Tar Spot: An Understudied Disease Threatening Corn Production in the Americas

Valle-Torres, J.; Ross, Tiffanna J.; Plewa, D.; Avellaneda, Mavir Carolina; Check, Jill; Chilvers, Martin I.; Cruz, Andres P.; Lana, Felipe Dalla; Groves, Carol L.; Gongora-Canul, Carlos; Henriquez-Dole, L.; Jamann, Tiffany M.; Kleczewski, Nathan M.; Lipps, Sarah; Malvick, Dean; McCoy, Austin G.; Mueller, Daren S.; Paul, Pierce A.; Puerto, C.; Schloemer, Carde; Raid, Richard N.; Robertson, Alison E.; Roggenkamp, Emily; Smith, Damon; Telenko, Darcy E. P.; Cruz, C. D.

doi:10.1094/pdis-02-20-0449-fe

Cited by 45 publications

(50 citation statements)

References 32 publications

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“…The choice of contour analysis, for the detection of tart spot stromata, was motivated by the morphology of the pathogen structure (stromata), characterized by the protrusion of black and semi-circular regions on the leaf surface, leading to an elevated and rough topology (Valle-Torres et al, 2020). Suppose that a scalar function is defined on the 2-dimensional Cartesian coordinate system, denoted by f (x, y).…”

Section: Generating Contour Lines To Detect Tar Spot Stromatamentioning

confidence: 99%

“…Tar spot, caused by Phyllachora maydis Maubl., is a fungal disease of corn that is endemic to Mexico and to various countries in Central and South America (Maublanc, 1903). The disease has established itself across the northern US since 2015 (Ruhl et al, 2016;McCoy et al, 2018;Dalla Lana et al, 2019;Kleczewski et al, 2019;Mueller et al, 2020;Valle-Torres et al, 2020) resulting in ∼$840 million in losses during 2018(Crop Protection Network, 2021. Phenotyping and surveillance of tar spot have been performed through human visual assessments of disease severity based on the detection of pathogenic structures called stromata.…”

Section: Introductionmentioning

confidence: 99%

“…Phenotyping and surveillance of tar spot have been performed through human visual assessments of disease severity based on the detection of pathogenic structures called stromata. These black-brown, semi-circular growths are produced as a result of P. maydis infection and are embedded in host tissue and can be observed across leaf surfaces and other tissues (Liu, 1973;Hock et al, 1995;Carson, 1999;Kleczewski et al, 2019;Valle-Torres et al, 2020). In addition, P. maydis can produce ascospores in sexual structures embedded in the stromata, acting as inocula (Kleczewski et al, 2019;Valle-Torres et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

“…These black-brown, semi-circular growths are produced as a result of P. maydis infection and are embedded in host tissue and can be observed across leaf surfaces and other tissues (Liu, 1973;Hock et al, 1995;Carson, 1999;Kleczewski et al, 2019;Valle-Torres et al, 2020). In addition, P. maydis can produce ascospores in sexual structures embedded in the stromata, acting as inocula (Kleczewski et al, 2019;Valle-Torres et al, 2020). Therefore, the proportion of stromata relative to the area of the corn leaf has been estimated to reflect tar spot severity.…”

Section: Introductionmentioning

confidence: 99%

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

Lee

Gongora-Canul

et al. 2021

Front. Plant Sci.

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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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Section: Generating Contour Lines To Detect Tar Spot Stromatamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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

Lee

Gongora-Canul

et al. 2021

Front. Plant Sci.

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“…After colonization, there is an incubation period of at least 14 d, after which stromata are produced on foliage, husks, and stalks infected by ascospores. Long‐distance wind dispersal of spores has been observed in several instances (Kleczewski & Bowman, 2020; Valle‐Torres et al., 2020). Ideal conditions for disease development include temperatures ranging from 16 to 23 o C, humidity levels above 75%, and leaf wetness duration of 7 h or more during the night (Hock et al., 1995).…”

Section: Introductionmentioning

confidence: 99%

Identification of resistance for Phyllachora maydis of maize in exotic‐derived germplasm

et al. 2022

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Tar spot of maize (Zea mays L.), caused by the obligate biotroph Phyllachora maydis Maubl., is an emerging disease in the United States and Canada, and the identification of sources of resistance for tar spot will enable the development of resistant hybrids. In 2019 and 2020, 25 accessions from the germplasm enhancement of maize (GEM) project containing exotic introgressions in elite backgrounds were evaluated in nine environments for tar spot severity. Environmental conditions had a major influence on disease development, as tar spot severity varied across locations with only four of the nine locations showing moderate to high levels of disease. In five environments, disease levels were low and disease severity data was not collected or used. Accessions were visually evaluated for tar spot during reproductive growth stages in three environments and during vegetative growth stages in one environment. There was a strong correlation between resistance to P. maydis across locations where accessions were evaluated in reproductive growth stages. Two accessions, GEMS‐0066 and GEMS‐0226, were the most resistant and could prove useful for tar spot resistance breeding. These accessions are publicly available and able to be directly used in breeding programs.

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Phyllachora species infecting maize and other grass species in the Americas represents a complex of closely related species

Broders

Iriarte‐Broders

Bergstrom

et al. 2022

Ecology and Evolution

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The genus Phyllachora contains numerous obligate fungal parasites that produce raised, melanized structures called stromata on their plant hosts referred to as tar spot. Members of this genus are known to infect many grass species but generally do not cause significant damage or defoliation, with the exception of P . maydis which has emerged as an important pathogen of maize throughout the Americas, but the origin of this pathogen remains unknown. To date, species designations for Phyllachora have been based on host associations and morphology, and most species are assumed to be host specific. We assessed the sequence diversity of 186 single stroma isolates collected from 16 hosts representing 15 countries. Samples included both herbarium and contemporary strains that covered a temporal range from 1905 to 2019. These 186 isolates were grouped into five distinct species with strong bootstrap support. We found three closely related, but genetically distinct groups of Phyllachora are capable of infecting maize in the United States, we refer to these as the P . maydis species complex. Based on herbarium specimens, we hypothesize that these three groups in the P . maydis species complex originated from Central America, Mexico, and the Caribbean. Although two of these groups were only found on maize, the third and largest group contained contemporary strains found on maize and other grass hosts, as well as herbarium specimens from maize and other grasses that include 10 species of Phyllachora . The herbarium specimens were previously identified based on morphology and host association. This work represents the first attempt at molecular characterization of Phyllachora species infecting grass hosts and indicates some Phyllachora species can infect a broad range of host species and there may be significant synonymy in the Phyllachora genus.

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Tar Spot: An Understudied Disease Threatening Corn Production in the Americas

Cited by 45 publications

References 32 publications

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

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

Identification of resistance for Phyllachora maydis of maize in exotic‐derived germplasm

Phyllachora species infecting maize and other grass species in the Americas represents a complex of closely related species

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