A Quantifiable Disease Severity Rating Scale for Frogeye Leaf Spot of Soybean

Price, Trey; Purvis, Myra; Pruitt, Hunter

doi:10.1094/php-br-15-0054

Cited by 8 publications

(5 citation statements)

References 2 publications

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“…For such types of evaluation standardized, reproducible, accurate, and precise methods are required. Only then can all kinds of variability be avoided when different evaluators are used to measure the same characteristics of the material (PRICE et al, 2016), (NUÑEZ et al, 2017). The most popularly used technique is the diagrammatic scales method because it is a direct and simple measurement process to assess disease severity (ORTEGA et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

A diagramatic scale to quantify severity of antracnose in Rubus glaucus Benth

2019

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Rubus glaucus Benth, commonly referred to as mora de Castilla, is affected by Colletotrichum acutatum, as it induces anthracnose in many of the plant organs. Generally, it affects the fruits during the post-harvest phase and damages them, causing economic losses due to the poor crop quality. At present, no standardized methods are available for this pathosystem that can be used to characterize quantitatively the epidemic and to permit the prediction and comparison of the disease management techniques. In this research, we proposed a logarithmic diagrammatic scale of the severity of anthracnose induced by C. acutatum in the fruits of the thornless variety of R. glaucus Benth. This scale is constructed on the adjustment of the Weber-Fechner law and includes six classes, viz., 0%, 1-6%, 7-25%, 26-50%, 51-75%, 76-100%. The scale was validated using 14 evaluators, which entailed measuring the affected fruits with and without utilizing the scale; this improved the precision, accuracy and reproducibility of the calculations whenever the scale was used. We concluded that the scale proposed can be used to assess the severity of anthracnose induced by the fungus in the R. glaucus Benth fruits.

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Section: Introductionmentioning

confidence: 99%

A diagramatic scale to quantify severity of antracnose in Rubus glaucus Benth

2019

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“…Mapping genetic resistance in 'Davis, ' 'Peking, ' PI 594774, PI 5944891, and other plant introductions (PI) have all relied on classifying plants or families as susceptible or resistant based on the presence or absence of lesions, respectively [10][11][12][13]. Quantitative methods of phenotyping have been developed as well, including ordinal scales and sets of standard area diagrams [14,15], but have been used less frequently for genetic mapping research. Standard area diagrams offer increased precision over qualitative assessments, but accuracy may still be impacted by rater experience, the number of diagrams, and the quality of diagrams [16].…”

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confidence: 99%

Automated, image-based disease measurement for phenotyping resistance to soybean frogeye leaf spot

McDonald

Buck

2022

Plant Methods

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Background Frogeye leaf spot is a disease of soybean, and there are limited sources of crop genetic resistance. Accurate quantification of resistance is necessary for the discovery of novel resistance sources, which can be accelerated by using a low-cost and easy-to-use image analysis system to phenotype the disease. The objective herein was to develop an automated image analysis phenotyping pipeline to measure and count frogeye leaf spot lesions on soybean leaves with high precision and resolution while ensuring data integrity. Results The image analysis program developed measures two traits: the percent of diseased leaf area and the number of lesions on a leaf. Percent of diseased leaf area is calculated by dividing the number of diseased pixels by the total number of leaf pixels, which are segmented through a series of color space transformations and pixel value thresholding. Lesion number is determined by counting the number of objects remaining in the image when the lesions are segmented. Automated measurement of the percent of diseased leaf area deviates from the manually measured value by less than 0.05% on average. Automatic lesion counting deviates by an average of 1.6 lesions from the manually counted value. The proposed method is highly correlated with a conventional method using a 1–5 ordinal scale based on a standard area diagram. Input image compression was optimal at a resolution of 1500 × 1000 pixels. At this resolution, the image analysis method proposed can process an image in less than 10 s and is highly concordant with uncompressed images. Conclusion Image analysis provides improved resolution over conventional methods of frogeye leaf spot disease phenotyping. This method can improve the precision and resolution of phenotyping frogeye leaf spot, which can be used in genetic mapping to identify QTLs for crop genetic resistance and in breeding efforts for resistance to the disease.

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“…Disease severity was visually assessed as percent leaf area exhibiting FLS symptoms at the plot level, 4 weeks post-fungicide application (approximately at the R6 growth stage; full seed) focusing specifically on the upper third of the canopy. A standard area diagram, when needed, was used as reference during the ratings (Price et al, 2016). Yield was obtained by harvesting the 2 central rows of each plot after full maturity (R8) with a small plot combine.…”

Section: Data Source and Criteria For Inclusion Of Trials In The Anal...mentioning

confidence: 99%

“…Disease severity was visually assessed as percent leaf area exhibiting FLS symptoms at the plot level, 4 weeks post-fungicide application (approximately at the full seed developmental stage [R6]) focusing specifically on the upper third of the canopy. The aid of a standard leaf area diagram as a reference was used while rating (Price et al, 2016). Yield was obtained by harvesting the middle two rows of each plot after full maturity (R8 developmental stage) with a small plot combine equipped with a grain gauge that measured total harvested grain weight and seed moisture.…”

Section: Data Source and Criteria For Fungicide Selectionmentioning

confidence: 99%

“…The intensity of FLS is usually quantified as severity, or the proportion of tissue area affected by the disease (Bock et al, 2022). A standard area diagram also has been developed to aid and standardize visual assessment of FLS severity in soybean (Price et al, 2016). The study of yield losses has been performed through the understanding of the relationships between disease intensity (in this case, severity) and yield from field experiments, where disease levels are assessed and the corresponding yield measured (Savary et al, 2006).…”

Section: Introductionmentioning

confidence: 99%

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Epidemiology and management of frogeye leaf spot of soybean: damage thresholds, efficacy and profitability of foliar fungicides

Barro¹

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Frogeye leaf spot (FLS), caused by Cercospora sojina, is an economically important disease of soybean in many parts of the world where soybean is grown, including the United States. A meta-analytic approach was used to summarize a data set of 66 uniform field research trials conducted to evaluate fungicide efficacy against FLS on soybean. The dataset spanned 10 years (2012 to 2021) of experiments conducted across eight states in the U.S., including Alabama, Arkansas, Illinois, Iowa, Kentucky, Louisiana, Mississippi and Tennessee. First, the relationship between FLS severity and soybean yield was investigated. A significant negative slope obtained through random effects meta-analytic models confirmed the negative linear relationship between FLS severity and soybean yield. Additionally, the overall relative damage coefficient was calculated to be 0.51%. In addition, economic damage thresholds were estimated by using the damage coefficient, for a range of soybean prices and control costs, taking into account three different fungicide efficacies (25%, 50% and 75%). The threshold values increased as the control efficacy also increased and were affected by different crop prices and fungicide costs. Second, the best fungicide options to control the disease were investigated. The results demonstrated that fungicide efficacy against FLS differ among active ingredients and is decreasing over time possibly due to fungicide resistant populations (mainly to the quinone outside inhibitors [QoIs]). The best performing fungicide reported in this study was a mixture of difenoconazole + pydiflumetofen, and the poorest performing fungicide was pyraclostrobin, a QoI fungicide. A statistically significant (P<0.05) decline in performance was detected for two fungicide mixtures (azoxystrobin + difenoconazole and thiophanate-methyl + tebuconazole) and two single active ingredients (pyraclostrobin and tetraconazole). Greater yields in trials with conditions favorable for severe epidemics were found, which could be explained by the more evident effect of the fungicides among the treated plots when compared to the nontreated control. Accordingly, the most effective treatments were more likely to be profitable under higher disease pressure and, as expected, the less effective treatment reported the higher risk of not offsetting the costs. Third, the profitability of applying fungicides was investigated in the absence or very low levels of FLS in double-crop soybean by using a different data set of 22 fungicide trials conducted between 2008 and 2021 across five states in the U.S. (Illinois, Indiana, Kentucky, Missouri, and Tennessee). The results showed no significant difference in yield response between the fungicide treatments and the nontreated control. Economic analyses indicated that, due to the lower yield responses, probabilities of breaking even were less than 50% for all the single fungicide classes, or up to 51% for mixtures, depending on fungicide cost and soybean price values. Overall, these research findings may provide useful information for regional risk assessment of potential yield loss caused by FLS, and for planning fungicide programs to control this important foliar disease. Decisions on fungicide planning must take into account, not only technical information such as control efficacy and yield return, but also profitability and strategies to mitigate fungicide resistance issues. Keywords: Cercospora sojina. Chemical control. Economic risk. Fungicide. Glycine max. Yield loss.

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A Quantifiable Disease Severity Rating Scale for Frogeye Leaf Spot of Soybean

Cited by 8 publications

References 2 publications

A diagramatic scale to quantify severity of antracnose in Rubus glaucus Benth

A diagramatic scale to quantify severity of antracnose in Rubus glaucus Benth

Automated, image-based disease measurement for phenotyping resistance to soybean frogeye leaf spot

Epidemiology and management of frogeye leaf spot of soybean: damage thresholds, efficacy and profitability of foliar fungicides

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