Methods of Assessing Fusarium Damage to Wheat Kernels

Abstract

Fusarium Head Blight (FHB), or head scab, primarily caused by Fusarium graminearum Schw., is a destructive disease of wheat (Triticum aestivum L.). It has reemerged worldwide as a disease of economic importance. Damage produced by the fungus includes: reduction of yield, mycotoxin contamination (DON), discolored, shriveled “tombstone” kernels and reduction in seed quality. The disease also reduces the test weight and lowers the market grade. Thus, there is great interest among breeders in selecting for resistance to both traits of DON and fusarium damaged kernel (FDK). This study was conducted to determine the effect of mass selection for FHB resistance using an image-based optical sorter compared to other methods. Fusariun damaged kernel (FDK) percentage on a count basis is more accurate than the weight basis, which was obvious in the visual estimate method although they were highly correlated. Visually adjusting the scabby portion (output of air separation machine) increases the accuracy of FDK percentage. Moderate correlations existed between FDK and DON measured by using traditional methods (FDK on basis of visual estimate, FDK on basis of air-separation, traditional methods of DON measurement), NIR, and image-based optical sorter. DON was correlated better with FDK measured by the image-based optical sorter than FDK measured by the air
 separation machine, NIR, or a visual estimate. The image-based optical sorter is easier to run, not time
 consuming compared to other methods since the speed of sorting can be adjusted according to the user, and its ability to detect variation among the populations at 0.05 level of significance. Over the twenty populations tested, the data suggest that the image-based optical sorter effectively provides a better way to assess FDK and DON. This method could accelerate FDK and DON assessment, and can be a great tool for breeding programs to assess and select for low FDK and DON.

Evaluation of Efficacy of Fungicides for Control of Wheat Fusarium Head Blight Based on Digital Imaging

“…F-measure was used to balance Precision and Recall [30]. These metrics were calculated as follows: (10) where TP represents the pixel area correctly predicted as wheat ear; TN represents the pixel area correctly predicted as background; FP is the pixel area predicted as wheat ear, but actually these pixels belong to the background. FN is the pixel area predicted as background, but actually these pixels belong to wheat ears.…”

Evaluation of Methods for Measuring Fusarium-Damaged Kernels of Wheat

“…Type I and Type II resistances can be assessed by a visual evaluation in the field, which provides advantages in terms of ease and quickness compared with other resistance measurements. While incidence and severity have been effective criteria for evaluating FHB resistance, Khaeim et al (2019) points out limitations of using field ratings to evaluate Type I and II resistance: (1) incidence and severity do not always reliably correlate with accumulated toxin levels; (2) optimum timing for rating varies by genotype and environmental conditions; (3) accurate rating requires a random sample, but this is difficult to accomplish or verify in the field; and (4) ratings are likely to differ between personnel if not all done by one individual [11].…”

Phenotyping Fusarium head blight through seed morphology characteristics using RGB imaging

“…FDK is quantified traditionally by estimating the amount of visibly damaged kernels, which appear smaller, shriveled, and in a range of colors from pale pink to brown ( Delwiche et al., 2010 ), according to a predetermined scale for visual assessments or by employing manual tools ( Ackerman et al., 2022 ). Comparisons between both types of resistance (resistance types II and III) have revealed that it would be more efficient and consistent to estimate FHB than the degree of colonization on the spike ( Agostinelli, 2009 ; Balut et al., 2013 ; Khaeim et al., 2019 ; Ackerman et al., 2022 ). However, screening by either manual or visual assessments is a labor- and time-consuming process for rating genotypes, is biased due to the subjectivity of visual assessments, and has low reproducibility among experiments ( Barbedo et al., 2015 ; Khaeim et al., 2019 ).…”