Digital Image Analysis Method for Estimation of Fusarium‐Damaged Kernels in Wheat

Abstract

Fusarium head blight (FHB) of wheat (Triticum aestivum L.), or head scab, causes a reduction in grain yield and quality through the formation of shriveled, dull‐grey seeds called “tombstones” or Fusarium‐damaged kernels (FDK). Fusarium‐damaged kernels are commonly quantified on a percentage basis by visually separating damaged from healthy kernels following harvest, in a process that is both time consuming and labor intensive. The objective of this study was to evaluate an alternative method for quantifying FDK through the use of the digital image analysis program ImageJ. The ‘NC‐Neuse’ × ‘AGS 2000’ F5–derived recombinant inbred population of 172 lines and the NC‐Neuse בBess’ double haploid population of 112 lines were used in this study. NC‐Neuse and Bess were moderately resistant and AGS 2000 was susceptible to FHB. The populations were evaluated under moderate to heavy FHB epidemics in a total of five environments in North Carolina, Maryland, and Missouri with two to three replications per environment during the 2010 to 2011 and 2011 to 2012 seasons. Following hand harvest and grain processing, FDK was estimated by (i) visual separation of diseased kernels and (ii) digital image analysis using ImageJ on captured images of grain samples. The correlation between the two methods ranged from 0.72 to 0.80 over five environments. A lower correlation was observed in one environment due to cracked and broken kernels in the samples. Digital image analysis was three times faster than the visual method and estimated FDK on a larger scale per plot sample, whereas labor and time constraints limited the sample size for the visual method. Digital image analysis was consistent over samples and appears well suited as an alternative form to estimate percent of FDK in grain that is not damaged in other ways.

Breeding strategies and advances in line selection for Fusarium head blight resistance in wheat

“…Apart from scorings in the field, post-harvest kernel samples can also be analyzed for disease symptoms. Usually this is done either by visual scoring or digital image analysis (Maloney et al 2014) of the percentage of diseased grains in harvested samples or by measuring yield (or yield components) relative to non-inoculated controls (Dill-Macky 2003). A low mycotoxin concentration in the harvested kernels is most important, but measuring mycotoxin content is both time-consuming and expensive.…”

Tamaño y número de granos de trigo analizados mediante procesamiento de imagen digital

“…The reduction of the mean relative error between the number of grains observed and those calculated as the sample size increases indicates that the number of under and overestimated grains is maintained, according to Maloney et al (2014), accuracy increases to a larger sample size and indicate that a limiting factor is the amount of grains that can be captured in an image, which depends on the digital camera and its configuration, on the capacity of the container where the samples are placed. This amount also depends on the size of the grain, in the observations of this research with the flatbed scanner used, about 2 000 grains fit randomly distributed without touching the edges of the glass.…”

“…La reducción del error relativo medio entre el número de granos observados y los calculados conforme se incrementa el tamaño de la muestra indica que el número de granos sub y sobrestimados se mantiene, de acuerdo con Maloney et al (2014), la exactitud se incrementa a mayor tamaño de la muestra e indican que una limitante es la cantidad de grano que se puede capturar en una imagen, la cual depende de la cámara digital y su configuración, de la capacidad del contenedor o recipiente dónde se coloquen las muestras. Dicha cantidad también depende del tamaño del grano, en las observaciones de este estudio con el escáner de cama plana utilizado caben alrededor de 2 000 granos distribuidos al azar, sin tocar los bordes del cristal.…”

“…El tamaño es componente significativo del rendimiento (Tanabata et al, 2012;Williams y Sorrells, 2014) y contribuye en el vigor de germinación (Whan et al, 2014); asimismo, se relaciona con la calidad de molienda (Williams et al, 2013). La cuantificación manual generalmente es exacta, pero requiere mano de obra, es tediosa y debido al tiempo que requiere se utilizan muestras limitadas (Maloney et al, 2014).…”

“…Size is a significant component of yield (Tanabata et al, 2012;Williams and Sorrells, 2014) and contributes to germination vigor (Whan et al, 2014); also, it is related to grinding quality (Williams et al, 2013). Manual quantification is usually accurate, but labor-intensive, tedious, and due to the time required, limited samples are used (Maloney et al, 2014).…”