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

Autor:	Lee DY; Department of Botany and Plant Pathology, Purdue University, West Lafayette, IN, United States., Na DY; School of Electrical and Computer Engineering, Purdue University, West Lafayette, IN, United States., Góngora-Canul C; Department of Botany and Plant Pathology, Purdue University, West Lafayette, IN, United States.; Tecnológico Nacional de México, Instituto Tecnológico de Conkal, Yucatán, Mexico., Baireddy S; School of Electrical and Computer Engineering, Purdue University, West Lafayette, IN, United States., Lane B; Department of Botany and Plant Pathology, Purdue University, West Lafayette, IN, United States., Cruz AP; Department of Botany and Plant Pathology, Purdue University, West Lafayette, IN, United States., Fernández-Campos M; Department of Botany and Plant Pathology, Purdue University, West Lafayette, IN, United States., Kleczewski NM; Department of Crop Science, University of Illinois, Urbana, IL, United States., Telenko DEP; Department of Botany and Plant Pathology, Purdue University, West Lafayette, IN, United States., Goodwin SB; Department of Botany and Plant Pathology, Purdue University, West Lafayette, IN, United States.; U.S. Department of Agriculture-Agricultural Research Service, West Lafayette, IN, United States., Delp EJ; School of Electrical and Computer Engineering, Purdue University, West Lafayette, IN, United States., Cruz CD; Department of Botany and Plant Pathology, Purdue University, West Lafayette, IN, United States.
Jazyk:	angličtina
Zdroj:	Frontiers in plant science [Front Plant Sci] 2021 Oct 01; Vol. 12, pp. 675975. Date of Electronic Publication: 2021 Oct 01 (Print Publication: 2021).
DOI:	10.3389/fpls.2021.675975
Abstrakt:	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. Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. (Copyright © 2021 Lee, Na, Góngora-Canul, Baireddy, Lane, Cruz, Fernández-Campos, Kleczewski, Telenko, Goodwin, Delp and Cruz.)
Databáze:	MEDLINE
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