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. 2014 Jul 8:10:23.
doi: 10.1186/1746-4811-10-23. eCollection 2014.

GrainScan: a low cost, fast method for grain size and colour measurements

Alex P Whan  1 , Alison B Smith  2 , Colin R Cavanagh  1 , Jean-Philippe F Ral  1 , Lindsay M Shaw  1 , Crispin A Howitt  1 , Leanne Bischof  3
Affiliations

GrainScan: a low cost, fast method for grain size and colour measurements

Alex P Whan et al. Plant Methods. .

Abstract

Background: Measuring grain characteristics is an integral component of cereal breeding and research into genetic control of seed development. Measures such as thousand grain weight are fast, but do not give an indication of variation within a sample. Other methods exist for detailed analysis of grain size, but are generally costly and very low throughput. Grain colour analysis is generally difficult to perform with accuracy, and existing methods are expensive and involved.

Results: We have developed a software method to measure grain size and colour from images captured with consumer level flatbed scanners, in a robust, standardised way. The accuracy and precision of the method have been demonstrated through screening wheat and Brachypodium distachyon populations for variation in size and colour.

Conclusion: By using GrainScan, cheap and fast measurement of grain colour and size will enable plant research programs to gain deeper understanding of material, where limited or no information is currently available.

Keywords: Brachypodium distachyon; Image analysis; Seed colour; Seed size; Wheat.

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Figures

Figure 1
Figure 1
Density distributions of grain area for six randomly chosen samples of wheat grain. The mean and confidence interval, along with the number of seeds included in each scan is noted on each panel.
Figure 2
Figure 2
Correleation of BLUPs for size traits. Pairwise plot of BLUPs of packet effects (above the diagonal) and REML estimates of correlations between packet effects (below the diagonal) for size traits from GrainScan, SmartGrain and SeedCount. Method labels are on the diagonal: SC (SeedCount), SG (SmartGrain) and GS (GrainScan).
Figure 3
Figure 3
Correlation of BLUPs for colour traits. Pairwise plot of BLUPs of packet effects (above the diagonal) and REML estimates of correlations between packet effects (below the diagonal) for colour traits from GrainScan, SmartGrain and SeedCount. Panels represent each colour trait (L*, a* and b*) as labelled. Labels for each method are on the diagonal of each panel: SC (SeedCount), Min (Minolta Colorimeter), GSncd (GrainScan - only those grains where no crease was detected), GSwc (GrainScan – only the non-crease areas of seeds where a crease was detected) and GS (total grain area of all seeds detected by GrainScan).
Figure 4
Figure 4
Density distributions of grain area for six randomly chosen samples of Brachypodium. The mean and confidence interval, along with the number of seeds included in each scan is noted on each panel.
Figure 5
Figure 5
Examples of GrainScan input and output. Panel A: Scanned wheat grain for GrainScan input. Panel B: GrainScan output highlighting segmented grains as determined by the software. Different colours indicate different grains. Panel C: Optional crease detection output highlighting regions identified as grain crease.
See this image and copyright information in PMC

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