Advances in Phenotyping of Functional Traits

  • Charles Y. Chen
  • Christopher L. Butts
  • Phat M. Dang
  • Ming Li Wang


Phenotyping is analyzing a plant’s phenotype and providing a critical means to understand morphological, biochemical, and physiological principles in the control of basic plant functions as well as to select superior genotypes in plant breeding. Besides well-known classical plant phenotyping procedures based on visual observations, measurements, or biochemical analyses, many recent developments are target specific and highly automated analysis procedures. Automated phenotyping approaches are far more successful at the laboratory and greenhouse scale than in field conditions where many other variable factors complicate the retrieval of imaging data collected in the field. With respect to plant breeding, rapid measurement procedures, a high throughput, a high degree of automation, and an access to appropriate, well-conceived databases are required to depict the performance of certain genotypes in the field. This chapter will focus on destructive, nondestructive, and automated techniques available to quantify plant morphological and biomass traits, root system architecture, physiological functional traits, biochemical quality and nutritional compositions, and postharvest characteristics.


Normalize Difference Vegetation Index Isotope Ratio Mass Spectrometry Enhance Vegetation Index Root System Architecture Normalize Difference Water Index 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. Adams WW III, Demmig-Adams B (2004) Chlorophyll fluorescence as a tool to monitor plant response to the environment. In: Papageorgiou GC, Govindjee (eds) Chlorophyll fluorescence: a probe of photosynthesis. Springer, Dordrecht, pp 583–604CrossRefGoogle Scholar
  2. Aiken RM (1992) Functional relations of root distributions with the flux and uptake of water and nitrate. Dissertation, Michigan State UniversityGoogle Scholar
  3. Albus J, Bostelman R, Dagalakis N (1993) NIST robocrane. J Robot Syst 10:709–724CrossRefGoogle Scholar
  4. Andrade-Sanchez P, Heun JT, Gore MA, French AN, Carmo-Silva E, Salvucci ME (2012) Use of a moving platform for field deployment of plant sensors. In: Proceedings of the 2012 ASABE Annual International Meeting, Dallas, TXGoogle Scholar
  5. Armstrong PR, Maghirang EB, Xie EB, Dowell FE (2006) Comparison of dispersive and fourier-transform NIR instruments for measuring grain and flour attributes. Appl Eng Agric 22:453–457CrossRefGoogle Scholar
  6. Arvidsson S, Pérez-Rodríguez P, Mueller-Roeber B (2011) A growth phenotyping pipeline for Arabidopsis thaliana integrating image analysis and rosette area modeling for robust quantification of genotype effects. New Phytol 191:895–907CrossRefPubMedGoogle Scholar
  7. Baianu IC, You T, Costescu DM, Lozano PR, Prisecaru V, Nelson RL (2012) Nature proceedings. doi: 10.1038/npre.2012.7053.1
  8. Beebe SE, Rojas-Pierce M, Yan X, Blair MW, Pedraza F, Munoz F, Tohme J, Lynch JP (2006) Quantitative trait loci for root architecture traits correlated with phosphorus acquisition in common bean. Crop Sci 46:413–423CrossRefGoogle Scholar
  9. Berni JAJ, Zarco-Tejada PJ, Suarez L, Fereres E (2009) Thermal and narrowband multispectral remote sensing for vegetation monitoring from an unmanned aerial vehicle. IEEE T Geosci Remote 47:722–738CrossRefGoogle Scholar
  10. Biskup B, Scharr H, Schurr U, Rascher U (2007) A stereo imaging system for measuring structural parameters of plant canopies. Plant Cell Environ 30:1299–1308CrossRefPubMedGoogle Scholar
  11. Boldor D, Sanders TH, Swartzel KR, Simunovic J (2002) Computer-assisted color classification of peanut pods. Peanut Sci 29:41–46CrossRefGoogle Scholar
  12. Carter GA, Knapp AK (2001) Leaf optical properties in higher plants: linking spectral characteristics to stress and chlorophyll concentration. Am J Bot 88:677–684CrossRefPubMedGoogle Scholar
  13. Chamberlin KD, Melouk HA, Madden R, Dillwith JW, Bannore Y, El Rassi Z, Payton M (2011) Determining the oleic/linoleic acid ratio in a single peanut seed: a comparison of two methods. Peanut Sci 38:78–84CrossRefGoogle Scholar
  14. Colaizzi PD, Barnes EM, Clarke TR, Choi CY, Waller PM, Haberland J, Kostrzewski M (2003) Water stress detection under high frequency sprinkler irrigation with water deficit index. J Irrig Drain E-ASCE 129:36–43CrossRefGoogle Scholar
  15. Colvin BC, Rowland DL, Faircloth WH, Ferrell JA (2013) Assessment of a digital imaging system for determining peanut maturity: plot and on-farm trials. 2012 Proceedings of the American Peanut Research Education Society, Raleigh, NCGoogle Scholar
  16. Craig H (1957) Isotopic standards for carbon and oxygen and correction factors for mass-spectrometric analysis of carbon dioxide. Geochim Cosmochim Acta 12:133–149CrossRefGoogle Scholar
  17. Davidson JI, Whitaker JA, Dickens JW (1982) Grading, cleaning, storage, shelling, and marketing of peanuts in the United States. In: Pattee, Young (eds) Peanut science and technology. American Peanut Research and Education Society, Yoakum, TXGoogle Scholar
  18. De Wolf J, Duchateau L, Schrevens E (2008) Dealing with sources of variability in the data-analysis of phenotyping experiments with transgenic rice. Euphytica 160:325–337CrossRefGoogle Scholar
  19. Dean LL, Hendrix KW, Davis JP, Sanders TH, Klevorn CM (2013) Development of lipid components of high- and normal-oleic peanuts. 2013 Proceedings of the American Peanut Research Education Society Annual Meeting, Young Harris, GAGoogle Scholar
  20. Dowell FE, Maghirang EB, Jayaraman V (2009) Technical note: measuring grain and insect characteristics using NIR laser array technology. Appl Eng Agric 26:165–169CrossRefGoogle Scholar
  21. Fang S, Yan X, Liao H (2009) 3D reconstruction and dynamic modeling of root architecture in situ and its application to crop phosphorus research. Plant J 60:1096–1108CrossRefPubMedGoogle Scholar
  22. Farquhar GD, Richards RA (1984) Isotopic composition of plant carbon correlates with water-use efficiency in wheat genotypes. Aust J Plant Physiol 11:539–552CrossRefGoogle Scholar
  23. Farquhar GD, Ehleringer JR, Hubick KT (1989) Carbon isotope discrimination and photosynthesis. Annu Rev Plant Physiol Plant Mol Biol 40:503–537CrossRefGoogle Scholar
  24. Filella I, Serrano I, Serra J, Penuelas J (1995) Evaluating wheat nitrogen status with canopy reflectance indices and discriminant analysis. Crop Sci 35:1400–1405CrossRefGoogle Scholar
  25. Fitter A (2002) Characteristics and functions of root systems. In: Waisel Y, Eshel A, Kafkafi L (eds) Plant roots: the hidden half, 3rd edn. Marcel Dekker Inc, New YorkGoogle Scholar
  26. French AN, Hunsaker DJ, Clarke TR, Fitzgerald GJ, Luckett WE, Pinter PJ Jr (2007) Energy balance estimation of evapotranspiration for wheat grown under variable management practices in central Arizona. Trans ASABE 50:2059–2071CrossRefGoogle Scholar
  27. Galmes J, Ribas-Carbo M, Medrano H, Flexas J (2011) Rubisco activity in Mediterranean species is regulated by the chloroplastic CO2 concentration under water stress. J Exp Bot 62:653–665CrossRefPubMedCentralPubMedGoogle Scholar
  28. Gao BC (1996) NDWI – a normalized difference water index for remote sensing of vegetation liquid water from space. Remote Sens Environ 58:257–266CrossRefGoogle Scholar
  29. Gärtner H, Denier C (2006) Application of a 3D laser scanning device to acquire the structure of whole root systems- a pilot study. In: Heinrich I, Gärtner H, Monbaron M, Schleser G (eds) TRACE – tree rings in archaeology. Climatology and ecology, vol 4., pp 288–294Google Scholar
  30. Gartner H, Wagner B, Heinrich I, Denier C (2009) 3D-laser scanning: a new method to analyze coarse tree root systems. For Snow Landsc Res 82:95–106Google Scholar
  31. Gitelson AA, Merzlyak MN, Chivkunova OB (2001) Optical properties and nondestructive estimation of anthocyanin content in plant leaves. Photochem Photobiol 74:38–45CrossRefPubMedGoogle Scholar
  32. Gitelson AA, Zur Y, Chivkunova OB, Merzlyak MN (2002) Assessing carotenoid content in plant leaves with reflectance spectroscopy. Photochem Photobiol 75:272–281CrossRefPubMedGoogle Scholar
  33. Granier C, Aguirrezabal L, Chenu L, Cookson SJ, Dauzat M, Hamard P, Thioux JJ, Rolland G, Bouchier-Combaud S, Lebaudy A, Muller B, Simonneau T, Tardieu F (2006) PHENOPSIS, an automated platform for reproducible phenotyping of plant responses to soil water deficit in Arabidopsis thaliana permitted the identification of an accession with low sensitivity to soil water deficit. New Phytol 169:623–635CrossRefPubMedGoogle Scholar
  34. Grimm CC, Champagne ET, Sanders TH (1998) Determination of peanut maturity using a hunter colorimeter. Peanut Sci 25:99–103CrossRefGoogle Scholar
  35. Haberland JA, Colaizzi PD, Kostrzewski MA, Waller PM, Choi CY, Eaton FE, Barnes EM, Clarke TR (2010) AgIIS, Agricultural Irrigation Imaging System. Appl Eng Agric 26:247–253CrossRefGoogle Scholar
  36. Hakala T, Suomalainen J, Peltoniemi J, (2010) Acquisition of bidirectional reflectance factor dataset using a micro unmanned aerial vehicle and a consumer camera. Remote Sens 2:819–832CrossRefGoogle Scholar
  37. Hall AE, Richards RA, Condon AG, Wright GC, Farquhar GD (2010) Carbon isotope discrimination and plant breeding. In: Plant breeding reviews. Wiley, New YorkGoogle Scholar
  38. Hammons RO, Tai PYP, Young CT (1978) Arginine maturity index: relationship with other traits in peanuts. Peanut Sci 5:68–71CrossRefGoogle Scholar
  39. Hochholdinger F, Tuberosa R (2009) Genetic and genomic dissection of maize root development and architecture. Curr Opin Plant Biol 12:172–177CrossRefPubMedGoogle Scholar
  40. Huete AR, Didan K, Shimabukuro YE, Ratana P, Saleska SR, Hutyra LR, Yang W, Nemani RR, Myneni R (2006) Amazon rainforests green-up with sunlight in dry season. Geophys Res Lett 33:6405CrossRefGoogle Scholar
  41. Hunt ER Jr, Cavigelli M, Daughtry CST III, McMurtrey JE, Walthall CL (2005) Evaluation of digital photography from model aircraft for remote sensing of crop biomass and nitrogen status. Precis Agric 6:359–378CrossRefGoogle Scholar
  42. Isleib TG, Pattee HE (2007) A note on combining ability for sensory quality of peanut. Peanut Sci 34:122–125CrossRefGoogle Scholar
  43. Isleib TG, Pattee HE, Giesbrecht FG (2003) Narrow-sense heritability of selected sensory descriptors in Virginia-type peanut (Arachis hypogaea L.). Peanut Sci 30:64–66CrossRefGoogle Scholar
  44. Iyer-Pascuzzi AS, Symonova O, Mileyko Y, Yueling H, Belcher H, Harer J, Weitz JS, Benfey PN (2010) Imaging and analysis platform for automatic phenotyping and trait ranking of plant root systems. Plant Physiol 152:1148–1157CrossRefPubMedCentralPubMedGoogle Scholar
  45. Jahnke S, Menzel MI, van Dusschoten D, Roeb GW, Bu¨ hler J, Minwuyelet S, Blu¨mler P, Temperton VM, Hombach T, Streun M, Beer S, Khodaverdi M, Ziemons K, Coene HH, Schurr U (2009) Combined MRI-PET dissects dynamic changes in plant structures and functions. Plant J 59:634–644CrossRefPubMedGoogle Scholar
  46. Jensen T, Apan A, Young F, Zeller L (2007) Detecting the attributes of a wheat crop using digital imagery acquired from a low-altitude platform. Comput Electron Agric 59:66–77CrossRefGoogle Scholar
  47. Johnson BR, Mozingo RW, Young CT (1976) Evaluation of the arginine maturity index (AMI) method of maturity estimation for Virginia type peanuts. Peanut Sci 3:32–36CrossRefGoogle Scholar
  48. Jones HG, Vaughan RA (2010) Remote sensing of vegetation: principles, techniques and applications. Oxford University Press, New YorkGoogle Scholar
  49. Jones CL, Maness NO, Stone ML, Jayasekara R (2007) Chlorophyll estimation using multispectral reflectance and height sensing. Trans ASABE 50:1867–1872CrossRefGoogle Scholar
  50. Kandala CVK, Nelson SO (2005) Nondestructive moisture determination in small samples of peanuts by Rf impedance measurement. Trans ASAE 48:715–718CrossRefGoogle Scholar
  51. Kolber Z, Klimov D, Ananyev G, Rascher U, Berry J, Osmond B (2005) Measuring photosynthetic parameters at a distance: laser induced fluorescence transient (LIFT) method for remote measurements of photosynthesis in terrestrial vegetation. Photosynth Res 84:121–129CrossRefPubMedGoogle Scholar
  52. Kostrzewski M, Waller P, Guertin P, Haberland J, Colaizzi P, Barnes E, Thompson T, Clarke T, Riley E, Choi C (2003) Ground-based remote sensing of water and nitrogen stress. Trans ASAE 46:29–38CrossRefGoogle Scholar
  53. Kulkarni SS, Bajwa SG, Rupe JC, Kirkpatrick TL (2008) Spatial correlation of crop response to soybean cyst nematode (Heterodera glycines). Trans ASABE 51:1451–1459CrossRefGoogle Scholar
  54. Lawlor DW, Cornic G (2002) Photosynthetic carbon assimilation and associated metabolism in relation to water deficits in higher plants. Plant Cell Environ 25:275–294CrossRefPubMedGoogle Scholar
  55. LemnaTec (2013) Image processing in biology. Accessed 11 Dec 2013
  56. Liedgens MM (1998) Seasonal development of the maize root system minirhizotron-equipped lysimeters. Dissertation, Swiss Federal Institute of Technology Zürich, SwedenGoogle Scholar
  57. Linsenmeier A, Lehnart R, Löhnertz O, Michel H (2010) Investigation of grapevine root distribution by in situ minirhizotron observation. Vitis 49:1–6Google Scholar
  58. Lynch J (1995) Root architecture and plant productivity. Plant Physiol 109:7–13PubMedCentralPubMedGoogle Scholar
  59. Merz TC, Chapman S (2011) Autonomous unmanned helicopter system for remote sensing missions in unknown environments. Int Arch Photogr Remote Sens Spat Inform Sci 38:1–6Google Scholar
  60. Merzlyak MN, Gitelson AA, Chivkunova OB, Rakitin VY (1999) Non-destructive optical detection of pigment changes during leaf senescence and fruit ripening. Physiol Planta 106:135–141CrossRefGoogle Scholar
  61. Myneni RB, Hall FG, Sellers PJ, Marshak AL (1995) The interpretation of spectral vegetation indexes. IEEE Trans Geosci Remote Sens 33:481–486CrossRefGoogle Scholar
  62. Nagler PL, Inoue Y, Glenn EP, Russ AL, Daughtry CST (2003) Cellulose absorption index (CAI) to quantify mixed soil-plant litter scenes. Remote Sens Environ 87:310–325CrossRefGoogle Scholar
  63. O’Leary MH (1988) Carbon isotopes in photosynthesis. Bio Sci 38:328–336Google Scholar
  64. Ostonen I, Püttsepp Ü, Biel C, Alberton O, Bakker MR, Lõhmus K, Majdi H, Metcalfe D, Olsthoorn AFM, Pronk A, Vanguelova E, Weih M, Brunner I (2007) Specific root length as an indicator of environmental change. Plant Biosyst 41:426–442CrossRefGoogle Scholar
  65. Padmalatha Y, Rami Reddy S, Yellamanda Reddy T (2006) The relationship between weather parameters during developmental phase and fruit attributes and yield of peanut. Peanut Sci 33:118–124CrossRefGoogle Scholar
  66. Passioura JB (1977) Grain yield, harvest index and water use of wheat. J Aust Inst Agric Sci 43:117–120Google Scholar
  67. Pattee HE, Wynne JC, Young JH, Cox FR (1977) The seed-hull weight ratio as an index of peanut maturity. Peanut Sci 4:47–50CrossRefGoogle Scholar
  68. Pattee HE, Isleib TG, Gorbet DW, Giesbrecht FG, Cui Z (2001) Parent selection in breeding for roasted peanut flavor quality. Peanut Sci 28:51–58CrossRefGoogle Scholar
  69. Payero JO, Neale CMU, Wright JL (2004) Comparison of eleven vegetation indices for estimating plant height of alfalfa and grass. Appl Eng Agric 20:385–393CrossRefGoogle Scholar
  70. Penuelas J, Filella I (1998) Technical focus: visible and near-infrared reflectance techniques for diagnostic plant physiological status. Trends Plant Sci 3:151–156CrossRefGoogle Scholar
  71. Penuelas J, Filella I, Gamon JA (1995) Assessment of photosynthetic radiation-use efficiency with spectral reflectance. New Phytol 131:291–296CrossRefGoogle Scholar
  72. Perret JS, Al-Belushi ME, Deadman M (2007) Non-destructive visualization and quantification of roots using computed tomography. Soil Biol Biochem 39:391–399CrossRefGoogle Scholar
  73. Rascher U, Damm A, van der Linden S, Okujeni A, Pieruschka R, Schickling A, Hostert P (2010) Sensing of photosynthetic activity of crops. In: Oerke EC, Gerhards R, Menz G (eds) Precision crop protection – the challenge and use of heterogeneity. Springer, HeidelbergGoogle Scholar
  74. Rascher U, Blossfeld S, Fiorani F, Jahnke S, Jansen M, Kuhn AJ, Matsubara S, Märtin LLA, Merchant A, Metzner R, Müller-Linow M, Nagel KA, Pieruschka R, Pinto F, Schreiber CM, Temperton VM, Thorpe MR, van Dusschoten D, van Volkenburgh E, Windt CW, Schurr U (2011) Non-invasive approaches for phenotyping of enhanced performance traits in bean. Funct Plant Biol 38:968–983CrossRefGoogle Scholar
  75. Reuzeau C, Pen J, Frankard V, de Wolf J, Peerbolte R, Broekaert W, van Camp W (2005) TraitMill™: a discovery engine for identifying yield enhancement genes in cereals. Mol Plant Breed 5:753–759Google Scholar
  76. Richards RA, Rebetzke GJ, Watt M, Condon AG, Spielmeyer W, Dolferus R (2010) Breeding for improved water productivity in temperate cereals: phenotyping, quantitative trait loci, markers and the selection environment. Funct Plant Biol 37:85–97CrossRefGoogle Scholar
  77. Ritchie GL, Sullivan DG, Perry CD, Hook JE, Bednarz CW (2008) Preparation of a low-cost digital camera system for remote sensing. Appl Eng Agric 24:885–896CrossRefGoogle Scholar
  78. Rohacek K, Bartak M (1999) Technique of the modulated chlorophyll fluorescence: basic concepts, useful parameters, and some applications. Photosynthetica 37:339–363CrossRefGoogle Scholar
  79. Romer C, Wahabzada M, Ballvora A, Pinto F, Rossini M, Panigada C, Behmann J, Léon J, Thurau C, Bauckhage C, Kersting K, Rascher U, Plümer L (2012) Early drought stress detection in cereals: simplex volume maximisation for hyperspectral image analysis. Funct Plant Biol 39:878–890CrossRefGoogle Scholar
  80. Rowland DL, Sorensen RB, Balkcom RS, Lamb MC (2005) Estimating stem water flow in peanut (Arachis hypogaea L.) under different irrigation methods. Peanut Sci 32:81–90CrossRefGoogle Scholar
  81. Rowland DL, Sorensen RB, Butts CL, Faircloth WH, Sullivan DG (2008) Canopy characteristics and their ability to predict peanut maturity. Peanut Sci 35:43–54CrossRefGoogle Scholar
  82. Ruixiu S, Wilkerson JB, Wilhelm LR, Tompkins FD (1989) A microcomputer-based morphometer for bush-type plants. Comput Electron Agric 4:43–58CrossRefGoogle Scholar
  83. Sanders TH, McMichael RW, Hendrix KW (2000) Occurrence of resveratrol in edible peanuts. J Agric Food Chem 48:1243–1246CrossRefPubMedGoogle Scholar
  84. Schleicher TD, Bausch WC, Delgado JA (2003) Low ground-cover filtering to improve reliability of the nitrogen reflectance index (NRI) for corn N status classification. Trans ASAE 46:1707–1711CrossRefGoogle Scholar
  85. Schmilovitch Z, Nelson SO, Kandala CVK, Lawrence KC (1996) Implementation of dual-frequency RF impedance technique for on-line moisture sensing in single in-shell pecans. Appl Eng Agric 12:475–479CrossRefGoogle Scholar
  86. Schreiber U (2004) Pulse-Amplitude-modulation (PAM) fluorometry and saturation pulse method: an overview. In: Papageorgiou GC, Govindjee (eds) Chlorophyll a fluorescence: a signature of photosynthesis. Springer, DordrechtGoogle Scholar
  87. Schröder JJ, Groenwold J, Zaharieva T (1996) Soil mineral nitrogen availability to young maize plants as related to root length density distribution and fertilizer application method. Neth J Agric Sci 44:209–225Google Scholar
  88. Serrano L, Penuelas J, Ustin SL (2002) Remote sensing of nitrogen and lignin in mediterranean vegetation from AVIRIS data: decomposing biochemical from structural signals. Remote Sens Environ 81:355–364CrossRefGoogle Scholar
  89. Shou XC, Luo XW (2009) Advances in non-destructive measurement and 3D visualization methods for plant root based on machine vision. In: Proceedings of the 2nd international conference on biomedical engineering and informatics. Tianjin, BMEI’09, pp 1–5Google Scholar
  90. Sinclair TR, Tanner CB, Bennett JM (1984) Water-use efficiency in crop production. Bio Sci 34:36–40Google Scholar
  91. Steele KA, Virk DS, Kumar R, Prasad SC, Witcombe JR (2007) Field evaluation of upland rice lines selected for QTLs controlling root traits. Field Crops Res 101:180–186CrossRefGoogle Scholar
  92. Sundaram J, Kandala CV, Butts CL, Chen CY, Sobolev V (2011) Nondestructive NIR reflectance spectroscopic method for rapid fatty acid analysis of peanut seeds. Peanut Sci 38:85–92CrossRefGoogle Scholar
  93. Taylor HM, Huck MG, Klepper B, Lund ZF (1970) Measurement of soil-grown roots in a rhizotron. Agron J 62:807–809CrossRefGoogle Scholar
  94. Tester M, Langridge P (2010) Breeding technologies to increase crop production in a changing world. Science 327(5967):818–822CrossRefPubMedGoogle Scholar
  95. Tollner EW, Boudolf VA III, McClendon RW, Hung YC (1998) Predicting peanut maturity with magnetic resonance. Trans ASABE 41:1199–1205CrossRefGoogle Scholar
  96. Trabelsi S, Nelson SO (2006) Microwave sensing technique for nondestructive determination of bulk density and moisture content in unshelled and shelled peanuts. Trans ASABE 49:1563–1568CrossRefGoogle Scholar
  97. Upchurch DR, Ritchie JT (1983) Root observations using a video recording system in minirhizotrons. Agron J 75:1009–1015CrossRefGoogle Scholar
  98. Vaughn BH, Ferretti DF, Miller J, White JWC (2004) Stable isotope measurements of atmospheric CO2 and CH4. In: de Groot PA (ed) Handbook of stable isotope analytical techniques. Elsevier, AmsterdamGoogle Scholar
  99. Violle C, Navas ML, Vile D, Kazakou E, Fortunel C, Hummel I, Garnier E (2007) Let the concept of trait be functional! Oikos 116:882–892CrossRefGoogle Scholar
  100. Vos J, Groenwol J (1987) The relation between root growth along observation tubes and in bulk soil. In: Taylor HM (ed) Minirhizotron observation tubes: methods and applications for measuring rhizosphere dynamics. American Society of Agronomy, Inc., Crop Science Society of America, Inc., Soil Science Society of America, Inc, MadisonGoogle Scholar
  101. Walter A, Scharr H, Gilmer F, Zierer R, Nagel KA, Ernst M, Wiese A, Virnich O, Christ MM, Uhlig B, Juenger S, Schurr U (2007) Dynamics of seedling growth acclimation towards altered light conditions can be quantified via GROWSCREEN: a setup and procedure designed for rapid optical phenotyping of different plant species. New Phytol 174:447–455CrossRefPubMedGoogle Scholar
  102. Wang ML, Morris JB (2007) Flavonoid content in seeds of guar germplasm using HPLC. Plant Genet Resour 5:96–99CrossRefGoogle Scholar
  103. Werner C, Schnyder H, Cuntz M, Keitel C, Zeeman MJ, Dawson TE, Badeck FW, Brugnoli E, Ghashghaie J, Grams TEE, Kayler ZE, Lakatos M, Lee X, Maguas C, Ogee J, Rascher KG, Siegwolf RTW, Unger S, Welker J, Wingate L, Gessler A (2012) Progress and challenges in using stable isotopes to trace plant carbon and water relations across scales. Biogeosciences 8:3083–3111CrossRefGoogle Scholar
  104. 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 G (2012) Field-based phenomics for plant genetics research. Field Crop Res 133:101–112CrossRefGoogle Scholar
  105. Williams EJ, Drexler JS (1981) A non-destructive method for determining peanut pod maturity. Peanut Sci 8:134–141CrossRefGoogle Scholar
  106. Yeh N, Chung JP (2009) High-brightness LEDs—energy efficient lighting sources and their potential in indoor plant cultivation. Renew Sustain Energ Rev 13:2175–2180CrossRefGoogle Scholar
  107. Yu S, Wilson R, Edmondson R, Parsons N (2007) Surface modelling of plants from stereo images. In: Proceedings of the 6th international conference on 3-D digital imaging and modeling, 3DIM’07, Montreal, QCGoogle Scholar
  108. Zarco-Tejada PJ, Berni JAJ, Subrez L, Sepulcre-Canto G, Morales F, Miller JR (2009) Imaging chlorophyll fluorescence with an airborne narrow-band multispectral camera for vegetation stress detection. Remote Sens Environ 113:1262–1275CrossRefGoogle Scholar
  109. Zeng G, Birchfield S, Wells C (2010) Rapid automated detection of roots in minirhizotron images. Mach Vis Appl 21:309–317CrossRefGoogle Scholar
  110. Zhu JM, Ingram PA, Benfey PN, Elich T (2011) From lab to field, new approaches to phenotyping root system architecture. Curr Opin Plant Biol 14:310–317CrossRefPubMedGoogle Scholar

Copyright information

© Springer India 2015

Authors and Affiliations

  • Charles Y. Chen
    • 1
  • Christopher L. Butts
    • 2
  • Phat M. Dang
    • 2
  • Ming Li Wang
    • 3
  1. 1.Department of Crop, Soil and Environmental SciencesAuburn UniversityAuburnUSA
  2. 2.USDA-ARS National Peanut Research LaboratoryDawsonUSA
  3. 3.USDA-ARS Plant Genetic Resources Conservation UnitGriffinUSA

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