Skip to main content
SpringerLink
Log in

Seed Phenomics

  • Chapter
  • First Online:
Phenomics

Abstract

Plant seeds present complex phenotypes that can be difficult to assess quantitatively. The burgeoning field of phenomics seeks to describe phenotypes in high-throughput and with quantitative descriptors that allow computational methods for analysis. This chapter summarizes technology platforms for collecting information-rich seed phenotypes that can also be scaled to high-throughput. Seed phenotypes can be assessed using imaging, spectroscopy, transcriptomes, proteomes, metabolomes, and ionomes. We focus on how these technologies have been developed and applied to maize seeds to define genotype-phenotype relationships.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 149.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 199.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  • Alexander DE (1982) The use of wide-line NMR in breeding high-oil corn. J Am Oil Chem Soc 59:A284

    Article  Google Scholar 

  • Alexander DE, Silvela L, Collins FI, Rodgers RC (1967) Analysis of oil content of maize by wide-line NMR. J Am Oil Chem Soc 44:555–558

    Article  CAS  PubMed  Google Scholar 

  • Anttonen MJ, Lehesranta S, Auriola S, Röhlig RM, Engel KH, Kärenlampi SO (2010) Genetic and environmental influence on maize kernel proteome. J Proteome Res 9:6160–6168

    Article  CAS  PubMed  Google Scholar 

  • Armstrong PR (2006) Rapid single-kernel NIR measurement of grain and oil-seed attributes. Appl Eng Agric 22:767–772

    Article  Google Scholar 

  • Armstrong PR, Tallada JG (2012) Prediction of kernel density of corn using single-kernel near infrared spectroscopy. Appl Eng Agric 28:569–574

    Article  Google Scholar 

  • Azmach G, Gedil M, Menkir A, Spillane C (2013) Marker-trait association analysis of functional gene markers for provitamin A levels across diverse tropical yellow maize inbred lines. BMC Plant Biol 13:227

    Article  PubMed Central  PubMed  Google Scholar 

  • Baxter IR, Gustin JL, Settles AM, Hoekenga OA (2013) Ionomic characterization of maize kernels in the intermated B73xMo17 population. Crop Sci 53:208–220

    Article  CAS  Google Scholar 

  • Baxter IR, Ziegler G, Lahner B, Mickelbart MV, Foley R, Danku J, Armstrong P, Salt DE, Hoekenga OA (2014) Single-kernel ionomic profiles are highly heritable indicators of genetic and environmental influences on elemental accumulation in maize grain (Zea mays). PLoS ONE 9:e87628

    Article  PubMed Central  PubMed  Google Scholar 

  • Baye TM, Pearson TC, Settles AM (2006) Development of a calibration to predict maize seed composition using single kernel near infrared spectroscopy. J Cereal Sci 43:236–243

    Article  CAS  Google Scholar 

  • Berardo N, Mazzinelli G, Valoti P, Lagana P, Redaelli R (2009) Characterization of maize germplasm for the chemical composition of the grain. J Agric Food Chem 57:2378–2384

    Article  CAS  PubMed  Google Scholar 

  • Brenna OV, Berardo N (2004) Application of near-infrared reflectance spectroscopy (NIRS) to the evaluation of carotenoids content in maize. J Agric Food Chem 52:5577–5582

    Article  CAS  PubMed  Google Scholar 

  • Cakmak I, Pfeiffer W, McClafferty B (2010) Biofortification of durum wheat with zinc and iron. Cereal Chem 87:10–20

    Article  CAS  Google Scholar 

  • Campbell KG, Bergman CJ, Gualberto DG, Anderson JA, Giroux MJ, Hareland G, Fulcher RG, Sorrells ME, Finney PL (1999) Quantitative trait loci associated with kernel traits in a soft x hard wheat cross. Crop Sci 39:1184–1195

    Article  CAS  Google Scholar 

  • Campbell MR, Brumm TJ, Glover DV (1997) Whole grain amylose analysis in maize using near-infrared transmittance spectroscopy. Cereal Chem 74:300–303

    Article  CAS  Google Scholar 

  • Campbell MR, Yeager H, Abdubek N, Pollak LM, Glover DV (2002) Comparison of methods for amylose screening among amylose-extender (ae) maize starches from exotic backgrounds. Cereal Chem 79:317–321

    Article  CAS  Google Scholar 

  • Chary KV, Govil G (2008) NMR in biological systems: from molecules to humans. In: Kapein R (ed) Series: focus on structural biology, vol 6. Dordrecht, The Netherlands

    Google Scholar 

  • Cogdill RP, Hurburgh CR, Rippke GR (2004) Single-kernel maize analysis by near-infrared hyperspectral imaging. Trans ASAE 47:311–320

    Article  Google Scholar 

  • Conway TF, Earle FR (1963) Nuclear magnetic resonance for determining oil content of seeds. J Am Oil Chem Soc 40:265–268

    Article  CAS  Google Scholar 

  • Cook JP, McMullen MD, Holland JB, Tian F, Bradbury P, Ross-Ibarra J, Buckler ES, Flint-Garcia SA (2012) Genetic architecture of maize kernel composition in the nested association mapping and inbred association panels. Plant Physiol 158:824–834

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  • Correa CES, Shaver RD, Pereira MN, Lauer JG, Kohn K (2002) Relationship between corn vitreousness and ruminal in situ starch degradability. J Dairy Sci 85:3008–3012

    Article  CAS  PubMed  Google Scholar 

  • Dhondt S, Wuyts N, Inzé D (2013) Cell to whole-plant phenotyping: the best is yet to come. Trends Plant Sci 18:428–439

    Article  CAS  PubMed  Google Scholar 

  • Domínguez F, Cejudo FJ (2014) Programmed cell death (PCD): an essential process of cereal seed development and germination. Front Plant Sci 5:366

    PubMed Central  PubMed  Google Scholar 

  • Dowell FE, Pearson TC, Maghirang EB, Xie F, Wicklow DT (2002) Reflectance and transmittance spectroscopy applied to detecting fumonisin in single corn kernels infected with Fusarium verticillioides. Cereal Chem 79:222–226

    Article  CAS  Google Scholar 

  • Erasmus C, Taylor JRN (2004) Optimising the determination of maize endosperm vitreousness by a rapid non-destructive image analysis technique. J Sci Food Agric 84:920–930

    Article  CAS  Google Scholar 

  • Evers AD, Cox RI, Shaheedullah MZ, Withey RP (1990) Predicting milling extraction rate by image analysis of wheat grains. Asp Appl Biol 25:417–426

    Google Scholar 

  • Fernie AR, Schauer N (2009) Metabolomics-assisted breeding: a viable option for crop improvement? Trends Genet 25:39–48

    Article  CAS  PubMed  Google Scholar 

  • Finney EE, Norris KH (1978) Determination of moisture in corn kernels by near-infrared transmittance measurements. Trans ASAE 21:581–584

    Article  Google Scholar 

  • Fontaine J, Schirmer B, Horr J (2002) Near-infrared reflectance spectroscopy (NIRS) enables the fast and accurate prediction of essential amino acid contents. J Agric Food Chem 50:3902–3911

    Article  CAS  PubMed  Google Scholar 

  • Fox G, Manley M (2009) Hardness methods for testing maize kernels. J Agric Food Chem 57:5647–5657

    Article  CAS  PubMed  Google Scholar 

  • Gault CM, Settles AM (2014) Functional genomics. In: Wusirika R, Bohn M, Lai J (eds) Genetics, genomics, and breeding of maize. CRC Press, Boca Raton, pp 131–154

    Google Scholar 

  • Gustin JL, Settles AM (2013) Machine vision for seed phenomics. In: Becraft PW (ed) Seed genomics. Wiley, Hoboken, pp 237–251

    Chapter  Google Scholar 

  • Gwirtz JA, Garcia-Casal MN (2014) Processing maize flour and corn meal food products. Ann NY Acad Sci 1312:66–75

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  • Harjes CE, Rocheford TR, Bai L, Brutnell TP, Kandianis CB, Sowinski SG, Stapleton AE, Vallabhaneni R, Williams M, Wurtzel ET, Yan J, Buckler ES (2008) Natural genetic variation in lycopene epsilon cyclase tapped for maize biofortification. Science 319:330–333

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  • Holding DR, Hunter BG, Chung T, Gibbon BC, Ford CF, Bharti AK, Messing J, Hamaker BR, Larkins BA (2008) Genetic analysis of opaque2 modifier loci in quality protein maize. Theor Appl Genet 117:157–170

    Article  CAS  PubMed  Google Scholar 

  • Holding DR, Hunter BG, Klingler JP, Wu S, Guo X, Gibbon BC, Wu R, Schulze JM, Jung R, Larkins BA (2011) Characterization of opaque2 modifier QTLs and candidate genes in recombinant inbred lines derived from the K0326Y quality protein maize inbred. Theor Appl Genet 122:783–794

    Article  CAS  PubMed  Google Scholar 

  • Holloway B, Luck S, Beatty M, Rafalski JA, Li B (2011) Genome-wide expression quantitative trait loci (eQTL) analysis in maize. BMC Genom 12:336

    Article  Google Scholar 

  • Houle D, Govindaraju DR, Omholt S (2010) Phenomics: the next challenge. Nat Rev Genet 11:855–866

    Article  CAS  PubMed  Google Scholar 

  • Huang X, Han B (2014) Natural variations and genome-wide association studies in crop plants. Annu Rev Plant Biol 65:531–551

    Article  CAS  PubMed  Google Scholar 

  • Iwai T, Takahashi M, Oda K, Terada Y, Yoshida KT (2012) Dynamic changes in the distribution of minerals in relation to phytic acid accumulation during rice seed development. Plant Physiol 160:2007–2014

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  • Janni J, Weinstock BA, Hagen L, Wright S (2008) Novel near-infrared sampling apparatus for single kernel analysis of oil content in maize. Appl Spectrosc 62:423–426

    Article  CAS  PubMed  Google Scholar 

  • Jin X, Fu Z, Ding D, Li W, Liu Z, Tang J (2013) Proteomic identification of genes associated with maize grain-filling rate. PLoS ONE 8:e59353

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  • Juliano BO, Villareal CP (1993) Grain quality evaluation of world rices. International Rice Research Institute, Manila

    Google Scholar 

  • Kiesselbach TA (1949) The structure and reproduction of corn. Nebr Agric Exp Stn Ann Rep 161:1–96

    Google Scholar 

  • Kirchberger S, Leroch M, Huynen MA, Wahl M, Neuhaus HE, Tjaden J (2007) Molecular and biochemical analysis of the plastidic ADP-glucose transporter (ZmBT1) from Zea mays. J Biol Chem 282:22481–22491

    Article  CAS  PubMed  Google Scholar 

  • Kotyk JJ, Pagel MD, Deppermann KL, Colletti RF, Hoffman NG, Yannakakis EJ, Das PK, Ackerman JJ (2005) High-throughput determination of oil content in corn kernels using nuclear magnetic resonance imaging. J Am Oil Chem Soc 82(855):862

    Google Scholar 

  • Li L, Petsch K, Shimizu R, Liu S, Xu WW, Ying K, Yu J, Scanlon MJ, Schnable PS, Timmermans MC, Springer NM, Muehlbauer GJ (2013) Mendelian and non-mendelian regulation of gene expression in maize. PLoS Genet 9:e1003202

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  • Liao K, Paulsen MR, Reid JF (1994) Real-time detection of colour and surface defects of maize kernels using machine vision. J Agric Eng Res 59:263–271

    Article  Google Scholar 

  • Marcon C, Schützenmeister A, Schütz W, Madlung J, Piepho HP, Hochholdinger F (2010) Nonadditive protein accumulation patterns in Maize (Zea mays L.) hybrids during embryo development. J Proteome Res 9(12):6511–6522

    Article  CAS  PubMed  Google Scholar 

  • Marion D (2013) An introduction to biological NMR spectroscopy. Mol Cell Proteomics 12:3006–3025

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  • Miura K, Ashikari M, Matsuoka M (2011) The role of QTLs in the breeding of high-yielding rice. Trends Plant Sci 16:319–326

    Article  CAS  PubMed  Google Scholar 

  • Ngonyamo-Majee D, Shaver RD, Coors JG, Sapienza D, Correa CES, Lauer JG, Berzaghi P (2008) Relationships between kernel vitreousness and dry matter degradability for diverse corn germplasm I. Development of near-infrared reflectance spectroscopy calibrations. Anim Feed Sci Tech 142:247–258

    Article  Google Scholar 

  • Norton GJ, Douglas A, Lahner B, Yakubova E, Guerinot ML, Pinson SRM, Tarpley L, Eizenga GC, McGrath SP, Zhao FJ, Islam MR, Islam S, Duan G, Zhu Y, Salt DE, Meharg AA, Price AH (2014) Genome wide association mapping of grain arsenic, copper, molybdenum and zinc in rice (Oryza sativa L.) grown at four international field sites. PLoS ONE 9:e89685

    Article  PubMed Central  PubMed  Google Scholar 

  • Orman BA, Schumann RA (1991) Comparison of near-infrared spectroscopy calibration methods for the prediction of protein, oil, and starch in maize grain. J Agric Food Chem 39:883–886

    Article  CAS  Google Scholar 

  • Orman BA, Schumann RA (1992) Nondestructive single-kernel oil determination of maize by near-infrared transmission spectroscopy. J Am Oil Chem Soc 69:1036–1038

    Article  CAS  Google Scholar 

  • Ozturk L, Yazici MA, Yucel C, Torun A, Cekic C, Bagci A, Ozkan H, Braun HJ, Sayers Z, Cakmak I (2006) Concentration and localization of zinc during seed development and germination in wheat. Physiol Plant 128:144–152

    Article  CAS  Google Scholar 

  • Paulsen MR, Mbuvi SW, Haken AE, Ye B, Stewart RK (2003a) Extractable starch as a quality measurement of dried corn. Appl Eng Agric 19:211–217

    Article  Google Scholar 

  • Paulsen MR, Pordesimo LO, Singh M, Mbuvi SW, Ye BY (2003b) Maize starch yield calibrations with near infrared reflectance. Biosyst Eng 85:455–460

    Article  Google Scholar 

  • Paulsen MR, Singh M (2004) Calibration of a near-infrared transmission grain analyzer for extractable starch in maize. Biosyst Eng 89:79–83

    Article  Google Scholar 

  • Pearson TC, Wicklow DT, Brabec DL (2010) Characteristics and sorting of white food corn contaminated with mycotoxins. Appl Eng Agric 26:109–113

    Article  Google Scholar 

  • Pearson TC, Wicklow DT, Maghirang EB, Xie F, Dowell FE (2001) Detecting aflatoxin in single corn kernels by transmittance and reflectance spectroscopy. Trans ASAE 44:1247–1254

    Article  CAS  Google Scholar 

  • Pearson TC, Wicklow DT, Pasikatan MC (2004) Reduction of aflatoxin and fumonisin contamination in yellow corn by high-speed dual-wavelength sorting. Cereal Chem 81:490–498

    Article  CAS  Google Scholar 

  • Peng B, Li Y, Wang Y, Liu C, Liu Z, Tan W, Zhang Y, Wang D, Shi Y, Sun B, Song Y, Wang T, Li Y (2011) QTL analysis for yield components and kernel-related traits in maize across multi-environments. Theor Appl Genet 122:1305–1320

    Article  PubMed  Google Scholar 

  • Purugganan MD, Fuller DQ (2009) The nature of selection during plant domestication. Nature 457:843–848

    Article  CAS  PubMed  Google Scholar 

  • Ranum P, Peña-Rosas JP, Garcia-Casal MN (2014) Global maize production, utilization, and consumption. Ann NY Acad Sci 1312:105–112

    Article  PubMed  Google Scholar 

  • Robutti JL (1995) Maize kernel hardness estimation in breeding by near-infrared transmission analysis. Cereal Chem 72:632–636

    CAS  Google Scholar 

  • Römisch-Margl L, Spielbauer G, Schützenmeister A, Schwab W, Piepho HP, Genschel U, Gierl A (2010) Heterotic patterns of sugar and amino acid components in developing maize kernels. Theor Appl Genet 120:369–381

    Article  PubMed  Google Scholar 

  • Salt DE, Baxter I, Lahner B (2008) Ionomics and the study of the plant ionome. Annu Rev Plant Biol 59:709–733

    Article  CAS  PubMed  Google Scholar 

  • Sekhon RS, Hirsch CN, Childs KL, Breitzman MW, Kell P, Duvick S, Spalding EP, Buell CR, de Leon N, Kaeppler SM (2014) Phenotypic and transcriptional analysis of divergently selected maize populations reveals the role of developmental timing in seed size determination. Plant Physiol 165:658–669

    Google Scholar 

  • Shen M, Broeckling CD, Chu EY, Ziegler G, Baxter IR, Prenni JE, Hoekenga OA (2013) Leveraging non-targeted metabolite profiling via statistical genomics. PLoS ONE 8:e57667

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  • Shi C, Uzarowska A, Ouzunova M, Landbeck M, Wenzel G, Lübberstedt T (2007) Identification of candidate genes associated with cell wall digestibility and eQTL (expression quantitative trait loci) analysis in a Flint x Flint maize recombinant inbred line population. BMC Genom 8:22

    Article  Google Scholar 

  • Siesler HW (2008) Basic principles of vibrational spectroscopy. In: Burns DA, Ciurczak EW (eds) Handbook of near-infrared analysis. CRC Press, Boca Raton, Florida, pp 7–18

    Google Scholar 

  • Silva-Sanchez C, Chen S, Li J, Chourey PS (2014) A comparative glycoproteome study of developing endosperm in the hexose-deficient miniature1 (mn1) seed mutant and its wild type Mn1 in maize. Front Plant Sci 5:63

    Article  PubMed Central  PubMed  Google Scholar 

  • Song TM, Chen SJ (2004) Long term selection for oil concentration in five maize populations. Maydica 49:9–14

    Google Scholar 

  • Song XF, Song TM, Dai JR, Rocheford T, Li JS (2004) QTL mapping of kernel oil concentration with high-oil maize by SSR markers. Maydica 49:41–48

    Google Scholar 

  • Spielbauer G, Armstrong P, Baier JW, Allen WB, Richardson K, Shen B, Settles AM (2009) High-throughput near-infrared reflectance spectroscopy for predicting quantitative and qualitative composition phenotypes of individual maize kernels. Cereal Chem 86:556–564

    Article  CAS  Google Scholar 

  • Spielbauer G, Li L, Römisch-Margl L, Do PT, Fouquet R, Fernie AR, Eisenreich W, Gierl A, Settles AM (2013) Chloroplast-localized 6-phosphogluconate dehydrogenase is critical for maize endosperm starch accumulation. J Exp Bot 64:2231–2242

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  • Swanson-Wagner RA, DeCook R, Jia Y, Bancroft T, Ji T, Zhao X, Nettleton D, Schnable PS (2009) Paternal dominance of trans-eQTL influences gene expression patterns in maize hybrids. Science 326:1118–1120

    Article  CAS  PubMed  Google Scholar 

  • Takhar PS, Maier DE, Campanella OH, Chen G (2011) Hybrid mixture theory based moisture transport and stress development in corn kernels during drying: validation and simulation results. J Food Eng 106:275–282

    Article  Google Scholar 

  • Tallada JG, Palacios-Rojas N, Armstrong PR (2009) Prediction of maize seed attributes using a rapid single kernel near infrared instrument. J Cereal Sci 50:381–387

    Article  CAS  Google Scholar 

  • Tobias RB, Boyer CD, Shannon JC (1992) Alterations in carbohydrate intermediates in the endosperm of starch-deficient maize (Zea mays L.) genotypes. Plant Physiol 99:146–152

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  • Vallabhaneni R, Gallagher CE, Licciardello N, Cuttriss AJ, Quinlan RF, Wurtzel ET (2009) Metabolite sorting of a germplasm collection reveals the hydroxylase3 locus as a new target for maize provitamin A biofortification. Plant Physiol 151:1635–1645

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  • Walley JW, Shen Z, Sartor R, Wu KJ, Osborn J, Smith LG, Briggs SP (2013) Reconstruction of protein networks from an atlas of maize seed proteotypes. Proc Natl Acad Sci USA 110:E4808–E4817

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  • Weinstock BA, Janni J, Hagen L, Wright S (2006) Prediction of oil and oleic acid concentrations in individual corn (Zea mays L.) kernels using near-infrared reflectance hyperspectral imaging and multivariate analysis. Appl Spectrosc 60:9–16

    Article  CAS  PubMed  Google Scholar 

  • Wen W, Li D, Li X, Gao Y, Li W, Li H, Liu J, Liu H, Chen W, Luo J, Yan J (2014) Metabolome-based genome-wide association study of maize kernel leads to novel biochemical insights. Nat Commun 5:3438

    PubMed Central  PubMed  Google Scholar 

  • Yan J, Kandianis CB, Harjes CE, Bai L, Kim EH, Yang X, Skinner DJ, Fu Z, Mitchell S, Li Q, Fernandez MG, Zaharieva M, Babu R, Fu Y, Palacios N, Li J, Dellapenna D, Brutnell T, Buckler ES, Warburton ML, Rocheford T (2010) Rare genetic variation at Zea mays crtRB1 increases beta-carotene in maize grain. Nat Genet 42:322–327

    Article  CAS  PubMed  Google Scholar 

  • Yang XH, Guo YQ, Fu Y, Hu JY, Chai YC, Zhang YR, Li JS (2009) Measuring fatty acid concentration in maize grain by near-infrared reflectance spectroscopy. Spectrosc Spectral Anal 29:106–109

    Google Scholar 

  • Zhang M, Pinson SR, Tarpley L, Huang XY, Lahner B, Yakubova E, Baxter I, Guerinot ML, Salt DE (2014) Mapping and validation of quantitative trait loci associated with concentrations of 16 elements in unmilled rice grain. Theor Appl Genet 127:137–165

    Article  CAS  PubMed  Google Scholar 

  • Zheng P, Allen WB, Roesler K, Williams ME, Zhang S, Li J, Glassman K, Ranch J, Nubel D, Solawetz W, Bhattramakki D, Llaca V, Deschamps S, Zhong GY, Tarczynski MC, Shen B (2008) A phenylalanine in DGAT is a key determinant of oil content and composition in maize. Nat Genet 40:367–372

    Article  CAS  PubMed  Google Scholar 

  • Zolla L, Rinalducci S, Antonioli P, Righetti PG (2008) Proteomics as a complementary tool for identifying unintended side effects occurring in transgenic maize seeds as a result of genetic modifications. J Proteome Res 7:1850–1861

    Article  CAS  PubMed  Google Scholar 

Download references

Acknowledgments

We apologize to the authors of relevant research articles that were not highlighted in this chapter due to space constraints. The authors’ research on seed phenomics is supported by grants from the National Science Foundation (awards IOS-1031416 and MCB-1412218), the National Institute of Food and Agriculture (awards 2010-04228 and 2011-67013-30032), and the Vasil-Monsanto Endowment.

Author information

Authors and Affiliations

  1. Horticultural Sciences Department, University of Florida, Gainesville, FL, USA

    Jeffrey L. Gustin & A. Mark Settles

Authors
  1. Jeffrey L. Gustin
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. A. Mark Settles
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to A. Mark Settles .

Editor information

Editors and Affiliations

  1. Departamento de Genética, University of São Paulo, Piracicaba, São Paulo, Brazil

    Roberto Fritsche-Neto

  2. Departamento de Fitotecnia, Federal University of Viçosa, Viçosa, Minas Gerais, Brazil

    Aluízio Borém

Rights and permissions

Reprints and permissions

Copyright information

© 2015 Springer International Publishing Switzerland

About this chapter

Cite this chapter

Gustin, J.L., Settles, A.M. (2015). Seed Phenomics. In: Fritsche-Neto, R., Borém, A. (eds) Phenomics. Springer, Cham. https://doi.org/10.1007/978-3-319-13677-6_5

Download citation

Publish with us

Policies and ethics

Search

Navigation