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Abstract

Precise and accurate measurement of traits plays an important role in the genetic improvement of crop plants. Therefore, a lot of development has taken place in the area of phenomics in the recent past. Both forward and reverse phenomics have been evolved, which can help in identification of either the best genotype having the desirable traits or mechanism and genes that make a genotype the best. This includes development of high throughput non-invasive imaging technologies including colour imaging for biomass, plant structure, phenology and leaf health (chlorosis, necrosis); near infrared imaging for measuring tissue and soil water contents; far infrared imaging for canopy/leaf temperature; fluorescence imaging for physiological state of photosynthetic machinery; and automated weighing and watering for water usage imposing drought/salinity. These phenomics tools and techniques are paving the way in harnessing the potentiality of genomic resources in genetic improvement of crop plants. These techniques have become much more advanced and have now entered the era of high throughput integrated phenotyping platforms to provide a solution to genomics-enabled improvement and address our need of precise and efficient phenotyping of crop plants.

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References

  • Alenyà G, Dellen B, Foix S, Torras C (2012) Leaf segmentation from time-of-flight data for robotized plant probing. IEEE Robot Autom Mag 20:50–59

    Article  Google Scholar 

  • Annicchiarico P (2002) Genotype × environment interaction: challenges and opportunities for plant breeding and cultivar recommendations. FAO Plant Production and Protection Paper 74, FAO, Rome, pp 132

    Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  • Barbagallo RP, Oxborough K, Pallett KE, Baker NR (2003) ***Rapid, non-invasive screening for perturbations of metabolism and plant growth using chlorophyll fluorescence imaging. Plant Physiol 132:485–493, 37

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  • Berger B, Parent B, Tester M (2010) High throughput shoot imaging to study drought responses. J Exp Bot 61:3519–3528

    Article  CAS  PubMed  Google Scholar 

  • Bruinsma J (2003) World agriculture: towards 2015/2030: an FAO perspective. Earthscan, London

    Google Scholar 

  • Chaerle L, Lenk S, Leinonen I, Jones HG, Van Der Straeten D, Buschmann C (2009) Multi-sensor plant imaging: towards the development of a stress-catalogue. Biotechnol J 4:1152–1167

    Article  CAS  PubMed  Google Scholar 

  • Comar A, Burger PH, de Solan B, Baret F, Daumard F, Hanocq JF (2012) A semi-automatic system for high throughput phenotyping wheat cultivars in-field conditions: description and first results. Funct Plant Biol 39:914–924

    Article  Google Scholar 

  • Delseny M, Han B, Hsing YI (2010) High throughput DNA sequencing: the new sequencing revolution. Plant Sci 179:407–422

    Article  CAS  PubMed  Google Scholar 

  • Diamond J (1997) Guns, germs, and steel: the fates of human societies. Norton and Company, New York

    Google Scholar 

  • Dornbusch T, Lorrain S, Kuznetsov D, Fortier A, Liechti R, Xenarios I, Fiorani F, Rascher U, Jahnke S, Schurr U (2012) Imaging plants dynamics in heterogenic environments. Curr Opin Biotechnol 23:227–235

    Article  Google Scholar 

  • Finkel E (2009) With ‘phenomics’ plant scientists hope to shift breeding into overdrive. Science 325:380–381

    Article  CAS  PubMed  Google Scholar 

  • Fiorani F, Rascher U, Jahnke S, Schurr U (2012) Imaging plants dynamics in heterogenic environments. Curr Opin Biotechnol 23:227–235

    Article  CAS  PubMed  Google Scholar 

  • Fisher RA (1925) Statistical methods for research workers. Oliver & Boyd, Edinburgh

    Google Scholar 

  • Furbank RT, Tester M (2011) Phenomics – technologies to relieve the phenotyping bottleneck. Trends Plant Sci 16:635–644

    Article  CAS  PubMed  Google Scholar 

  • Furbank RT, von Caemmerer S, Sheehy J, Edwards G (2009) C4 rice: a challenge for plant phenomics. Funct Plant Biol 36:845–856

    Article  Google Scholar 

  • Granier C, Aguirrezabal L, Chenu K, 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 169:623–635

    Article  PubMed  Google Scholar 

  • Grime JP (1979) Plant strategies and vegetation processes. Wiley, Chichester

    Google Scholar 

  • Grime JP, Hunt R (1975) Relative growth rate: its range and adaptive significance in a local flora. J Ecol 63:393–422

    Article  Google Scholar 

  • Harris BN, Sadras VO, Tester M (2010) A water-centred framework to assess the effects of salinity on the growth and yield of wheat and barley. Plant Soil 336:377–389

    Article  CAS  Google Scholar 

  • Houle D (2010) Numbering the hairs on our heads: the shared challenge and promise of phenomics. PNAS USA 107:1793–1799

    Article  PubMed Central  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  • Huala E, Dickerman AW, Garcia-Hernandez M, Weems D, Reiser L, LaFond F, Hanley D, Kiphart D, Zhuang M, Huang W, Mueller LA, Bhattacharyya D, Bhaya D, Sobral BW, Beavis W, Meinke DW, Town CD, Somerville C, Rhee SY (2001) The Arabidopsis Information Resource (TAIR): a comprehensive database and web-based information retrieval, analysis, and visualization system for a model plant. Nucleic Acids Res 29:102–105

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  • Jansen M, Gilmer F, Biskup B, Nagel K, Rascher U, Fischbach A, Briem S, Dreissen G, Tittmann S, Braun S, De Jaeger I, Metzlaff M, Schurr U, Scharr H, Walter A (2009) Simultaneous phenotyping of leaf growth and chlorophyll fluorescence via GROWSCREEN FLUORO allows detection of stress tolerance in Arabidopsis thaliana and other rosette plants. Funct Plant Biol 36:902–914

    Article  CAS  Google Scholar 

  • Jefferies SP, Barr AR, Karakousis A, Kretschmer JM, Manning S, Chalmers KJ, Nelson JC, Islam AKMR, Langridge P (1999) Mapping of chromosome regions conferring boron toxicity tolerance in barley (Hordeum vulgare L.). Theor Appl Genet 98:1293–1303

    Article  CAS  Google Scholar 

  • Johannsen W (1911) The genotype conception of heredity. Am Nat 45(531):129–159

    Article  Google Scholar 

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

    Google Scholar 

  • Knox J, Hess T, Daccache A, Wheeler T (2012) Climate change impacts on crop productivity in Africa and South Asia. Environ Res Lett 7:034032

    Article  Google Scholar 

  • Kolukisaoglu U, Thurow K (2010) Future and frontiers of automated screening in plant sciences. Plant Sci 178:476–484

    Article  CAS  Google Scholar 

  • Langridge P, Fleury D (2011) Making the most of ‘omics’ for crop breeding. Trends Biotechnol 29:33–40

    Article  CAS  PubMed  Google Scholar 

  • Leakey ADB, Ainsworth EA, Bernacchi CJ, Rogers A, Long SP, Ort DR (2009) Elevated CO2 effects on plant carbon, nitrogen, relations: six important lessons from FACE. J Exp Bot 60:2859–2876

    Article  CAS  PubMed  Google Scholar 

  • Mahlein AK, Oerke EC, Steiner U, Dehne HW (2012) Recent advances in sensing plant diseases for precision crop protection. Eur J Plant Pathol 133:197–209

    Article  CAS  Google Scholar 

  • Mahner M, Kary M (1997) What exactly are genomes, genotypes and phenotypes? And what about phenomes? J Theor Biol 186:55–63

    Article  CAS  PubMed  Google Scholar 

  • Mayr LM, Bojanic D (2009) Novel trends in high-throughput screening. Curr Opin Pharmacol 9:580–588. doi:10.1016/j.coph.2009. 08.004

    Article  CAS  PubMed  Google Scholar 

  • Meinke DW, Cherry JM, Dean C, Rounsley SD, Koornneef M (1998) Arabidopsis thaliana: a model plant for genome analysis. Science 282:662–682

    Article  CAS  PubMed  Google Scholar 

  • Miyao A, Iwasaki Y, Kitano H, Itoh J, Maekawa M, Murata K, Yatou O, Nagato Y, Hirochika H (2007) A large-scale collection of phenotypic data describing an insertional mutant population to facilitate functional analysis of rice genes. Plant Mol Biol 63:625–635

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  • Noldus LPJJ, Spink AJ, Tegelenbosch RAJ (2001) Etho vision: a versatile video tracking system for automation of behavioral experiments. Behav Res Methods 3:398–414

    Article  Google Scholar 

  • Pearson CH, Ernst SM, Barbarick KA, Hatfield JL, Peterson GA, Buxton DR (2008) Agronomy Journal turns one hundred. Agron J 100:1–8

    Article  Google Scholar 

  • Pieruschka R, Klimov D, Kolber Z, Berry JA (2010) Continuous measurements of the effects of cold stress on photochemical efficiency using laser induced fluorescence transient (LIFT) approach. Funct Plant Biol 37:395–402

    Article  Google Scholar 

  • Poorter H, Pot CS, Lambers H (1988) The effect of an elevated atmospheric CO2 concentration on growth, photosynthesis and respiration of Plantago major. Physiol Plant 73:553–559

    Article  CAS  Google Scholar 

  • Poorter H, Remkes C, Lambers H (1990) Carbon and nitrogen economy of 24 wild species differing in relative growth rate. Plant Physiol 94:621–627

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  • Poorter H, Niinemets Ü, Walter A, Fiorani F, Schurr U (2010) A method to construct dose–response curves for a wide range of environmental factors and plant traits by means of a meta-analysis of phenotypic data. J Exp Bot 61:2043–2055

    Article  CAS  PubMed  Google Scholar 

  • Rascher U, Pieruschka R (2008) Spatio-temporal variations of photosynthesis: the potential of optical remote sensing to better understand and scale light use efficiency and stresses of plant ecosystems. Precis Agric 9:355–366

    Article  Google Scholar 

  • Reich PB, Walters MB, Ellsworth DS (1992) Leaf life-span in relation to leaf, plant, and stand characteristics among diverse ecosystems. Ecol Monogr 62:365–392

    Article  Google Scholar 

  • Riano-Pachon DM, Nagel A, Neigenfind J, Wagner R, Basekow R, Weber E, Mueller-Roeber B, Diehl S, Kersten B (2009) GabiPD: the GABI primary database – plant integrative ‘omics’ database. Nucleic Acids Res 37:D954–D959

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  • Ruiz-Garcia L, Lunadei L, Barreiro P, Robla JI (2009) A review of wireless sensor technologies and applications in agriculture and food industry: state of the art and current trends. Sensors (Basel, Switzerland) 9:4728–4750

    Article  Google Scholar 

  • Schnurbusch T, Hayes JE, Sutton TJ (2010) Boron toxicity tolerance in wheat and barley: Australian perspectives. Breed Sci 60:297–304

    Article  CAS  Google Scholar 

  • Scotford IM, Miller PCH (2005) Applications of spectral reflectance techniques in northern European cereal production: a review. Biosyst Eng 90:235–250

    Article  Google Scholar 

  • Sirault XRR, James RA, Furbank RT (2009) A new screening method for osmotic component of salinity tolerance in cereals using infrared thermography. Funct Plant Biol 36:970–977

    Article  CAS  Google Scholar 

  • Solomon S, Qin D, Manning M, Chen Z, Marquis M, Averyt KB, Tignor M, Miller HL (2007) Climate change 2007: the physical science basis: contribution of working group I to the fourth assessment report of the intergovernmental panel on climate change. Cambridge Univ Press, New York, pp 1–8

    Google Scholar 

  • Soulé M (1967) Phenetics of natural populations I. Phenetic relationships of insular populations of the side-blotched lizard. Evolution 21:584–591

    Article  Google Scholar 

  • Sticklen MB (2007) Feedstock crop genetic engineering for alcohol fuels. Crop Sci 47:2238–2248

    Article  CAS  Google Scholar 

  • Suzuki DT, Griffiths AJF, Lewontin RC (1981) An introduction to genetic analysis, 2nd edn. W H Freeman, New York

    Google Scholar 

  • Swarbrick PJ, Schulze-Lefert P, Scholes JD (2006) The metabolic consequences of susceptibility and the activation of race specific or broad spectrum resistance pathways in barley leaves challenged with the powdery mildew fungus. Plant Cell Environ 29:1061–1076

    Article  CAS  PubMed  Google Scholar 

  • Tester M, Langridge P (2010) Breeding technologies to increase crop production in a changing world. Science 327:818–822

    Article  CAS  PubMed  Google Scholar 

  • Wheeler T, von Braun J (2013) Climate change impacts on global food security. Science 341:508–513

    Article  CAS  PubMed  Google Scholar 

  • Walter A, Studer B, Kolliker R (2012) Advanced phenotyping offers opportunities for improved breeding of forage and turf species. Ann Bot 110:1271–1279

    Article  PubMed Central  PubMed  Google Scholar 

  • Woo N, Badger MR, Pogson BJ (2008) A rapid non-invasive procedure for quantitative assessment of drought survival using chlorophyll fluorescence. Plant Methods 4:27

    Article  PubMed Central  PubMed  Google Scholar 

  • Wright IJ, Reich PB, Westoby M, Ackerly DD, Baruch Z, Bongers F, Cavender-Bares J, Chapin T, Cornelissen JHC, Diemer M, Flexas J, Garnier E, Groom PK, Gulias J, Hikosaka K, Lamont BB, Lee T, Lee W, Lusk C, Midgley JJ, Navas ML, Niinemets Ü, Oleksyn J, Osada N, Poorter H, Poot P, Prior L, Pyankov VI, Roumet C, Thomas SC, Tjoelker MG, Veneklaas EJ, Villar R (2004) The worldwide leaf economics spectrum. Nature 428:821–827

    Article  CAS  PubMed  Google Scholar 

  • Zamir D (2013) Where Have All the Crop Phenotypes Gone? PLoS Biol 11(6): e1001595. doi:10.1371/journal.pbio.1001595

  • Ziska LH, Bunce JA (2007) Predicting the impact of changing CO2 on crop yields: some thoughts on food. New Phytol 175:607–618

    Article  CAS  PubMed  Google Scholar 

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Correspondence to Jitendra Kumar .

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Kumar, J., Pratap, A., Kumar, S. (2015). Plant Phenomics: An Overview. 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_1

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