Unknown

Dataset Information

0

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


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.

SUBMITTER: Whan AP 

PROVIDER: S-EPMC4105244 | biostudies-other | 2014

REPOSITORIES: biostudies-other

altmetric image

Publications

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

Whan Alex P AP   Smith Alison B AB   Cavanagh Colin R CR   Ral Jean-Philippe F JP   Shaw Lindsay M LM   Howitt Crispin A CA   Bischof Leanne L  

Plant methods 20140708


<h4>Background</h4>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.<h4>Results</h4  ...[more]

Similar Datasets

| S-EPMC10825635 | biostudies-literature
| S-EPMC7587348 | biostudies-literature
| S-EPMC4961984 | biostudies-other
| S-EPMC6806460 | biostudies-literature
| S-EPMC7959643 | biostudies-literature
| S-EPMC11325429 | biostudies-literature
| S-EPMC3715229 | biostudies-literature
| S-EPMC6111670 | biostudies-literature
2023-06-06 | GSE213984 | GEO
| S-EPMC8285579 | biostudies-literature