Skip to main content

Abstract

The term plant phenotyping has been regenerated with the contribution of sensors, system technologies, and algorithms. This new plant describing concept allows multi-trait assessment with automatic measurements. Uniform structure, nondestructive measurements, precise results, and direct storage are the advantages of digital phenotyping. The hyper-spectral spectroradiometers and imaging technologies lead the way of new plant phenotyping applications. This high-throughput technique therefore requires lots of traditional and novel traits to present new characterization. Digital-based phenotyping in plants is new and still a developing area of research. The most often used traits of digital phenotyping are canopy temperature, chlorophyll fluorescence, stomatal conductance, chlorophyll content, leaf water potential, leaf area, fruit color, carbon isotope discrimination, light interception, senescence, and root traits which have been discussed in this chapter together with their advantages, limitations, and plant breeding potentials.

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

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 109.99
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

  • Alchanatis V, Cohen Y, Cohen S, Moller M, Sprinstin M, Meron M, Tsipris J, Saranga Y, Sela E (2010) Evaluation of different approaches for estimating and mapping crop water status in cotton with thermal imaging. Precis Agric 11:27–41

    Article  Google Scholar 

  • Baker NR (2008) Chlorophyll florescence: a probe of photosynthesis in vivo. Annu Rev Plant Biol 113:59–89

    Google Scholar 

  • Barbour MM, Fischer RA, Sayre KD, Farquhar GD (2000) Oxygen isotope ratio of leaf and grain material correlates with stomatal conductance and grain yield in irrigated wheat. Aust J Plant Physiol 27:625–637

    CAS  Google Scholar 

  • Benamar A, Pierart A, Baecker V, Avelange-Macherel MH, Rolland A, Gaudichon S, di Gioia L, Macherel D (2013) Simple system using natural mineral water for high-throughput phenotyping of Arabidopsis thaliana seedlings in liquid culture. Int J High Throughput Screen 4:1–15

    Google Scholar 

  • Berger B, Tester M (2009) High throughput phenotyping for measuring drought tolerance. Paper presented at: The Conference of InterDrought III, Shanghai, 11–16 Oct 2009

    Google Scholar 

  • Berger B, de Regt B, Tester M (2013) Applications of high-throughput plant phenotyping to study nutrient use efficiency. Methods Mol Biol 953:277–290

    Article  CAS  PubMed  Google Scholar 

  • Bignami C, Rossini F (1996) Image analysis estimation of leaf area index and plant size of young hazelnut plants. J Hort Sci 71:113–121

    Google Scholar 

  • Bilder RM, Sabb FW, Cannon TD, London ED, Jentsch JD, Parker DS, Poldrack RA, Evans C, Freimer NB (2009) Phenomics: the systematic study of phenotypes on a genome-wide scale. Neuroscience 164:30–42

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  • Bioversity International (2007) Guidelines for the development of crop descriptor lists. Bioversity technical bulletin series. Rome, Italy

    Google Scholar 

  • Bioversity International and Rural Development Administration (2009) A training module for the international course on plant genetic resources and genebank management. Bioversity International, Rome

    Google Scholar 

  • Chaerle L, Leinonen I, Jones HG, Straeten DVD (2007) Monitoring and screening plant populations with combined thermal and chlorophyll fluorescence imaging. J Exp Bot 58:773–784

    Article  CAS  PubMed  Google Scholar 

  • Chen YL, Dunbabin VM, Diggle AJ, Siddique KHM, Rengel Z (2011) Development of a novel semi-hydroponic phenotyping system for studying root architecture. Funct Plant Biol 38:355–363

    Article  Google Scholar 

  • Chenu K, Fournier C, Andrieu B, GiauVret C (2007) An architectural approach to investigate maize response to low temperature. In: Spiertz JHJ, Struik PC, Can Laar HH (eds) Scale and complexity in plant systems research: gene – plant – crop relations. Springer, Heidelberg, pp 203–212

    Chapter  Google Scholar 

  • Clark RT, MacCurdy RB, Jung JK, Shaff JE, McCouch SR, Aneshansley DJ, Kochian LV (2011) Three-dimensional root phenotyping with a novel imaging and software platform. Plant Physiol 156:455–465

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  • Cohen Y, Alchanatis V, Meron M, Saranga Y, Tsipris J (2005) Estimation of leaf water potential by thermal imagery and spatial analysis. J Exp Bot 56:1843–1852

    Article  CAS  PubMed  Google Scholar 

  • Cristofori V, Rouphael Y, Gyves EM, Bignami C (2007) A simple model for estimating leaf area of hazel nut from linear measurements. Sci Hortic 113:221–225

    Article  Google Scholar 

  • Darrigues A, Hall J, Van der Knaap E, Francis DM (2008) Tomato analyzer-color test: a new tool for efficient digital phenotyping. J Am Soc Hortic Sci 133:579–586

    Google Scholar 

  • De Bei R, Cozzolino D, Sullivan W, Cynkar W, Fuentes S, Dambergs R, Pech J, Tyerman S (2011) Non-destructive measurement of grapevine water potential using near infrared spectroscopy. Aust J Grape Wine Res 17:62–71

    Article  Google Scholar 

  • Dwyer LM, Tollenaar M, Houwing L (1991) A non-destructive method to monitor leaf greenness in corn. Can J Plant Sci 71:505–509

    Article  Google Scholar 

  • Eberius M (2008) Tomato phenotyping. http://www.lemnatec.com/plant-phenotyping.php, updated September 2008. Accessed 26 Oct 2012

  • Edwards KD, Humphry M, Sanchez-Tamburrino P (2012) Advances in plant senescence. In: Nagata T (ed) Senescence. InTech, Rijeka, pp 117–136

    Google Scholar 

  • 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 60:1096–1108

    Article  CAS  PubMed  Google Scholar 

  • Fender F, Hanneken M, Stroth S, Kielhorn A, Linz A, Ruckelshausen (2006) Sensor fusion meets GPS- individual plant detection. Proceedings CIGR, Eur Ag Eng. Bonn, Germany, 03–07 Sept 2006, pp 279–280

    Google Scholar 

  • Ferrio JP, Mateo MA, Bort J, Abdalla O, Voltas J, Araus JL (2007) Relationships of grain D13C and D18O with wheat phenology and yield under water-limited conditions. Ann Appl Biol 150:207–215

    Article  CAS  Google Scholar 

  • Fischer RA, Rees D, Sayre KD, Lu ZM, Condon AG, Saavedra AL (1998) Wheat yield progress associated with higher stomatal conductance and photosynthetic rate, and cooler canopies. Crop Sci 38:1467–1475

    Article  Google Scholar 

  • Furbank RT, Tester M (2011) Phenomics – technologies to relieve the phenotyping bottleneck. Trends Plant Sci 16:635–944

    Article  CAS  PubMed  Google Scholar 

  • Gao M, Van der Heijden GWAM, Vos J, Eveleens BA, Marcelis LFM (2012) Estimation of leaf area for large scale phenotyping and modeling of rose genotypes. Sci Hortic 138:227–234

    Article  Google Scholar 

  • Gonzalo MJ, Brewer MT, Anderson C, Sullivan D, Gray S, Van der Knaap E (2009) Tomato fruit shape analysis using morphometric and morphology attributes implemented in Tomato Analyzer software program. J Am Soc Hortic Sci 134:77–87

    Google Scholar 

  • Grant OM, Chaves MM, Jones HG (2006) Optimizing thermal imaging as a technique for detecting stomatal closure induced by drought stress under greenhouse conditions. Physiol Plant 127:507–518

    Article  CAS  Google Scholar 

  • Grant OM, Tronina L, Jones HG, Chaves MM (2007) Exploring thermal imaging variables for the detection of stress responses in grapevine under different irrigation regimes. J Exp Bot 58:815–825

    Article  CAS  PubMed  Google Scholar 

  • Gregory P, Hutchison D, Read D, Jenneson P, Gilboy W, Morton E (2003) Non-invasive imaging of roots with high resolution X-ray micro-tomography. Plant Soil 255:351–359

    Article  CAS  Google Scholar 

  • Harlan JR (1975) Crops and man. American Society of Agronomy and Crop Science Society of America, Madison

    Google Scholar 

  • Howarth C, Gay A, Draper J, Bartlett T, Doonan J (2011) Development of high throughput plant phenotyping facilities at Aberystwyth. Proceeding book of Phenodays, Hof van Wageningen, 12–14 Oct 2011, p 18

    Google Scholar 

  • Hund A (2010) Genetic variation in the gravitropic response of maize roots to low temperatures. Plant Root 4:22–30

    Article  Google Scholar 

  • Iyer-Pascuzzi AS, Symonova O, Mileyko Y, Hao Y, 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 152:1148–1157

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  • Jahnke S, Menzel MI, van Dusschoten D, Roeb GW, Buhler J, Minwuyelet S, Blumler P, Temperton VM, Hombach T, Streun M et al (2009) Combined MRI-PET dissects dynamic changes in plant structures and functions. Plant J59:634–644

    Article  Google Scholar 

  • James RA, Sirault XR (2012) Infrared thermography in plant phenotyping for salinity tolerance. Methods Mol Biol 913:173–189

    CAS  PubMed  Google Scholar 

  • Jaramillo S, Baena M (2002) Ex situ conservation of plant genetic resources: training module. International Plant Genetic Resources Institute, Cali

    Google Scholar 

  • Jiménez-Bello MA, Ballester C, Castel JR, Intrigliolo DS (2011) Development and validation of an automatic thermal imaging process for assessing plant water status. Agr Water Manag 98:1497–1504

    Article  Google Scholar 

  • Jones HG, Vaughan RA (2010) Remote sensing of vegetation principles, techniques, and applications. Oxford University Press, Oxford

    Google Scholar 

  • Khazaie H, Mohammady S, Monneveux P, Stoddard F (2011) The determination of direct and indirect effects of carbon isotope discrimination (Δ), stomatal characteristics and water use efficiency on grain yield in wheat using sequential path analysis. Aust J Crop Sci 5:466–472

    Google Scholar 

  • Klose R, Penlington J, Ruckelshausen A (2009) Usability study of 3D time-of-flight cameras for automatic plant phenotyping. 1st international workshop on computer image analysis in agriculture, Potsdam, Germany, 27–28 Aug 2009

    Google Scholar 

  • Kriston-Vizi J, Umeda M, Miyamoto K, Ferenczy A (2003) Leaf water potential – measurement method using computer image analysis in Satsuma mandarin. ASAE annual international meeting, Las Vegas, USA, 27–30 July 2003

    Google Scholar 

  • Lang NS, Silbernagel J, Perry EM, Smithyman R, Mills L, Wample RL (2000) Remote image and leaf reflectance analysis to evaluate the impact of environmental stress on grape canopy metabolism. HortTechnology 10:468–474

    Google Scholar 

  • Leport L, Musse M, Cambert M, De Franscesci L, Le Cahérec F, Burel A, Mariette F, Bouchereau A (2011) Canola leaf senescence phenotyping and identification of subcellular changes using NMR tool. 2nd international plant phenotyping symposium toward plant phenotyping science: challenges and perspectives, Forschungszentrum Jülich, Germany, 05–07 Sept 2011

    Google Scholar 

  • Lim PO, Kim HJ, Nam HG (2007) Leaf senescence. Annu Rev Plant Biol 58:115–136

    Article  CAS  PubMed  Google Scholar 

  • Liu T, Song F, Liu S, Zhu X (2012) Light interception and radiation use efficiency response to narrow-wide row planting patterns in maize. Aust J Crop Sci 6:506–513

    Google Scholar 

  • Lootens P, Waes JV, Carlier L (2007) Evaluation of the tepal colour of Begonia × tuberhybrida Voss for DUS testing using image analysis. Euphytica 155:135–142

    Article  Google Scholar 

  • Lopes M, Mullan D (2012) Carbon isotope discrimination. In: Pask A, Pietragalla J, Mullan D, Reynolds M (eds) Physiological breeding II: a field guide to wheat phenotyping. CIMMYT, Mexico, pp 28–30

    Google Scholar 

  • Majer P, Sass L, Horvath GV, Hideg E (2010) Leaf hue measurements offer a fast, high-throughput initial screening of photosynthesis in leaves. J Plant Physiol 167:74–76

    Article  CAS  PubMed  Google Scholar 

  • Matsuda O, Tanaka A, Fujita T, Iba K (2012) Hyperspectral imaging techniques for rapid identification of Arabidopsis mutants with altered leaf pigment status. Plant Cell Physiol 53:1154–1170

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  • McCrady RL, Jokela EJ (1998) Canopy dynamics, light interception, and radiation use efficiency of selected loblolly pine families. For Sci 44:64–72

    Google Scholar 

  • Merah O, Deléens E, Teulat B, Monneveux P (2001) Productivity and carbon isotope discrimination in durum wheat organs under a Mediterranean climate. C R Acad Sci 324:51–57

    Article  CAS  Google Scholar 

  • Moghaddam PA, Derafshi MH, Shirzad V (2011) Estimation of single leaf chlorophyll content in sugar beet using machine vision. Turk J Agric For 35:563–568

    CAS  Google Scholar 

  • Monneveux P, Reynolds MP, Trethowan R, González-Santoyo H, Peña RJ, Zapata F (2005) Relationship between grain yield and carbon isotope discrimination in bread wheat under four water regimes. Eur J Agron 22:231–242

    Article  CAS  Google Scholar 

  • Montes JM, Melchinger AE, Reif JC (2007) Novel throughput phenotyping platforms in plant genetic studies. Trends Plant Sci 12:433–436

    Article  CAS  PubMed  Google Scholar 

  • Mullan D, Mullan D (2012) Chlorophyll content. In: Pask A, Pietragalla J, Mullan D, Reynolds M (eds) Physiological breeding II: a field guide to wheat phenotyping. CIMMYT, Mexico, pp 41–43

    Google Scholar 

  • Mullan D, Pietragalla J (2012) Leaf relative water content. In: Pask A, Pietragalla J, Mullan D, Reynolds M (eds) Physiological breeding II: a field guide to wheat phenotyping. CIMMYT, Mexico, pp 25–27

    Google Scholar 

  • Munns R, James RA, Sirault XRR, Furbank RT, Jones HG (2010) New phenotyping methods for screening wheat and barley for beneficial responses to water deficit. J Exp Bot 61:3499–3507

    Article  CAS  PubMed  Google Scholar 

  • Nasarudin NEM, Shafri HZM (2011) Development and utilization of urban spectral library for remote sensing of urban environment. J Urban Environ Eng 5:44–56

    Article  Google Scholar 

  • NIFA-NSF Phenomics Workshop Report (2011) Phenomics: genotype to phenotype. National Science Foundation, Michigan State University, USA

    Google Scholar 

  • O’Shaughnessy SA, Hebel MA, Evett SR, Colaizzi PD (2011) Evaluation of a wireless infrared thermometer with a narrow field of view. Comput Electron Agric 76:59–68

    Article  Google Scholar 

  • Orbegozo HO (2012) Application of thermography for the assessment of vineyard water status. Dissertation, Universidad De La Rioja, La Rioja

    Google Scholar 

  • Padhi J, Misra RK, Payero JO (2012) Estimation of soil water deficit in an irrigated cotton field with infrared thermography. Field Crop Res 126:45–55

    Article  Google Scholar 

  • Pask A, Pietragalla J (2012) Leaf area, green crop area and senescence. In: Pask A, Pietragalla J, Mullan D, Reynolds M (eds) Physiological breeding II: a field guide to wheat phenotyping. CIMMYT, Mexico, pp 58–62

    Google Scholar 

  • Picha D (2006) Horticultural crop quality characteristics important in international trade. Acta Hort 712:423–426

    Google Scholar 

  • Pierre CS, Arce VT (2012) Osmotic adjustment. In: Pask A, Pietragalla J, Mullan D, Reynolds M (eds) Physiological breeding II: a field guide to wheat phenotyping. CIMMYT, Mexico, pp 21–24

    Google Scholar 

  • Pierre CS, González JLB (2011) Leaf water potential. In: Pask A, Pietragalla J, Mullan D, Reynolds M (eds) Physiological breeding II: a field guide to wheat phenotyping. CIMMYT, Mexico, pp 19–20

    Google Scholar 

  • Pierre CS, Crossa JL, Bonnett D, Yamaguchi-Shinozaki K, Reynolds MP (2012) Phenotyping transgenic wheat for drought resistance. J Exp Bot 63:1799–1808

    Article  Google Scholar 

  • Pietragalla J (2012) Canopy temperature. In: Pask A, Pietragalla J, Mullan D, Reynolds M (eds) Physiological breeding II: a field guide to wheat phenotyping. CIMMYT, Mexico, pp 106–112

    Google Scholar 

  • Pietragalla J, Pask A (2012) Stomatal conductance. In: Pask A, Pietragalla J, Mullan D, Reynolds M (eds) Physiological breeding II: a field guide to wheat phenotyping. CIMMYT, Mexico, pp 15–17

    Google Scholar 

  • Poblaciones MJ, Bellido LL, Bellido RJL (2009) Field estimation of technological bread-making quality in wheat. Field Crop Res 112:253–259

    Article  Google Scholar 

  • Post J (2011) CROP.SENSe.net – phenotyping science for plant breeding and management. Proceeding book of Phenodays, Hof van Wageningen, 12–14 Oct 2011, p 38

    Google Scholar 

  • Puangbut D, Jogloy S, Vorasoot N, Akkasaeng C, Kesmalac T, Patanothai A (2009) Variability in yield responses of peanut (Arachis hypogaea L.) genotypes under early season drought. Asian J Plant Sci 8:254–264

    Article  Google Scholar 

  • Rao NK (2004) Plant genetic resources: advancing conservation and use through biotechnology. Afr J Biotechnol 3:136–145

    Google Scholar 

  • Rebetzke GJ, Condon AG, Richards RA, Farquhar GD (2002) Selection for reduced carbon isotope discrimination increases aerial biomass and grain yield of rainfed bread wheat. Crop Sci 42:739–745

    Article  Google Scholar 

  • Riley K, Rao VR, Zhou MD, Quek P (1996) Characterization and evaluation of plant genetic resources-present status and future challenges. The fourth Ministry of Agriculture, Forestry and Fisheries, Japan (MAFF) International workshop on genetic resources, Tsukuba, NIAR, Japan, 22–24 Oct 1996

    Google Scholar 

  • Rodriguez IR, Miller GL (2000) Using a chlorophyll meter to determine the chlorophyll concentration, nitrogen concentration, and visual quality of St. Augustinegrass. Hortscience 35:751–754

    CAS  Google Scholar 

  • Rodríguez GR, Moyseenko JB, Robbins MD, Morejón NH, Francis DM, Van der Knaap E (2010) Tomato analyzer: a useful software application to collect accurate and detailed morphological and colorimetric data from two-dimensional objects. J Vis Exp 37:1–12

    Google Scholar 

  • Romano G, Zia S, Spreer W, Sanchez C, Cairns J, Araus JL, Müller J (2011) Use of thermography for high throughput phenotyping of tropical maize adaptation in water stress. Comput Electron Agric 79:67–74

    Article  Google Scholar 

  • Rosati A, Badeck FW, Dejong TM (2001) Estimating canopy light interception and absorption using leaf mass per unit leaf area in Solanum melongena. Ann Bot 88:101–109

    Article  Google Scholar 

  • Roth G, Goyne P (2004) Measuring plant water status. In: Dugdale H, Harris G, Neilsen J, Richards J, Roth G, Williams D (eds) WATERpak – a guide for irrigation management in cotton. Cotton Research and Development Corporation, Australia, pp 157–164

    Google Scholar 

  • Salekdeh GH, Reynolds M, Bennett J, Boyer J (2009) Conceptual framework for drought phenotyping during molecular breeding. Trends Plant Sci 14:488–496

    Article  CAS  PubMed  Google Scholar 

  • Samdur MY, Singh AL, Mathur R, Manivel P, Chikani BM, Gor And HK, Khan MA (2000) Field evaluation of chlorophyll meter for screening groundnut (Arachis hypogaea L.) genotypes tolerant to iron-deficiency chlorosis. Curr Sci 79:221–230

    Google Scholar 

  • Sarlikioti V, de Visser PHB, Marcelis LFM (2011) Exploring the spatial distribution of light interception and photosynthesis of canopies by means of a functional – structural plant model. Ann Bot 107:875–883

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  • Scott JW (2010) Automated analysis of fine-root dynamics using a series of digital images. Hortscience 45:1314–1316

    Google Scholar 

  • Songsri P, Jogloy S, Holbrook CC, Kesmala T, Vorasoot N, Akkasaeng C, Patanothai A (2009) Association of root, specific leaf area and SPAD chlorophyll meter reading to water use efficiency of peanut under different available soil water. Agric Water Manag 96:790–798

    Article  Google Scholar 

  • Stoll M, Schultz HR, Berkelmann-Loehnertz B (2008) Exploring the sensitivity of thermal imaging for Plasmopara viticola pathogen detection in grapevines under different water status. Funct Plant Biol 35:281–288

    Article  Google Scholar 

  • Summerfield RJ, Ellis RH, Craufurd PQ (1996) Phenological adaptation to cropping environment. From evaluation descriptors of times to flowering to the genetic characterisation of flowering responses to photoperiod and temperature. Euphytica 92:281–286

    Article  Google Scholar 

  • Tambussi EA, Bort J, Nogues S, Guiamet JJ, Araus JL (2007) The photosynthetic role of ears in C3 cereals: metabolism, water use efficiency and contribution to grain yield. Crit Rev Plant Sci 26:1–16

    Article  CAS  Google Scholar 

  • Tanksley SD, McCouch SR (1997) Seed banks and molecular maps: unlocking genetic potential from the wild. Science 277:1063–1066

    Article  CAS  PubMed  Google Scholar 

  • Tardieu F, Schurr U (2009) ‘White paper’ on plant phenotyping. The main outcome of the EPSO workshop on plant phenotyping, Jülich, 02–03 Nov 2009, pp 1–4

    Google Scholar 

  • Tharakan PJ, Volk TA, Nowak CA, Ofezu GJ (2008) Assessment of canopy structure, light interception, and light-use efficiency of first year regrowth of shrub willow (Salix sp.). Bioenerg Res 1:229–238

    Article  Google Scholar 

  • Thomas H (2012) Plant senescence. In: Minelli A, Contrafatto G (eds) Biological science fundamentals and systematics. Encyclopedia of life support systems (EOLSS), developed under the Auspices of UNESCO. Eolss, Oxford

    Google Scholar 

  • Topp CN, Benfey PN (2012) Growth control of root architecture. In: Altman A, Hasegawa (eds) Plant biotechnology and agriculture: prospects for the 21st century. Elsevier, London, pp 373–386

    Chapter  Google Scholar 

  • Torres A, Pietragalla J (2012) Crop morphological traits. In: Pask A, Pietragalla J, Mullan D, Reynolds M (eds) Physiological breeding II: a field guide to wheat phenotyping. CIMMYT, Mexico, pp 106–112

    Google Scholar 

  • Trachsel S, Kaeppler SM, Brown KM, Lynch JP (2011) Shovelomics: high throughput phenotyping of maize (Zea mays L.) root architecture in the field. Plant Soil 341:75–87

    Article  CAS  Google Scholar 

  • Tracy SR, Roberts JA, Black CR, McNeill A, Davidson R, Mooney SJ (2010) The X-factor: visualizing undisturbed root architecture in soils using X-ray computed tomography. J Exp Bot 61:311–313

    Article  CAS  PubMed  Google Scholar 

  • Tsaftaris SA, Noutsos C (2009) Plant phenotyping with low cost digital cameras and image analytics. 4th International ICSC Symposium, Thessaloniki, Greece, 28–29 May 2009

    Google Scholar 

  • Tuberosa R (2011) Phenotyping drought-stressed crops: key concepts, issues and approaches. In: Monneveux P, Ribau JM (eds) Drought phenotyping in crops: from theory to practice. CGIAR Generation Challenge Programme, Mexico, pp 3–35

    Google Scholar 

  • Turner NC (1997) Further progress in crop water relations. Adv Agron 528:293–338

    Google Scholar 

  • Vila H, Hugalde I, Di Filippo M (2011) Estimation of leaf water potential by thermographic and spectral measurements in grapevine. Rev Investig Agropecuarias 37:46–52

    Google Scholar 

  • Vollmann J, Walter H, Sato T, Schweiger P (2011) Digital image analysis and chlorophyll metering for phenotyping the effects of nodulation in soybean. Comput Electron Agric 75:190–195

    Article  Google Scholar 

  • Wang D, Gartung J (2010) Infrared canopy temperature of early-ripening peach trees under postharvest deficit irrigation. Agric Water Manag 97:1787–1794

    Article  Google Scholar 

  • Waring RH, Cleary BD (1967) Plant moisture stress: evaluation by pressure bomb. Science 155:1248–1254

    Article  CAS  PubMed  Google Scholar 

  • Weitz J (2009) Automated phenotyping of plant root systems. EPSO workshop on plant phenotyping, Forschungszentrum Jülich, Germany, 02 Nov 2009

    Google Scholar 

  • Winterhalter L, Mistele B, Jampatong S, Schmidhalter U (2011) High throughput phenotyping of canopy water mass and canopy temperature in well-watered and drought stressed tropical maize hybrids in the vegetative stage. Eur J Agron 35:22–32

    Article  Google Scholar 

  • Wu C, Niu Z, Tang Q, Huang W (2008) Estimating chlorophyll content from hyperspectral vegetation indices: modeling and validation. Agric For Meteorol 148:1230–1241

    Article  Google Scholar 

  • Xu Y (2010) Molecular plant breeding. CABI, Wallingford

    Book  Google Scholar 

  • Xu YB, Crouch JH (2008) Marker-assisted selection in plant breeding: from publications to practice. Crop Sci 48:391–407

    Article  Google Scholar 

  • Xu R, Dai J, Luo W, Yin X, Li Y, Tai X, Han L, Chen Y, Lin L, Li G, Zou C, Dua W, Diao M (2010) A photothermal model of leaf area index for greenhouse crops. Agric For Meteorol 150:541–552

    Article  Google Scholar 

  • Yazdanbakhsh N, Fisahn J (2012) High-throughput phenotyping of root growth dynamics. Methods Mol Biol 918:21–40

    Article  CAS  PubMed  Google Scholar 

  • Yoshioka Y, Fukino N (2010) Image-based phenotyping: use of colour signature in evaluation of melon fruit colour. Euphytica 171:409–416

    Article  Google Scholar 

  • Yoshioka Y, Iwata ROH, Ninomiya S, Fukuta N (2006) Quantitative evaluation of petal shape and picotee color pattern in lisianthus by image analysis. J Am Soc Hort Sci 131:261–266

    Google Scholar 

  • Zakaluk R, Sri Ranjan R (2008) Predicting the leaf water potential of potato plants using RGB reflectance. Can Biosyst Eng 50:1–12

    Google Scholar 

  • Zhu J, Ingram PA, Benfey PN, Elich T (2011) From lab to field, new approaches to phenotyping root system architecture. Curr Opin Plant Biol 14:310–317

    Article  PubMed  Google Scholar 

  • Zia S, Romano G, Spreer W, Sanchez C, Cairns J, Araus JL, Muller J (2012) Infrared thermal imaging as a rapid tool for identifying water-stress tolerant maize genotypes of different phenology. J Agron Crop Sci 199:75–84

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Bulent Uzun .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2015 Springer India

About this chapter

Cite this chapter

Yol, E., Toker, C., Uzun, B. (2015). Traits for Phenotyping. In: Kumar, J., Pratap, A., Kumar, S. (eds) Phenomics in Crop Plants: Trends, Options and Limitations. Springer, New Delhi. https://doi.org/10.1007/978-81-322-2226-2_2

Download citation

Publish with us

Policies and ethics