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Perspectives in High-Throughput Phenotyping of Qualitative Traits at the Whole-Plant Level

  • Oksana Sytar
  • Marek Zivcak
  • Katarina Olsovska
  • Marian Brestic
Chapter

Abstract

Recent advances in technology have enabled the rapid development of high-throughput automated and semi-automated field and laboratory phenotyping platforms worldwide. In this review, we discuss possible ways of matching the qualitative traits of the above-ground parts of crop plants, also defining the target traits and possible approaches that would be useful in automated phenotyping systems. Optical tools based on light reflectance are presented as a high-throughput and low-cost alternative to some destructive analytical methods. Special attention is paid to hyperspectral imaging and its integration in high-throughput phenotyping systems, as well as its special applications for the assessment of specific plant material traits associated with food quality.

Keywords

Phenotyping Phenomics Hyperspectral imaging Qualitative traits 

Abbreviations

DESI-MSI

Desorption electrospray ionization mass spectrometry imaging

DNA

Deoxyribonucleic acid

ESI

Electrospray ionization

FAO

Food and Agriculture Organization of the United Nations

HPLC-MS

High-performance liquid chromatography–mass spectrometry

HSI

Hyperspectral imaging

IR light

Infrared light

LEDI

Lettuce decay indices

MALDI

Matrix-assisted laser desorption ionization

MS

Mass spectrometry

MSI

Mass spectrometry imaging

NIR light

Near-infrared light

NMR

Nuclear magnetic resonance

PCA

Principal component analysis

PMT

Photomultiplier tube

QTL

Quantitative trait locus

RGB camera

Red–green–blue camera

SVDD

Support vector data description

SWIR light

Short-wave infrared light

TIR light

Thermal infrared light

TNC

Total nitrogen content

TOF

Time-of-flight (mass spectrometer)

Notes

Acknowledgments

This work was supported by the research project of the Scientific Grant Agency of the Slovak Republic VEGA- 1-0923-16 and APVV-15-0721.

Competing Interests

The authors declare no financial conflict of interests.

Conflict of Interests

There are no conflicts of interest for this article.

Ethical Approval

The presented research does not require ethical approval.

References

  1. Abdel-Rahman EM, Fethi BA, van den Berg M (2010) Estimation of sugarcane leaf nitrogen concentration using in situ spectroscopy. Int J Appl Earth Obs Geoinf 12(1):S52–S57. https://doi.org/10.1016/j.jag.2009.11.003 CrossRefGoogle Scholar
  2. Agarwal UP, Atalla RH (2010) Vibrational spectroscopy. In: Heitner C, Dimmel DR, Schmidt JA (eds) Lignin and Lignans: advances in chemistry. CRC Press, Boca Raton, pp 103–136CrossRefGoogle Scholar
  3. Alexandratos N, Bruinsma J (2012) Report. Title: World agriculture towards 2030/2050: the 2012 revision. Authors: Publisher: Food and Agriculture Organization of the United Nations. Institution: FAO. Report number: ESA Working Paper No. 12–03Google Scholar
  4. Almeida TIR, De Souza Filho CR (2004) Principal component analysis applied to feature-oriented band ratios of hyperspectral data: a tool for vegetation studies. Int J Remote Sens 25:5005–50023CrossRefGoogle Scholar
  5. Araus JL, Cairns JE (2014) Field high-throughput phenotyping: the new crop breeding frontier. Trends Plant Sci 19(1):52–61. https://doi.org/10.1016/j.tplants.2013.09.008 PubMedCrossRefGoogle Scholar
  6. Ariana DP, Lu RF (2010) Evaluation of internal defect and surface color of whole pickles using hyperspectral imaging. J Food Eng 96(4):583–590. https://doi.org/10.1016/j.jfoodeng.2009.09.005 CrossRefGoogle Scholar
  7. Arngren M, Hansen PW, Eriksen B, Larsen J, Larsen R (2011) Analysis of pregerminated barley using hyperspectral image analysis. J Agric Food Chem 59:11385–11394. https://doi.org/10.1021/jf202122y PubMedCrossRefGoogle Scholar
  8. Baker NR (2008) Chlorophyll fluorescence: a probe of photosynthesis in vivo. Annu Rev Plant Biol 59:89–113. https://doi.org/10.1146/annurev.arplant.59.032607.092759
  9. Bakker MG, Manter DK, Sheflin AM, Weir TL, Vivanco JM (2012) Harnessing the rhizosphere microbiome through plant breeding and agricultural management. Plant Soil 360:1. https://doi.org/10.1007/s11104-012-1361-x
  10. Baranowski P, Mazurek W, Wozniak J, Majewska U (2012) Detection of early bruises in apples using hyperspectral data and thermal imaging. J Food Eng 110:345–355. https://doi.org/10.1016/j.jfoodeng.2011.12.038 CrossRefGoogle Scholar
  11. Bauriegel E, Herppich WB (2014) Hyperspectral and chlorophyll fluorescence imaging for early detection of plant diseases, with special reference to Fusarium spec. Infections on wheat. Agriculture 4:32–57. https://doi.org/10.3390/agriculture4010032 CrossRefGoogle Scholar
  12. Bauriegel E, Brabandt H, Gärber U, Herppich WB (2014) Chlorophyll fluorescence imaging to facilitate breeding of Bremia lactucae-resistant lettuce cultivars. Comput Electron Agric 105:74–82. https://doi.org/10.1016/j.compag.2014.04.010 CrossRefGoogle Scholar
  13. Beebe SE, Rao IM, Blair MW, Acosta-Gallegos JA (2013) Phenotyping common beans for adaptation to drought. Front Physiol 4:35. https://doi.org/10.3389/fphys.2013.00035 PubMedPubMedCentralCrossRefGoogle Scholar
  14. Blackburn GA (2007) Hyperspectral remote sensing of plant pigments. J Exp Bot 58(4):855–867. https://doi.org/10.1093/jxb/erl123 PubMedCrossRefGoogle Scholar
  15. Brestic M, Zivcak M, Datko M, Sytar O, Olsovska K, Shao H (2015) Novel resistance mechanism of barley Chlorina f104 antenna mutant against photoinhibition: possible role of new identified chloroplastic cpNrp protein. Theoret Exp Plant Physiol 27(1):75–85. https://doi.org/10.1007/s40626-015-0033-7 CrossRefGoogle Scholar
  16. Brotman Y, Riewe D, Lisec J, Meyer RC, Willmitzer L, Altmann T (2011) Identification of enzymatic and regulatory genes of plant metabolism through QTL analysis in Arabidopsis. J Plant Physiol 168(12):1387–1394. https://doi.org/10.1016/j.jplph.2011.03.008 PubMedCrossRefGoogle Scholar
  17. Brunetti C, George RM, Tattini M, Field K, Davey MP (2013) Metabolomics in plant environmental physiology. J Exp Bot 64(13):4011–4020. https://doi.org/10.1093/jxb/ert244 PubMedCrossRefGoogle Scholar
  18. Bucksch A, Burridge J, York LM, Das A, Nord E, Weitz JS, Lynch JP (2014) Image-based high-throughput field phenotyping of crop roots. Plant Physiol 166:470–486. https://doi.org/10.1104/pp.114.243519 PubMedPubMedCentralCrossRefGoogle Scholar
  19. Carreno-Quintero N, Bouwmeester HJ, Keurentjes JJ (2013) Genetic analysis of metabolome-phenotype interactions: from model to crop species. Trends Genet 29(1):41–50. https://doi.org/10.1016/j.tig.2012.09.006 PubMedCrossRefGoogle Scholar
  20. Chan JC-W, Paelinckx D (2008) Evaluation of random forest and adaboost tree-based ensemble classification and spectral band selection for ecotope mapping using airborne hyperspectral imagery. Remote Sens Environ 112:2999–3011. https://doi.org/10.1016/j.rse.2008.02.011 CrossRefGoogle Scholar
  21. Chaurand P, Schwartz SA, Caprioli RM (2002) Imaging mass spectrometry: a new tool to investigate the spatial organization of peptides and proteins in mammalian tissue sections. Curr Opin Chem Biol 6(5):676–681PubMedCrossRefGoogle Scholar
  22. Chen YR, Chao K, Kim MS (2002) Machine vision technology for agricultural applications. Comput Electron Agric 36(2):173–191. https://doi.org/10.1016/S0168-1699(02)00100-X CrossRefGoogle Scholar
  23. Chen P, Yan K, Shao H, Zhao S (2013) Physiological mechanisms for high salt tolerance in wild soybean (Glycine soja) from Yellow River delta, China: photosynthesis, osmotic regulation, ion flux and antioxidant capacity. PLoS One 8(12):e83227. https://doi.org/10.1371/journal.pone.0083227 PubMedPubMedCentralCrossRefGoogle Scholar
  24. Chen D, Neumann K, Friedel S, Kilian B, Chen M, Altmann T, Klukas C (2014) Dissecting the phenotypic components of crop plant growth and drought responses based on high-throughput image analysis. Plant Cell 26(12):4636–4655. https://doi.org/10.1105/tpc.114.129601 PubMedPubMedCentralCrossRefGoogle Scholar
  25. 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. https://doi.org/10.1007/s00122-013-2066-0
  26. Coops NC, Stone C, Culvenor DS, Chisholm LA, Merton RN (2003) Chlorophyll content in eucalypt vegetation at the leaf and canopy scales as derived from high spectral resolution data. Tree Physiol 23:23–31. https://doi.org/10.1093/treephys/23.1.23 PubMedCrossRefGoogle Scholar
  27. Deepak M, Fauch L, Keski-Saari S, Kontunen-Soppela S, Oksanen E, Keinanen M (2015) Variation in the secondary compounds of the silver birch leaves by chemical and imaging techniques. IPAP 2015, International Plant and Algal Phenomics Meeting, 27th–30th June 2015, Prague, Czech Republic, 37Google Scholar
  28. Dignat G, Welcker C, Sawkins M, Ribaut JM, Tardieu F (2013) The growths of leaves, shoots, roots and reproductive organs partly share their genetic control in maize plants. Plant, Cell Environ 36(6):1105–1119. https://doi.org/10.1111/pce.12045 CrossRefGoogle Scholar
  29. Elmasry G, Kamruzzaman M, Sun D-W, Allen P (2012) Principles and applications of hyperspectral imaging in quality evaluation of agro-food products: a review. Crit Rev Food Sci Nutr 52(11):999–1023PubMedCrossRefGoogle Scholar
  30. Fabio F, Ulrich S (2013) Future scenarios for plant phenotyping. Annu Rev Plant Biol 64(1):267CrossRefGoogle Scholar
  31. Fahlgren N, Gehan MA, Baxter I (2015) Lights, camera, action: high-throughput plant phenotyping is ready for a close-up. Curr Opin Plant Biol 24:93–99. https://doi.org/10.1016/j.pbi.2015.02.006 PubMedCrossRefGoogle Scholar
  32. Ferri CP, Formaggio AR, Schiavinato MA (2004) Narrow band spectral indexes for chlorophyll determination in soybean canopies [Glycinemax (L.) Merril]. Braz J Plant Physiol 16:131–136. https://doi.org/10.1590/S1677-04202004000300002 CrossRefGoogle Scholar
  33. Fiorani F, Schurr U (2013) Future scenarios for plant phenotyping. Annu Rev Plant Biol 64:267–291. https://doi.org/10.1146/annurev-arplant-050312-120137 PubMedCrossRefGoogle Scholar
  34. Firrao G, Torelli E, Gobbi E, Raranciuc S, Bianchi G, Locci R (2010) Prediction of milled maize fumonisin contamination by multispectral image analysis. J Cereal Sci 52(2):327–330. https://doi.org/10.1016/j.jcs.2010.06.017 CrossRefGoogle Scholar
  35. Fischer KS, Lafitte R, Fukai S, Atlin G, Hardy B (eds) (2003) Breeding rice for drought-prone environments. IRRI, Los Baños, The Philippines, 98 pp. knowledgebank.irri. org/drought/drought.pdfGoogle Scholar
  36. Fumio M, Okazaki Y, Oikawa A, Kusano M, Nakabayashi R, Kikuchi J, Yonemaru J-I, Ebana K, Yano M, Saito K (2012) Dissection of genotype–phenotype associations in rice grains using metabolome quantitative trait loci analysis. Plant J 70(4):624–636. https://doi.org/10.1111/j.1365-313X.2012.04903.x CrossRefGoogle Scholar
  37. Gamalero E, Trotta A, Massa N, Copetta A, Martinotti MG, Berta G (2004) Impact of two fluorescent pseudomonads and an arbuscular mycorrhizal fungus on tomato plant growth, root architecture and P acquisition. Mycorrhiza 14:233–251Google Scholar
  38. Gaston E, Frias JM, Cullen PJ, O’Donnell CP, Gowen AA (2010) Prediction of polyphenol oxidase activity using visible near-infrared hyperspectral imaging on mushroom (Agaricus bisporus) caps. J Agric Food Chem 58:6226–6233. https://doi.org/10.1021/jf100501q PubMedCrossRefGoogle Scholar
  39. Gay A, Thomas H, Roca M, James C, Taylor J, Rowland J, Ougham H (2008) Nondestructive analysis of senescence in mesophyll cells by spectral resolution of protein synthesis-dependent pigment metabolism. New Phytol 179(3):663–674. https://doi.org/10.1111/j.1469-8137.2008.02412.x PubMedCrossRefGoogle Scholar
  40. Gitelson AA, Merzlyak MN, Chivkunova OB (2001) Optical properties and nondestructive estimation of anthocyanin content in plant leaves. Photochem Photobiol 74:38–45. https://doi.org/10.1562/0031-8655(2001)0740038OPANEO2.0.CO2 PubMedCrossRefGoogle Scholar
  41. Goetz AFH, Vane G, Solomon JE, Rock BN (1985) Imaging spectrometry for earth remote-sensing. Science 228:1147–1153. https://doi.org/10.1126/science.228.4704.1147 PubMedCrossRefGoogle Scholar
  42. Gowen AA, O’Donnell CP, Cullen PJ, Downey G, Frias JM (2007) Hyperspectral imaging – an emerging process analytical tool for food quality and safety control. Trends Food Sci Technol 18(12):590–598. https://doi.org/10.1016/j.tifs.2007.06.001 CrossRefGoogle Scholar
  43. Hall AE (2012) Phenotyping cowpeas for adaptation to drought. Front Physiol 3:155. https://doi.org/10.3389/fphys.2012.00155 PubMedPubMedCentralCrossRefGoogle Scholar
  44. Hartmann A, Czaudern T, Hoffmann R, Stein N, Schreiber F (2011) HTPheno. An image analysis pipeline for high-throughput plant phenotyping. BMC Bioinf 12:148. https://doi.org/10.1186/1471-2105-12-148 CrossRefGoogle Scholar
  45. Hill CB, Julian D, Taylor JD, James E, Mather D, Langridge P, Bacic A, Roessne U (2015) Detection of QTL for metabolic and agronomic traits in wheat with adjustments for variation at genetic loci that affect plant phenology. Plant Sci 233:143–154. https://doi.org/10.1016/j.plantsci.2015.01.008 PubMedCrossRefGoogle Scholar
  46. Hillnhütter C, Mahlein A-K, Sikora RA, Oerke E-C (2011) Remote sensing to detect plant stress induced by Heterodera schachtii and Rhizoctonia solani in sugar beet fields. Field Crop Res 122:70–77. https://doi.org/10.1016/j.fcr.2011.02.007 CrossRefGoogle Scholar
  47. Hölscher D, Shroff R, Knop K, Gottschaldt M, Crecelius A, Schneider B, Heckel DG, Schubert US, Svatoš A (2009) Matrix-free UV-laser desorption/ionization (LDI) mass spectrometric imaging at the single-cell level: distribution of secondary metabolites of Arabidopsis thaliana and Hypericum species. Plant J 60:907–918. https://doi.org/10.1111/j.1365-313X.2009.04012.x PubMedCrossRefGoogle Scholar
  48. Jackson SN, Wang HY, Woods AS, Ugarov M, Egan T, Schultz JA (2005) Direct tissue analysis of phospholipids in rat brain using MALDI-TOFMS and MALDI-ion mobility-TOFMS. J Am Soc Mass Spectrom 16(2):133–138PubMedCrossRefGoogle Scholar
  49. Jayas DS, Singh CB, Paliwal J (2010) Classification of wheat kernels using near-infrared reflectance hyperspectral imaging. In: Sun D-W (ed) Hyperspectral imaging for food quality analysis and control, 1st edn. Academic/Elsevier, San Diego, pp 449–470CrossRefGoogle Scholar
  50. Jiseok L, Philipp G, Manfred K, Baret J-C (2013) Micro-optical lens array for fluorescence detection in droplet-based microfluidics. Lab Chip 13:1472–1475. https://doi.org/10.1039/C3LC41329B CrossRefGoogle Scholar
  51. Khakimov B, Bak S, Engelsen SB (2013) High-throughput cereal metabolomics: current analytical technologies, challenges and perspectives. J Cereal Sci 59(3):393–418. https://doi.org/10.1016/j.jcs.2013.10.002 CrossRefGoogle Scholar
  52. Kuska M, Wahabzada M, Leucker M, Dehne H-W, Kersting K, Oerke E-C, Steiner U, Mahlein A-K (2015) Hyperspectral phenotyping on the microscopic scale: towards automated characterization of plant-pathogen interactions. Plant Methods 11:28. https://doi.org/10.1186/s13007-015-0073-7PubMedPubMedCentralCrossRefGoogle Scholar
  53. Larsen KL, Barsberg S (2011) Environmental effects on the lignin model monomer, vanillyl alcohol, studied by Raman spectroscopy. J Phys Chem B 115:11470–11480. https://doi.org/10.1021/jp203910h PubMedCrossRefGoogle Scholar
  54. Le Maire G, François C, Soudani K, Berveiller D, Pontailler JY, Bréda N, Genet H, Davi H, Dufrêne E (2008) Calibration and validation of hyperspectral indices for the estimation of broadleaved forest leaf chlorophyll content, leaf mass per area, leaf area index and leaf canopy biomass. Remote Sens Environ 112(10):3846–3864. https://doi.org/10.1016/j.rse.2008.06.005 CrossRefGoogle Scholar
  55. Lee YJ, Perdian DC, Song Z, Yeung ES, Nikolau BJ (2012) Use of mass spectrometry for imaging metabolites in plants. Plant J 70:81–95. https://doi.org/10.1111/j.1365-313X.2012.04899.x PubMedCrossRefGoogle Scholar
  56. Li L, Zhang Q, Huang D (2014) A review of imaging techniques for plant phenotyping. Sensors 14:20079–20111. https://doi.org/10.3390/s141120078 Google Scholar
  57. Lim J, Gruner P, Konrad M, Baret JC (2013) Micro-optical lens array for fluorescence detection in dropletbased microfluidics. Lab on a Chip 13(8):1472–1475. https://doi.org/10.1039/c3lc41329b
  58. Lobet G, Draye X, Périlleux C (2013) An online database for plant image analysis software tools. Plant Methods 9:38. https://doi.org/10.1186/1746-4811-9-38 PubMedPubMedCentralCrossRefGoogle Scholar
  59. Lorente D, Aleixos N, Gómez-Sanchis J, Cubero S, García-Navarrete OL, Blasco J (2012) Recent advances and applications of hyperspectral imaging for fruit and vegetable quality assessment. Food Bioprocess Technol 5:1121. https://doi.org/10.1007/s11947-011-0725-1 CrossRefGoogle Scholar
  60. Lorente D, Aleixos N, Gómez-Sanchis J, Cubero S, Blasco J (2013) Selection of optimal wavelength features for decay detection in citrus fruit using the ROC curve and neural networks. Food Bioprocess Technol 6:530–541. https://doi.org/10.1007/s11947-011-0737-x CrossRefGoogle Scholar
  61. Lupoi JS, Smith EA (2012) Characterization of woody and herbaceous biomasses lignin composition with 1064 nm dispersive multichannel Raman spectroscopy. Appl Spectrosc 66:903–910. https://doi.org/10.1366/12-06621 PubMedCrossRefGoogle Scholar
  62. Lupoi JS, Gjersing E, Davis Mark F (2015) Evaluating lignocellulosic biomass, its derivatives, and downstream products with Raman spectroscopy. Front Bioeng Biotechnol 3:50. https://doi.org/10.3389/fbioe.2015.00050 PubMedPubMedCentralCrossRefGoogle Scholar
  63. McCouch SR, McNally KL, Wang W, Sackville Hamilton R (2012) Genomics of gene banks: a case study in rice. Am J Bot 99(2):407–423. https://doi.org/10.3732/ajb.1100385 PubMedCrossRefGoogle Scholar
  64. Mahlein AK, Steiner U, Dehne HW, Oerke EC (2010) Spectral signatures of sugar beet leaves for the detection and differentiation of diseases. Precis Agric 11:413–431. https://doi.org/10.1016/j.rse.2012.09.019 CrossRefGoogle Scholar
  65. Mahlein A-K, Oerke E-C, Steiner U, Dehne H-W (2012a) Recent advances in sensing plant diseases for precision crop protection. Eur J Plant Pathol 133:197–120. https://doi.org/10.1007/s10658-011-9878-z CrossRefGoogle Scholar
  66. Mahlein A-K, Steiner U, Hillnhütter C, Dehne H-W, Oerke E-C (2012b) Hyperspectral imaging for small-scale analysis of symptoms caused by different sugar beet diseases. Plant Мethods 8:3. https://doi.org/10.1186/1746-4811-8-3 Google Scholar
  67. Mahlein A-K, Rumpf T, Welke P, Dehne H-W, Plümer L, Steiner U, Oerke E-C (2013) Development of spectral indices for detecting and identifying plant diseases. Remote Sens Environ 128:21–30. https://doi.org/10.1016/j.rse.2012.09.019 CrossRefGoogle Scholar
  68. Matos DA, Cole BJ, Whitney IP, MacKinnon KJ-M, Kay SA, Hazen SP (2014) Daily changes in temperature, not the circadian clock, regulate growth rate in Brachypodium distachyon. PLoS One 9:e100072. https://doi.org/10.1371/journal.pone.0100072 PubMedPubMedCentralCrossRefGoogle Scholar
  69. Matros A, Mock H-P (2013) Mass spectrometry based imaging techniques for spatially resolved analysis of molecules. Front Plant Sci 4(19):89. https://doi.org/10.3389/fpls.2013.00089 PubMedPubMedCentralGoogle Scholar
  70. Matsuda F, Okazaki Y, Oikawa A, Kusano M, Nakabayashi R, Kikuchi J, Yonemaru J, Ebana K, Yano M, Saito K (2012) Dissection of genotype-phenotype associations in rice grains using metabolome quantitative trait loci analysis. Plant J 70(4):624–636. https://doi.org/10.1111/j.1365-313X.2012.04903.x PubMedCrossRefGoogle Scholar
  71. Mensack MM, Fitzgerald VK, Ryan EP, Lewis MR, Thompson HJ, Brick MA (2010) Evaluation of diversity among common beans (Phaseolus vulgaris L.) from two centers of domestication using ‘omics’ technologies. BMC Genomics 11:686. https://doi.org/10.1186/1471-2164-11-686 PubMedPubMedCentralCrossRefGoogle Scholar
  72. Merzlyak MN, Solovchenko AE, Gitelson AA (2003) Reflectance spectral features and non-destructive estimation of chlorophyll, carotenoid and anthocyanin content in apple fruit. Postharvest Biol Technol 27(2):197–211. https://doi.org/10.1016/S0925-5214(02)00066-2 CrossRefGoogle Scholar
  73. Messina CD, Podlich D, Dong ZS, Samples M, Cooper M (2011) Yield-trait performance landscapes: from theory to application in breeding maize for drought tolerance. J Exp Bot 62:855–868. https://doi.org/10.1093/jxb/erq329 PubMedCrossRefGoogle Scholar
  74. Meyer MW, Lupoi JS, Smith EA (2011) 1064 nm dispersive multichannel Raman spectroscopy for the analysis of plant lignin. Anal Chim Acta 706:164–170. https://doi.org/10.1016/j.aca.2011.08.031 PubMedCrossRefGoogle Scholar
  75. Millera OJ, Harrakb AE, Mangeatb T, Bareta J-C, Frenza L, Debsa BE, Mayota E, Samuelsc ML, Rooneyd EK, Dieue P, Galvand M, Linkc DR, Griffiths AD (2012) High-resolution dose–response screening using droplet-based microfluidics. PNAS 109(2):378–383. https://doi.org/10.1073/pnas.1113324109 CrossRefGoogle Scholar
  76. Mogensen KB, Klank H, Kutter JP (2004) Recent developments in detection for microfluidic systems. Electrophoresis 25:3498–3512PubMedCrossRefGoogle Scholar
  77. Monneveux P, Ruilian J, Misra SC (2012) Phenotyping wheat for adaptation to drought. Front Physiol 3:429. https://doi.org/10.3389/fphys.2012.00429 PubMedPubMedCentralCrossRefGoogle Scholar
  78. Moore CR, Johnson LS, Kwak I-Y, Livny M, Broman KW, Spalding EP (2013) High-throughput computer vision introduces the time axis to a quantitative trait map of a plant growth response. Genetics 195:1077–1086. https://doi.org/10.1534/genetics.113.153346 PubMedPubMedCentralCrossRefGoogle Scholar
  79. Nadella KD, Marla SS, Ananda Kumar P (2012) Metabolomics in agriculture. OMICS: J Integr Biol 16(4):149–159. https://doi.org/10.1089/omi.2011.0067 CrossRefGoogle Scholar
  80. Neilson EH, Edwards AM, Blomstedt CK, Berger B, Møller BL, Gleadow RM (2015) Utilization of a highthroughput shoot imaging system to examine the dynamic phenotypic responses of a C4 cereal crop plant to nitrogen and water deficiency over time. J Exp Bot 66:1817–1832PubMedPubMedCentralCrossRefGoogle Scholar
  81. Nicolai BM, Beullens K, Bobelyn E, Peirs A, Saeys W, Theron KI et al (2007) Nondestructive measurement of fruit and vegetable quality by means of NIR spectroscopy: a review. Postharvest Biol Technol 46(2):99–118. https://doi.org/10.1016/j.postharvbio.2007.06.024 CrossRefGoogle Scholar
  82. Noah AD, Macho AP, Zipfel C (2015) Plant PRRs and the activation of innate immune signaling. Mol Cell 54:263–272Google Scholar
  83. Okogbenin E, Setter TL, Ferguson M, Mutegi R, Ceballos H, Olasanmi B, Fregene M (2013) Phenotypic approaches to drought in cassava: review. Front Physiol 4:93. https://doi.org/10.3389/fphys.2013.00093 PubMedPubMedCentralCrossRefGoogle Scholar
  84. Ossipov V, Ossipova S, Bykov V, Oksanen E, Koricheva J, Haukioja E (2008) Application of metabolomics to genotype and phenotype discrimination of birch trees grown in a long-term openfield experiment. Metabolomics 4:39–51. https://doi.org/10.1007/s11306-007-0097-8 CrossRefGoogle Scholar
  85. Penuelas J, Filella I, Lloret P, Munoz F, Vilajeliu M (1995) Reflectance assessment of mite effects on apple trees. Int J Remote Sens 16(14):2727–2733. https://doi.org/10.1080/01431169508954588. PMCID: PMC3360494CrossRefGoogle Scholar
  86. Peukert M, Thiel J, Peshev D, Weschke W, Van den Ende W, Mock HP, Matros A (2014) Spatio-temporal dynamics of fructan metabolism in developing barley grains. Plant Cell 26:3728–3744. https://doi.org/10.1105/tpc.114.130211 PubMedPubMedCentralCrossRefGoogle Scholar
  87. Pires NMM, Dong T, Hanke U, Hoivik N (2014) Recent developments in optical detection technologies in lab-ona-chip devices for biosensing applications. Sensors 14(8):15458–15479. https://doi.org/10.3390/s140815458 PubMedPubMedCentralCrossRefGoogle Scholar
  88. Poorter H, Fiorani F, Stitt M, Schurr U, Finck A, Gibon Y, Usadel B, Munns R, Atkin OK, Pons TL (2012) The art of growing plants for experimental purposes: a practical guide for the plant biologist (review). Funct Plant Biol 39(11):821–838. https://doi.org/10.1071/FP12028 CrossRefGoogle Scholar
  89. Pytela J, Panzarova K, Chmelik D, Trtilek M (2015) Non-invasive spectral analysis of nitrogen content in barley leaves. IPAP 2015, International Plant and Algal Phenomics Meeting, 27th–30th June 2015, Prague, Czech Republic, P.44–45Google Scholar
  90. Rajendran K, Tester M, Roy SJ (2009) Quantifying the three main components of salinity tolerance in cereals. Plant Cell Environ 32:237–249. https://doi.org/10.1111/j.1365-3040.2008.01916.x PubMedCrossRefGoogle Scholar
  91. Ravi I, Uma S, Vaganan MM, Mustaffa MM (2013) Phenotyping bananas for drought resistance. Front Physiol 4:9. https://doi.org/10.3389/fphys.2013.00009 PubMedPubMedCentralCrossRefGoogle Scholar
  92. Roessner U, Bacic A (2009) Metabolomics in plant research. Aust Biochem 40(3):9–20Google Scholar
  93. Roessner U,·Willmitzer L, Fernie AR (2002) Metabolic profiling and biochemical phenotyping of plant systems. Plant Cell Rep 21:189–196. doi: https://doi.org/10.1007/s00299-002-0510-8 CrossRefGoogle Scholar
  94. Römer C, Wahabzada M, Ballvora A, Pinto F, Rossini M, Panigada C, Behmann J, Léon J, Thurau C, Bauckhage C et al (2012) Early drought stress detection in cereals: simplex volume maximisation for hyperspectral image analysis. Funct Plant Biol 39:878. https://doi.org/10.1071/FP12060 CrossRefGoogle Scholar
  95. Schaffert RE, Paulo EPA, Duarte JO, Garcia JC, Gomide RL, Guimarães CT, Magalhães PC, Magalhães JV, Queiroz Valéria AV (2011) Phenotyping sorghum for adaptation to drought. In: Monneveux P, Ribaut J-M (eds) Drought phenotyping in crops: from theory to practice. Frontiers Media SA, Lausanne, pp 287–299Google Scholar
  96. Schauer N, Fernie AR (2006) Plant metabolomics: towards biological function and mechanism. Trends Plant Sci 11:508–516. https://doi.org/10.1016/j.tplants.2006.08.007 PubMedCrossRefGoogle Scholar
  97. Schull MA, Knyazikhin Y, Xu L, Samanta A, Carmona PL, Lepine L, Jenkins JP, Ganguly S, Myneni RB (2011) Canopy spectral invariants, part 2: application to classification of forest types from hyperspectral data. J Quant Spectrosc Radiat Transf 112(4):736–775CrossRefGoogle Scholar
  98. Schwarz MA, Hauser PC (2001) Recent developments in detection methods for microfabricated analytical devices. Lab Chip 1(1):1–6PubMedCrossRefGoogle Scholar
  99. Seelig H-D, Hoehn A, Stodieck LS, Klaus DM III, Adams WW, Emery WJ (2008) The assessment of leaf water content using leaf reflectance ratios in the visible, near-, and short-wave-infrared. Int J Remote Sens 29:3701–3713. https://doi.org/10.1080/01431160701772500 CrossRefGoogle Scholar
  100. Shahin MA, Symons SJ (2011) Detection of Fusarium damaged kernels in Canada Western red Spring wheat using visible/near-infrared hyperspectral imaging and principal component analysis. Comput Electron Agric 75(1):107–112. https://doi.org/10.1016/j.compag.2010.10.004 CrossRefGoogle Scholar
  101. Shao HB, Liang ZS, Shao MA, Sun Q, Hu ZM (2005) Investigation on dynamic changes of photosynthetic characteristics of 10 wheat (Triticum aestivum L.) genotypes during two vegetative-growth stages at water deficits. Biointerfaces 43:221–227. https://doi.org/10.1016/j.colsurfb.2005.05.005 CrossRefGoogle Scholar
  102. Shao HB, Jaleel CA, Shao MA (2009) Understanding water deficit stress-induced changes in basic metabolisms of higher plants for biotechnologically and sustainably improving agriculture and ecoenvironment in arid regions on the globe. Crit Rev Biotechnol 29:131–151. https://doi.org/10.1080/07388550902869792 PubMedCrossRefGoogle Scholar
  103. Sheshshayee MS, Parsi Shashidhar G, Madhura JN, Beena R, Prasad TG, Udayakumar M (2011) Phenotyping groundnuts for adaptation to drought. In: Monneveux P, Ribaut J-M (eds) Drought phenotyping in crops: from theory to practice. Frontiers Media SA, Lausanne, pp 371–387Google Scholar
  104. Shi J-Y, Xiao-bo Z, Jie-Wen Z, Holmes M, Kai-liang W, Xue W, ChenH (2012) Determination of total flavonoids content in fresh ginkgo biloba leaf with different colors using near infrared spectroscopy. Spectrochim Acta A Mol Biomol Spectrosc 94:271–276. https://doi.org/10.1016/j.saa.2012.03.078 PubMedCrossRefGoogle Scholar
  105. Simko I, Jose AJ-B, Furbank Robert T (2015) Detection of decay in fresh-cut lettuce using hyperspectral imaging and chlorophyll fluorescence imaging. Postharvest Biol Technol 106:44–52. https://doi.org/10.1016/j.postharvbio.2015.04.007 CrossRefGoogle Scholar
  106. Sims DA, Gamon JA (2002) Relationships between leaf pigment content and spectral reflectance across a wide range of species, leaf structures and developmental stages. Remote Sens Environ 81:337. https://doi.org/10.1016/S0034-4257(02)00010-X CrossRefGoogle Scholar
  107. 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. https://doi.org/10.1071/FP09182 CrossRefGoogle Scholar
  108. Sun D-W (2010) Hyperspectral imaging for food quality analysis and control. Academic/Elsevier, San DiegoGoogle Scholar
  109. Sytar O, Bruckova K, Hunkova E, Zivcak M, Konate K, Brestic M (2015) The application of multiplex fluorimetric sensor for the analysis of flavonoids content in the medicinal herbs family Asteraceae, Lamiaceae, Rosaceae. Biol Res 48:5. https://doi.org/10.1186/0717-6287-48-5 PubMedPubMedCentralCrossRefGoogle Scholar
  110. Sytar O, Brestic M, Zivcak M, Tran L-S (2016) The contribution of buckwheat genetic resources to health and dietary diversity. Curr Genomics 17(3):193–206PubMedPubMedCentralCrossRefGoogle Scholar
  111. Sytar O, Bruckova K, Kovar M, Hemmerich I, Zivcak M, Brestic M (2017a) Nondestructive detection and biochemical quantification of buckwheat leaves using visible (VIS) and near-infrared (NIR) hyperspectral reflectance imaging. J Cent Eur Agric 18(4):864–878. https://doi.org/10.5513/JCEA01/18.4.1978 CrossRefGoogle Scholar
  112. Sytar O, Brestic M, Zivcak M, Olsovska K, Kovar M, Shao H, He X (2017b) Applying hyperspectral imaging to explore natural plant diversity towards improving salt stress tolerance. Sci Total Environ 578:90–99PubMedCrossRefGoogle Scholar
  113. Taghizadeh M, Gowen AA, O’Donnell CP (2011) Comparison of hyperspectral imaging with conventional RGB imaging for quality evaluation of Agaricus bisporus mushrooms. Biosyst Eng 108(2):191–194. https://doi.org/10.1016/j.biosystemseng.2010.10.005 CrossRefGoogle Scholar
  114. Tang X, Mu X, Shao H, Wang H, Brestic M (2014) Global plant-responding mechanisms to salt stress: physiological and molecular levels and implications in biotechnology. Crit Rev Biotechnol:1–13. https://doi.org/10.3109/07388551.2014.889080
  115. Tardieu F, Tuberosa R (2010) Dissection and modelling of abiotic stress tolerance in plants. Curr Opin Plant Biol 13:206–212. https://doi.org/10.1016/j.pbi.2009.12.012 PubMedCrossRefGoogle Scholar
  116. Tessmer OL, Jiao Y, Cruz JA, Kramer DM, Chen J (2013) Functional approach to high-throughput plant growth analysis. BMC systems Biology 20137(Suppl 6):S17 https://doi.org/10.1186/1752-0509-7-S6-S17
  117. Tester M, Langridge P (2010) Breeding technologies to increase crop production in a changing world. Science 327:818–822. https://doi.org/10.1126/science.1183700
  118. Thenkabail PS, Smith Ronald B, Pauw Eddy D (2000) Hyperspectral vegetation indices and their relationships with agricultural crop characteristics. Remote Sens Environ 71(2):158–182. https://doi.org/10.1016/S0034-4257(99)00067-X CrossRefGoogle Scholar
  119. Thenkabail PS, Enclona EA, Ashton MS, Van Der Meer V (2004a) Accuracy assessments of hyperspecral waveband performance for vegetation analysis applications. Remote Sens Environ 91(2–3):354–376. https://doi.org/10.1016/j.rse.2004.03.013 CrossRefGoogle Scholar
  120. Thenkabail PS, Enclona EA, Ashton MS, Legg C, Jean De Dieu M (2004b) Hyperion, IKONOS, ALI, and ETM +sensors in the study of African rainforests. Remote Sens Environ 90:23–43. https://doi.org/10.1016/j.rse.2003.11.018 CrossRefGoogle Scholar
  121. Todd PJ, Schaaff TG, Chaurand P, Caprioli RM (2001) Organic ion imaging of biological tissue with secondary ion mass spectrometry and matrix-assisted laser desorption/ionization. J Mass Spectrom 36:355–369PubMedCrossRefGoogle Scholar
  122. Topp CN, Iyer-Pascuzzi AS, Anderson JT, Lee C-R, Zurek PR, Symonova O, Zheng Y, Bucksch A, Mileyko Y, Galkovskyi T et al (2013) 3D phenotyping and quantitative trait locus mapping identify core regions of the rice genome controlling root architecture. Proc Natl Acad Sci U S A 110:E1695–E1704. https://doi.org/10.1073/pnas.1304354110 PubMedPubMedCentralCrossRefGoogle Scholar
  123. Upadhyaya HD, Kashiwagi J, Varshney RK, Gaur PM, Saxena KB, Krishnamurthy L, Gowda CL, Pundir RP, Chaturvedi SK, Basu PS, Singh IP (2012) Phenotyping chickpeas and pigeonpeas for adaptation to drought. Front Physiol 3:179. https://doi.org/10.3389/fphys.2012.00179 PubMedPubMedCentralCrossRefGoogle Scholar
  124. Urbanczyk-Wochniak E, Baxter C, Kolbe A, Kopka J, Sweetlove LJ, Fernie AR (2005) Profiling of diurnal patterns of metabolite and transcript abundance in potato (Solanum tuberosum) leaves. Planta 221:891–903. https://doi.org/10.1007/s00425-005-1483-y PubMedCrossRefGoogle Scholar
  125. Vadez V, Hash T, Bidinger FR, Kholova J (2012) Phenotyping pearl millet for adaptation to drought. Front Physiol 3:386. https://doi.org/10.3389/fphys.2012.00386. PubMedPubMedCentralCrossRefGoogle Scholar
  126. Verhoeven HA, de Vos CR, Bino RJ, Hall RD (2006) Plant metabolomics strategies based upon quadrupole time of flight mass spectrometry (QTOF-MS). In: Plant metabolomics. Springer, Berlin/HeidelbergGoogle Scholar
  127. Verpoorte R, Choi YH, Kim HK (2005) Ethnopharmacology and systems biology: a perfect holistic match. J Ethnopharmacol 100(1–2):53–56PubMedCrossRefGoogle Scholar
  128. Vilaró F (2011) Phenotyping sweet potatoes for adaptation to drought. In: Monneveux P, Ribaut J-M (eds) Drought phenotyping in crops: from theory to practice. Frontiers Media SA, Lausanne, pp 415–427Google Scholar
  129. Waggoner PS, Craighead HG (2007) Micro- and nanomechanical sensors for environmental, chemical, and biological detection. Lab Chip 7(10):1238–1255PubMedCrossRefGoogle Scholar
  130. Walter A, Liebisch F, Hund A (2015) Plant phenotyping: from bean weighing to image analysis. Plant Methods 11:14. https://doi.org/10.1186/s13007-015-0056-8 PubMedPubMedCentralCrossRefGoogle Scholar
  131. Warsta E, Lahdetie A, Jaaskelainen A-S, Vuorinen T (2012) Effect of pH on lignin analysis by Raman spectroscopy. Holzforschung 66:451–457. https://doi.org/10.1515/hf.2011.176 CrossRefGoogle Scholar
  132. Williams P, Manley M, Fox G, Geladi P (2010) Indirect detection of Fusarium verticillioides in maize (Zea maize L.) kernels by NIR hyperspectral imaging. J Near Infrared Spectrosc 18(1):49–58. https://doi.org/10.1255/jnirs.858 CrossRefGoogle Scholar
  133. Williams PJ, Geladi P, Britz TJ, Manley M (2012) Investigation of fungal development in maize kernels using NIR hyperspectral imaging and multivariate data analysis. J Cereal Sci 55:272–278. https://doi.org/10.1016/j.jcs.2011.12.003 CrossRefGoogle Scholar
  134. 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. https://doi.org/10.1016/j.eja.2011.03.004 CrossRefGoogle Scholar
  135. Witten IH, Frank E (2005) Data mining. Morgan Kauffman, San FranciscoGoogle Scholar
  136. Wu D, Sun D-W (2013) Advanced applications of hyperspectral imaging technology for food quality and safety analysis and assessment: a review — part II: applications. Innovative Food Sci Emerg Technol 19:15–28. https://doi.org/10.1016/j.ifset.2013.04.016 CrossRefGoogle Scholar
  137. Yamada N, Fujimura S (1991) Nondestructive measurement of chlorophyll pigment content in plant leaves from three-color reflectance and transmittance. Appl Opt 30:3964–3973. https://doi.org/10.1364/AO.30.003964 PubMedCrossRefGoogle Scholar
  138. Yan K, Chen P, Shao HB, Zhang LW, Zhang LH, Xu G, Sun JN (2011) Effects of short-term high temperature on photosynthesis and photosystem II performance in sorghum. J Agron Crop Sci 197:67–74. https://doi.org/10.1111/j.1439-037X.2011.00469.x CrossRefGoogle Scholar
  139. Yan K, Chen P, Shao HB, Zhao SJ, Zhang LH, Zhang LW, Xu G, Sun JN (2012) Photosynthetic characterization of Jerusalem artichoke during leaf expansion. Acta Physiol Plant 34:353–360. https://doi.org/10.1007/s11738-011-0834-5 CrossRefGoogle Scholar
  140. Yan K, Chen P, Shao HB, Shao CY, Zhao SJ, Brestic M (2013) Dissection of photosynthetic electron transport process in sweet sorghum under heat stress. PLoS One 8(5):e62100. https://doi.org/10.1371/journal.pone.0062100 PubMedPubMedCentralCrossRefGoogle Scholar
  141. Yang F, Li J, Gan X, Qian Y, Wu X, Yang Q (2010) Assessing nutritional status of Festuca arundinacea by monitoring photosynthetic pigments from hyperspectral data. Comput Electron Agric 70(1):52–59. https://doi.org/10.1016/j.compag.2009.08.010 CrossRefGoogle Scholar
  142. Yang CC, Kim MS, Kang S, Tao T, Chao K, Lefcourt AM et al (2011) The development of a simple multispectral algorithm for detection of fecal contamination on apples using a hyperspectral line-scan imaging system. Sens & Instrumen Food Qual 5(1):10–18. https://doi.org/10.1007/s11694-010-9105-1 CrossRefGoogle Scholar
  143. Yang C, Kim MS, Chao K (2012) Development and application of multispectral algorithms for defect apple inspection. ASABE Annual international meeting. Dallas, Texas: The American Society of Agricultural and Biological Engineers, St. Joseph, Michigan (Paper #12133701)Google Scholar
  144. Yang W, Guo Z, Huang C, Duan L, Chen G, Jiang N et al (2014) Combining high-throughput phenotyping and genome-wide association studies to reveal natural genetic variation in rice. Nat Commun 5:5087. https://doi.org/10.1038/ncomms6087 PubMedPubMedCentralCrossRefGoogle Scholar
  145. Yao X, Zhu Y, Tian Y-C, Feng W, Xing Cao W (2010a) Exploring hyperspectral bands and estimation indices for leaf nitrogen accumulation in wheat. Int J Appl Earth Obs Geoinf 12(2):89–100. https://doi.org/10.1016/j.jag.2009.11.008 CrossRefGoogle Scholar
  146. Yao H, Hruska Z, Kincaid R, Brown R, Cleveland T, Bhatnagar D (2010b) Correlation and classification of single kernel fluorescence hyperspectral data with aflatoxin concentration in corn kernels inoculated with Aspergillus flavus spores. Food Addit Contam 27(5):701–709. https://doi.org/10.1080/19440040903527368 CrossRefGoogle Scholar
  147. Yi Q-X, Huang J-F, Wang F-M, Wang X-Z (2008) Quantifying biochemical variables of corn by hyperspectral reflectance at leaf scale. J Zhejiang Univ Sci B 9(5):378–384. https://doi.org/10.1631/jzus.B0730019 PubMedPubMedCentralCrossRefGoogle Scholar
  148. Yu K-Q, Zhao Y-R, Li X-L, Shao Y-N, Liu F, He Y (2014) Hyperspectral imaging for mapping of total nitrogen spatial distribution in pepper plant. PLoS One 9(12):e116205. https://doi.org/10.1371/journal.pone.0116205 PubMedPubMedCentralCrossRefGoogle Scholar
  149. Zakis GF (1994) Functional analysis of lignins and their derivatives. Tappi Press, AtlantaGoogle Scholar
  150. Zhao D, Reddy KR, Kakani VG, Read JJ, Koti S (2005) Selection of optimum reflectance ratios for estimating leaf nitrogen and chlorophyll concentrations of field-grown cotton. Agron J 97:89–98. https://doi.org/10.2134/agronj2005.0089 CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  • Oksana Sytar
    • 1
    • 2
  • Marek Zivcak
    • 3
  • Katarina Olsovska
    • 3
  • Marian Brestic
    • 3
  1. 1.Department of Plant PhysiologySlovak University of Agriculture in NitraNitraSlovak Republic
  2. 2.Plant Physiology and Ecology Department, Institute of BiologyTaras Shevchenko National University of KyivKyivUkraine
  3. 3.Slovak University of Agriculture in NitraNitraSlovak Republic

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