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Contour-Based Detection and Quantification of Tar Spot Stromata Using Red-Green-Blue (RGB) Imagery

Quantifying symptoms of tar spot of corn has been conducted through visual-based estimations of the proportion of leaf area covered by the pathogenic structures generated by Phyllachora maydis (stromata). However, this traditional approach is costly in terms of time and labor, as well as prone to hu...

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Autores principales: Lee, Da-Young, Na, Dong-Yeop, Góngora-Canul, Carlos, Baireddy, Sriram, Lane, Brenden, Cruz, Andres P., Fernández-Campos, Mariela, Kleczewski, Nathan M., Telenko, Darcy E. P., Goodwin, Stephen B., Delp, Edward J., Cruz, C. D.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8517539/
https://www.ncbi.nlm.nih.gov/pubmed/34659275
http://dx.doi.org/10.3389/fpls.2021.675975
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author Lee, Da-Young
Na, Dong-Yeop
Góngora-Canul, Carlos
Baireddy, Sriram
Lane, Brenden
Cruz, Andres P.
Fernández-Campos, Mariela
Kleczewski, Nathan M.
Telenko, Darcy E. P.
Goodwin, Stephen B.
Delp, Edward J.
Cruz, C. D.
author_facet Lee, Da-Young
Na, Dong-Yeop
Góngora-Canul, Carlos
Baireddy, Sriram
Lane, Brenden
Cruz, Andres P.
Fernández-Campos, Mariela
Kleczewski, Nathan M.
Telenko, Darcy E. P.
Goodwin, Stephen B.
Delp, Edward J.
Cruz, C. D.
author_sort Lee, Da-Young
collection PubMed
description Quantifying symptoms of tar spot of corn has been conducted through visual-based estimations of the proportion of leaf area covered by the pathogenic structures generated by Phyllachora maydis (stromata). However, this traditional approach is costly in terms of time and labor, as well as prone to human subjectivity. An objective and accurate method, which is also time and labor-efficient, is of an urgent need for tar spot surveillance and high-throughput disease phenotyping. Here, we present the use of contour-based detection of fungal stromata to quantify disease intensity using Red-Green-Blue (RGB) images of tar spot-infected corn leaves. Image blocks (n = 1,130) generated by uniform partitioning the RGB images of leaves, were analyzed for their number of stromata by two independent, experienced human raters using ImageJ (visual estimates) and the experimental stromata contour detection algorithm (SCDA; digital measurements). Stromata count for each image block was then categorized into five classes and tested for the agreement of human raters and SCDA using Cohen's weighted kappa coefficient (κ). Adequate agreements of stromata counts were observed for each of the human raters to SCDA (κ = 0.83) and between the two human raters (κ = 0.95). Moreover, the SCDA was able to recognize “true stromata,” but to a lesser extent than human raters (average median recall = 90.5%, precision = 89.7%, and Dice = 88.3%). Furthermore, we tracked tar spot development throughout six time points using SCDA and we obtained high agreement between area under the disease progress curve (AUDPC) shared by visual disease severity and SCDA. Our results indicate the potential utility of SCDA in quantifying stromata using RGB images, complementing the traditional human, visual-based disease severity estimations, and serve as a foundation in building an accurate, high-throughput pipeline for the scoring of tar spot symptoms.
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spelling pubmed-85175392021-10-16 Contour-Based Detection and Quantification of Tar Spot Stromata Using Red-Green-Blue (RGB) Imagery Lee, Da-Young Na, Dong-Yeop Góngora-Canul, Carlos Baireddy, Sriram Lane, Brenden Cruz, Andres P. Fernández-Campos, Mariela Kleczewski, Nathan M. Telenko, Darcy E. P. Goodwin, Stephen B. Delp, Edward J. Cruz, C. D. Front Plant Sci Plant Science Quantifying symptoms of tar spot of corn has been conducted through visual-based estimations of the proportion of leaf area covered by the pathogenic structures generated by Phyllachora maydis (stromata). However, this traditional approach is costly in terms of time and labor, as well as prone to human subjectivity. An objective and accurate method, which is also time and labor-efficient, is of an urgent need for tar spot surveillance and high-throughput disease phenotyping. Here, we present the use of contour-based detection of fungal stromata to quantify disease intensity using Red-Green-Blue (RGB) images of tar spot-infected corn leaves. Image blocks (n = 1,130) generated by uniform partitioning the RGB images of leaves, were analyzed for their number of stromata by two independent, experienced human raters using ImageJ (visual estimates) and the experimental stromata contour detection algorithm (SCDA; digital measurements). Stromata count for each image block was then categorized into five classes and tested for the agreement of human raters and SCDA using Cohen's weighted kappa coefficient (κ). Adequate agreements of stromata counts were observed for each of the human raters to SCDA (κ = 0.83) and between the two human raters (κ = 0.95). Moreover, the SCDA was able to recognize “true stromata,” but to a lesser extent than human raters (average median recall = 90.5%, precision = 89.7%, and Dice = 88.3%). Furthermore, we tracked tar spot development throughout six time points using SCDA and we obtained high agreement between area under the disease progress curve (AUDPC) shared by visual disease severity and SCDA. Our results indicate the potential utility of SCDA in quantifying stromata using RGB images, complementing the traditional human, visual-based disease severity estimations, and serve as a foundation in building an accurate, high-throughput pipeline for the scoring of tar spot symptoms. Frontiers Media S.A. 2021-10-01 /pmc/articles/PMC8517539/ /pubmed/34659275 http://dx.doi.org/10.3389/fpls.2021.675975 Text en Copyright © 2021 Lee, Na, Góngora-Canul, Baireddy, Lane, Cruz, Fernández-Campos, Kleczewski, Telenko, Goodwin, Delp and Cruz. 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
Lee, Da-Young
Na, Dong-Yeop
Góngora-Canul, Carlos
Baireddy, Sriram
Lane, Brenden
Cruz, Andres P.
Fernández-Campos, Mariela
Kleczewski, Nathan M.
Telenko, Darcy E. P.
Goodwin, Stephen B.
Delp, Edward J.
Cruz, C. D.
Contour-Based Detection and Quantification of Tar Spot Stromata Using Red-Green-Blue (RGB) Imagery
title Contour-Based Detection and Quantification of Tar Spot Stromata Using Red-Green-Blue (RGB) Imagery
title_full Contour-Based Detection and Quantification of Tar Spot Stromata Using Red-Green-Blue (RGB) Imagery
title_fullStr Contour-Based Detection and Quantification of Tar Spot Stromata Using Red-Green-Blue (RGB) Imagery
title_full_unstemmed Contour-Based Detection and Quantification of Tar Spot Stromata Using Red-Green-Blue (RGB) Imagery
title_short Contour-Based Detection and Quantification of Tar Spot Stromata Using Red-Green-Blue (RGB) Imagery
title_sort contour-based detection and quantification of tar spot stromata using red-green-blue (rgb) imagery
topic Plant Science
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8517539/
https://www.ncbi.nlm.nih.gov/pubmed/34659275
http://dx.doi.org/10.3389/fpls.2021.675975
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