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Phenomics in Crop Plants: Trends, Options and Limitations pp 285–296Cite as

High-Throughput Plant Phenotyping Platforms

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Abstract

To meet the ever-increasing demand of food and feed for the burgeoning population, we need to double our food production by 2050 with a growth rate of about 2.4 %. This needs input-responsive, resource-use-efficient and short-duration genotypes which are stable and can perform well in an array of situations. For this, integrated breeding efforts connecting genomics and phenomics together are required. While a giant leap has been made in crop genotyping in the last two decades, especially with the development of next-generation DNA sequencing, the latest developments in automation, robotics, accurate environmental control and remote sensing facilities have offered opportunities for precise field phenotyping of crop plants through state-of-the-art high-throughput plant phenotyping platforms (HTPPs). Although the initially developed platforms had limitations with regard to accuracy, speed and ground clearance, the latest HTPPs are capable of taking multiple trait measurements simultaneously that have improved data acquisition as well as provide high-throughput phenotypic data required for crop breeding programmes. A number of analysis pipelines have also been developed which are equipped with high-speed computing. This chapter describes some of the most popular HTPPs and their specific features to achieve precision phenotypes in crop plants.

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References

  • Andrade-Sanchez P, Gore MA, Heun JT, Thorp KR, Carmo-Silva AE, French AN, Salvucci ME, White JW (2014) Development and evaluation of a field-based high-throughput phenotyping platform. Funct Plant Biol 41:68–79

    Article  Google Scholar 

  • Araus JL, Cairns J (2014) Field high-throughput phenotyping—the new crop breeding frontier. Trends Plant Sci 19:52–61

    Article  CAS  PubMed  Google Scholar 

  • Araus JL, Gustavo AS, Conxita R, Dolores Serret M (2008) Breeding for yield potential and stress adaptation in cereals. Crit Rev Plant Sci 27:1–36

    Article  Google Scholar 

  • Armengaud P, Zambaux K, Hills A, Sulpice R, Pattison RJ, Blatt MR, Amtmann A (2009) EZ-Rhizo: integrated software for the fast and accurate measurement of root system architecture. Plant J 57:945–956

    Article  CAS  PubMed  Google Scholar 

  • Arvidsson S, Perez-Rodriguez 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 191:895–907

    Article  PubMed  Google Scholar 

  • Belforte G, Deboli R, Gay P, Piccarolo P, Ricauda Aimonino D (2006) Robot design and testing for greenhouse applications. Biosyst Eng 95:309–321

    Article  Google Scholar 

  • Cairns JE, Sonder K, Zaidi PH, Verhulst N, Mahuku G, Babu R, Nair SK, Das B, Govaerts B, Vinayan MT, Rashid Z, Noor JJ, Devi P, Vicente FS, Prasanna BM (2012) Maize production in a changing climate: impacts, adaptation, and mitigation strategies. Adv Agron 114:1–58

    Article  CAS  Google Scholar 

  • Cairns JE, Hellin, Sonder K, Araus JL, MacRobert JF, Thierfelder C, Prasanna BM (2013) Adapting maize production to climate change in sub-Saharan Africa. Food Sec 5:345–360

    Article  Google Scholar 

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

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  • Cobb JN, DeClerck G, Greenberg A, Clark R, McCouch S (2013) Next-generation phenotyping: requirements and strategies for enhancing our understanding of genotype–phenotype relationships and its relevance to crop improvement. Theor Appl Genet 126:867–887

    Article  PubMed Central  PubMed  Google Scholar 

  • Comar A, Burger P, 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 

  • Fabre J, Myriam D, Vincent N, Nathalie W, Anne T, Emilie G, Pascal N, Sébastien T, Catherine M, Irène H, Christine G (2011) PHENOPSIS DB: an information system for Arabidopsis thaliana phenotypic data in an environmental context. BMC Plant Biol 11:77

    Article  PubMed Central  PubMed  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 

  • Fiorani F, Schurr U (2013) Future scenarios for plant phenotyping. Annu Rev Plant Biol 64:17.1–17.25

    Article  Google Scholar 

  • Gaudin AC, Henry A, Sparks AH, Slamet-Loedin IH (2013) Taking transgenic rice drought screening to the field. J Exp Bot 64:109–118

    Article  CAS  PubMed  Google Scholar 

  • Gebbers R, Adamchuk VI (2010) Precision agriculture and food security. Science 327:828–831

    Article  CAS  PubMed  Google Scholar 

  • Golzarian M, Frick R, Rajendran K, Berger B, Roy S et al (2011) Accurate inference of shoot biomass from high-throughput images of cereal plants. Plant Methods 7:2

    Article  PubMed Central  CAS  PubMed  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 

  • Gregory PJ, Hutchison DJ, Read DB, Jenneson PM, Gilboy WB, Morton EJ (2003) Non-invasive imaging of roots with high resolution X-ray micro-tomography. Plant Soil 255:351–359

    Article  CAS  Google Scholar 

  • Grift T, Zhang Q, Kondo N, Ting KC (2008) A review of automation and robotics for the bio-industry. J Biomechatron Eng 1:37–54

    Google Scholar 

  • Harris D (1989) Comparison of 1-, 2-, and 3-parameter models. Educ Meas 8(1):35–41

    Article  Google Scholar 

  • Hartmann A, Czauderna T, Hoffmann R, Stein N, Schreiber F (2011) HTPheno: an image analysis pipeline for high throughput plant phenotyping. BMC Bioinf 12:148

    Article  Google Scholar 

  • Heinen M (1999) Analytical growth equations and their genstat 5 equivalents. Neth J Agr Sci 47:67–89

    Google Scholar 

  • Iyer-Pascuzzi AS, Simpson J, Herrera-Estrella L, Benfey PN (2010) Functional genomics of root growth and development in Arabidopsis. Curr Opin Plant Biol 12:165–171

    Article  Google Scholar 

  • Iyer-Pascuzzi AS, Sozzani R (2014) Postembryonic control of root meristem growth and development. Curr Opin Plant Biol 17:7–12

    Article  PubMed  Google Scholar 

  • Jannink JL, Lorenz AJ, Iwata H (2010) Genomic selection in plant breeding: from theory to practice. Brief Funct Genomics 9:166–177

    Article  CAS  PubMed  Google Scholar 

  • Lan Y, Zhang H, Lacey R, Hoffman W, Wu W (2009) Development of an integration sensor and instrumentation system for measuring crop conditions. Agr Eng Int 11:1–16

    Google Scholar 

  • Lorenz AJ, Chao S, Asoro FG, Heffner EL, Hayashi T, Iwata H, Smith KP, Sorrells ME, Jannink JL (2011) Genomic selection in plant breeding: knowledge and prospects. Adv Agron 110:77–123

    Article  Google Scholar 

  • Losos JB (2013) Evolutionary biology for the 21st century. PLoS Biol 11:e1001466

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  • Lynch J (1995) Root architecture and plant productivity. Plant Physiol 109:7–13

    PubMed Central  CAS  PubMed  Google Scholar 

  • McCarthy C, Hancock N, Raine S (2010) Apparatus and infield evaluations of a prototype machine vision system for cotton plant internode length measurement. J Cotton Sci 14:221–232

    CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

  • Mulayim AY, Yilmaz U, Atalay V (2003) Silhouette-based 3D model reconstruction from multiple images. IEEE Trans Syst Man Cybern B Cybern 33:582–591

    Article  CAS  PubMed  Google Scholar 

  • Phillips RL (2010) Mobilizing science to break yield barriers. Crop Sci 50:99–108

    Article  Google Scholar 

  • Rascher U, Blossfeld FF, Jahnke S, Jansen M, Kuhn AJ, Matsubara S, Martin LLA, Merchant A, Metzner R, Muller-Linow M, Nagel KA, Pieruschka R, Pinto F, Schreiber CM, Temperton VM, Thorpe MR, Dusschoten DV, Volkenburg EV, Windt W, Schurr U (2011) Non-invasive approaches for phenotyping of enhanced performance traits in bean. Funct Plant Biol 38:968–983

    Article  CAS  Google Scholar 

  • Ray DK, Muller ND, West PC, Foley JA (2013) Yield trends are insufficient to double global crop production by 2050. PLoS One 8:66428

    Article  Google Scholar 

  • Ruixiu S, Wilkerson JB, Wilhelm LR, Tompkins FD (1989) A microcomputer-based morphometer for bush-type plants. Comput Electron Agric 4:43–58

    Article  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  • Singh BB, Pratap A, Basu PS (2013) Development of climate resilient pulse varieties. In: Proceedings of National conference of plant physiology on “Current Trends in Plant Biology Research”. Organized by Directorate of Groundnut Research, Junagadh and Junagadh Agricultural University, Junagadh, 13–16 Dec 2014, pp 118–129

    Google Scholar 

  • Skirycz A, Vandenbroucke K, Clauw P, Maleux K, De Meyer B et al (2011) Survival and growth of Arabidopsis plants given limited water are not equal. Nat Biotechnol 29:212–214

    Article  CAS  PubMed  Google Scholar 

  • Tessmer OL, Jiao Y, Cruz JA, Kramer DM, Chen J (2013) Functional approach to high-throughput plant growth analysis. BMC Syst Biol 7(6):17

    Article  Google Scholar 

  • Tisne S, Serrand Y, Bach L, Gilbault E, Ben Ameur R, Balasse H, Voisin R, Bouchez D, Durand-Tardif M, Guerche P, Chareyron G, Da Rugna J, Camilleri C, Loudet O (2013) Phenoscope: an automated large-scale phenotyping platform offering high spatial homogeneity. Plant J. doi:10.1111/tpj.12131

    PubMed  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 

  • Tuberosa R, Salvi S (2006) Genomics-based approaches to improve drought tolerance of crops. Trends Plant Sci 11:405–412

    Article  CAS  PubMed  Google Scholar 

  • Walter T, Shattuck DW, Baldock R, Bastin ME, Carpenter AE, Duce S, Ellenberg J, Fraser A, Hamilton N, Pieper S, Ragan MA, Schneider JE, Tomancak P, Hériché JK (2010) Visualization of image data from cells to organisms. Nat Methods 7:26–41

    Article  Google Scholar 

  • White JW, Conley MM (2013) A flexible, low-cost cart for proximal sensing. Crop Sci 53:1646–1649

    Article  Google Scholar 

  • 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 Crops Res 133:101–112

    Article  Google Scholar 

  • Zhang X, Hause RJ, Borevitz JO (2012) Natural genetic variation for growth and development revealed by high-throughput phenotyping in Arabidopsis thaliana. G3: Genes Genomes Genet 2(l):29–34

    Article  Google Scholar 

  • Zhu T, Fang S, Li Z, Liu Y, Liao H, Yan X (2006) Quantitative analysis of 3-dimensional root architecture based on image reconstruction and its application to research on phosphorus uptake in soybean. Chin Sci Bull 51:2351–2361

    Article  CAS  Google Scholar 

  • Zude M (ed) (2009) Optical monitoring of fresh and processed agricultural crops. CRC, Boca Raton, p 457

    Google Scholar 

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Authors and Affiliations

  1. Division of Crop Improvement, ICAR – Indian Institute of Pulses Research, Kalyanpur, Kanpur, 208024, Uttar Pradesh, India

    Aditya Pratap, Rakhi Tomar, Jitendra Kumar, Vankat Raman Pandey, Suhel Mehandi & Pradeep Kumar Katiyar

Authors
  1. Aditya Pratap
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  2. Rakhi Tomar
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  3. Jitendra Kumar
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  4. Vankat Raman Pandey
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  5. Suhel Mehandi
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  6. Pradeep Kumar Katiyar
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Corresponding author

Correspondence to Aditya Pratap .

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Editors and Affiliations

  1. Division of Crop Improvement, ICAR - Indian Institute of Pulses Research, Kalyanpur, Kanpur, Uttar Pradesh, India

    Jitendra Kumar

  2. Division of Crop Improvement, ICAR - Indian Institute of Pulses Research, Kalyanpur, Kanpur, Uttar Pradesh, India

    Aditya Pratap

  3. Rabat - Instituts, International Center for Agricultural Research in the Dry Areas, Rabat, Morocco

    Shiv Kumar

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Pratap, A., Tomar, R., Kumar, J., Pandey, V.R., Mehandi, S., Katiyar, P.K. (2015). High-Throughput Plant Phenotyping Platforms. 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_19

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