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

Plant Methods

Leanne Bischof¹  Crispin A Howitt³  Lindsay M Shaw³  Jean-Philippe F Ral³  Colin R Cavanagh³  Alison B Smith²  Alex P Whan³ 

[1] CSIRO Computational Informatics, North Ryde NSW 2113, Australia;National Institute for Applied Statistics and Research Australia, Univeristy of Wollongong Wollongong NSW 2522, Australia;CSIRO Plant Industry, GPO Box 1600, Canberra ACT 2601, Australia

关键词: Image analysis;    Seed colour;    Seed size;    Brachypodium distachyon;    Wheat;   

Others  :  1151861
DOI  :  10.1186/1746-4811-10-23

 received in 2014-04-29, accepted in 2014-06-29,  发布年份 2014

【 摘 要 】

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.

【 授权许可】

   
2014 Whan et al.; licensee BioMed Central Ltd.

【 图 表 】

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【 参考文献 】

• [1]Lafond GP, Baker RJ: Effects of genotype and seed size on speed of emergence and seedling vigor in nine spring wheat cultivars1. Crop Sci 1986, 26:341.
• [2]Demirlicakmak A, Kaufmann ML, Johnson LPV: The influence of seed size and seeding rate on yield and yield components of barley. Can J Plant Sci 1963, 43:330-337.
• [3]Berman M, Bason ML, Ellison F, Peden G, Wrigley CW: Image analysis of whole grains to screen for flour-milling yield in wheat breeding. Cereal Chem 1996, 73:323-327.
• [4]Novaro P, Colucci F, Venora G, D’Egidio MG: Image analysis of whole grains: a noninvasive method to predict semolina yield in durum wheat. Cereal Chem 2001, 78:217-221.
• [5]Marshall DR, Mares DJ, Moss HJ, Ellison FW: Effects of grain shape and size on milling yields in wheat .2. experimental studies. Aust J Agric Res 1986, 37:331-342.
• [6]Anderson JA, Sorrells ME, Tanksley SD: RFLP analysis of genomic regions associated with resistance to preharvest sprouting in wheat. Crop Sci 1993, 33:453-459.
• [7]Gu X-Y, Kianian SF, Foley ME: Multiple loci and epistases control genetic variation for seed dormancy in weedy rice (Oryza sativa). Genetics 2004, 166:1503-1516.
• [8]Ramya P, Chaubal A, Kulkarni K, Gupta L, Kadoo N, Dhaliwal HS, Chhuneja P, Lagu M, Gupt V: QTL mapping of 1000-kernel weight, kernel length, and kernel width in bread wheat (Triticum aestivum L.). J Appl Genet 2010, 51:421-429.
• [9]Martin CR, Rousser R, Brabec DL: Development of a single-kernel wheat characterization system. Trans ASAE 1993, 36(5):1399-1404.
• [10]Nilsson-Ehle H: Zur Kenntnis der mit der keimungsphysiologie des weizens in zusammenhang stehenden inneren faktoren. Z Für Planzenzüctung 1914, 2:153-187.
• [11]Groos C, Gay G, Perretant M-R, Gervais L, Bernard M, Dedryver F, Charmet G: Study of the relationship between pre-harvest sprouting and grain color by quantitative trait loci analysis in a white × red grain bread-wheat cross. Theor Appl Genet 2002, 104:39-47.
• [12]Nelson JC, Deynze AEV, Sorrells ME, Autrique E, Lu YH, Negre S, Bernard M, Leroy P: Molecular mapping of wheat: homoeologous group 3. Genome 1995, 38:525-533.
• [13]Himi E, Noda K: Red grain colour gene (R) of wheat is a Myb-type transcription factor. Euphytica 2005, 143:239-242.
• [14]Mares D, Mrva K, Cheong J, Williams K, Watson B, Storlie E, Sutherland M, Zou Y: A QTL located on chromosome 4A associated with dormancy in white- and red-grained wheats of diverse origin. Theor Appl Genet 2005, 111:1357-1364.
• [15]Liu S, Bai G, Cai S, Chen C: Dissection of genetic components of preharvest sprouting resistance in white wheat. Mol Breed 2011, 27:511-523.
• [16]Peterson CJ, Shelton DR, Martin TJ, Sears RG, Williams E, Graybosch RA: Grain color stability and classification of hard white wheat in the US. Euphytica 2001, 119:101-106.
• [17]Lamkin WM, Miller BS: Note on the use of sodium-hydroxide to distinguish red wheats from white common, club, and durum cultivars. Cereal Chem 1980, 57:293-294.
• [18]Humphries JM, Graham RD, Mares DJ: Application of reflectance colour measurement to the estimation of carotene and lutein content in wheat and triticale. J Cereal Sci 2004, 40:151-159.
• [19]Furbank RT, Tester M: Phenomics - technologies to relieve the phenotyping bottleneck. Trends Plant Sci 2011, 16:635-644.
• [20]White JW, Andrade-Sanchez P, Gore MA, Bronson KF, Coffelt TA, Conley MM, Feldmann KA, French AN, Heun JT, Hunsaker DJ, Jenks MA, Kimball BA, Roth RL, Strand RJ, Thorp KR, Wall GW, Wang GY: Field-based phenomics for plant genetics research. Field Crops Res 2012, 133:101-112.
• [21]Burton AL, Williams M, Lynch JP, Brown KM: RootScan: Software for high-throughput analysis of root anatomical traits. Plant Soil 2012, 357:189-203.
• [22]Gonzalo MJ, Brewer MT, Anderson C, Sullivan D, Gray S, van der Knaap E: Tomato fruit shape analysis using morphometric and morphology attributes implemented in Tomato Analyzer software program. J Am Soc Hortic Sci 2009, 134:77-87.
• [23]Green JM, Appel H, Rehrig EM, Harnsomburana J, Chang J-F, Balint-Kurti P, Shyu C-R: PhenoPhyte: a flexible affordable method to quantify 2D phenotypes from imagery. Plant Methods 2012, 8:45. BioMed Central Full Text
• [24]Abramoff MD, Magalhães PJ, Ram SJ: Image processing with ImageJ. Biophotonics Int 2004, 11:36-42.
• [25]Li D, Wang L, Wang M, Xu Y-Y, Luo W, Liu Y-J, Xu Z-H, Li J, Chong K: Engineering OsBAK1 gene as a molecular tool to improve rice architecture for high yield. Plant Biotechnol J 2009, 7:791-806.
• [26]Igathinathane C, Pordesimo LO, Batchelor WD: Major orthogonal dimensions measurement of food grains by machine vision using ImageJ. Food Res Int 2009, 42:76-84.
• [27]Breseghello F, Sorrells ME: QTL analysis of kernel size and shape in two hexaploid wheat mapping populations. Field Crops Res 2007, 101:172-179.
• [28]Herridge RP, Day RC, Baldwin S, Macknight RC: Rapid analysis of seed size in Arabidopsis for mutant and QTL discovery. Plant Methods 2011, 7:11. BioMed Central Full Text
• [29]Tanabata T, Shibaya T, Hori K, Ebana K, Yano M: SmartGrain: high-throughput phenotyping software for measuring seed shape through image analysis. Plant Physiol 2012, 160:1871-1880.
• [30]Iwata H, Ukai Y: SHAPE: a computer program package for quantitative evaluation of biological shapes based on elliptic fourier descriptors. J Hered 2002, 93:384-385.
• [31]Williams K, Munkvold J, Sorrells M: Comparison of digital image analysis using elliptic Fourier descriptors and major dimensions to phenotype seed shape in hexaploid wheat (Triticum aestivum L.). Euphytica 2013, 190:99-116.
• [32]Whan A, Bolger M, Bischof L: GrainScan - Software for analysis of grain images. 10.4225/08/536302C43FC28
• [33]Whan A, Cavanagh C: Scanned wheat grain images. 10.4225/08/52F9AE7262532
• [34]Salembier Clairon PJ, Wilkinson M: Connected operators: A review of region-based morphological image processing techniques. IEEE Signal Process. Mag 2009, 26:136-157.
• [35]Sintorn I-M, Bischof L, Jackway P, Haggarty S, Buckley M: Gradient based intensity normalization. J Microsc 2010, 240:249-258.
• [36]Cullis BR, Smith AB, Coombes NE: On the design of early generation variety trials with correlated data. J Agric Biol Environ Stat 2006, 11:381-393.
• [37]Butler D, Cullis B, Gilmour A, Gogel BJ: ASReml-R Reference Manual. Brisbane: Queensland Department of Primary Industries and Fisheries; 2007.
• [38]R Core Team: R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing; 2013.
• [39]Smith A, Cullis B, Thompson R: Analyzing variety by environment data using multiplicative mixed models and adjustments for spatial field trend. Biometrics 2001, 57:1138-1147.