yao, Zhifeng
He, Dongjian
author_facet Lei, Yu
Yao, Zhifeng
He, Dongjian
author_sort Lei, Yu
collection PubMed
description The quantitative monitoring of airborne urediniospores of Puccinia striiformis f. sp. tritici (Pst) using spore trap devices in wheat fields is an important process for devising strategies early and effectively controlling wheat stripe rust. The traditional microscopic spore counting method mainly relies on naked-eye observation. Because of the great number of trapped spores, this method is labour intensive and time-consuming and has low counting efficiency, sometimes leading to huge errors; thus, an alternative method is required. In this paper, a new algorithm was proposed for the automatic detection and counting of urediniospores of Pst, based on digital image processing. First, images of urediniospores were collected using portable volumetric spore traps in an indoor simulation. Then, the urediniospores were automatically detected and counted using a series of image processing approaches, including image segmentation using the K-means clustering algorithm, image pre-processing, the identification of touching urediniospores based on their shape factor and area, and touching urediniospore contour segmentation based on concavity and contour segment merging. This automatic counting algorithm was compared with the watershed transformation algorithm. The results show that the proposed algorithm is efficient and accurate for the automatic detection and counting of trapped urediniospores. It can provide technical support for the development of online airborne urediniospore monitoring equipment.
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spelling pubmed-61340822018-09-15 Automatic detection and counting of urediniospores of Puccinia striiformis f. sp. tritici using spore traps and image processing Lei, Yu Yao, Zhifeng He, Dongjian Sci Rep Article The quantitative monitoring of airborne urediniospores of Puccinia striiformis f. sp. tritici (Pst) using spore trap devices in wheat fields is an important process for devising strategies early and effectively controlling wheat stripe rust. The traditional microscopic spore counting method mainly relies on naked-eye observation. Because of the great number of trapped spores, this method is labour intensive and time-consuming and has low counting efficiency, sometimes leading to huge errors; thus, an alternative method is required. In this paper, a new algorithm was proposed for the automatic detection and counting of urediniospores of Pst, based on digital image processing. First, images of urediniospores were collected using portable volumetric spore traps in an indoor simulation. Then, the urediniospores were automatically detected and counted using a series of image processing approaches, including image segmentation using the K-means clustering algorithm, image pre-processing, the identification of touching urediniospores based on their shape factor and area, and touching urediniospore contour segmentation based on concavity and contour segment merging. This automatic counting algorithm was compared with the watershed transformation algorithm. The results show that the proposed algorithm is efficient and accurate for the automatic detection and counting of trapped urediniospores. It can provide technical support for the development of online airborne urediniospore monitoring equipment. Nature Publishing Group UK 2018-09-11 /pmc/articles/PMC6134082/ /pubmed/30206343 http://dx.doi.org/10.1038/s41598-018-31899-0 Text en © The Author(s) 2018 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.
spellingShingle Article
Lei, Yu
Yao, Zhifeng
He, Dongjian
Automatic detection and counting of urediniospores of Puccinia striiformis f. sp. tritici using spore traps and image processing
title Automatic detection and counting of urediniospores of Puccinia striiformis f. sp. tritici using spore traps and image processing
title_full Automatic detection and counting of urediniospores of Puccinia striiformis f. sp. tritici using spore traps and image processing
title_fullStr Automatic detection and counting of urediniospores of Puccinia striiformis f. sp. tritici using spore traps and image processing
title_full_unstemmed Automatic detection and counting of urediniospores of Puccinia striiformis f. sp. tritici using spore traps and image processing
title_short Automatic detection and counting of urediniospores of Puccinia striiformis f. sp. tritici using spore traps and image processing
title_sort automatic detection and counting of urediniospores of puccinia striiformis f. sp. tritici using spore traps and image processing
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6134082/
https://www.ncbi.nlm.nih.gov/pubmed/30206343
http://dx.doi.org/10.1038/s41598-018-31899-0
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