Phytochemistry Reviews

, Volume 17, Issue 6, pp 1329–1343 | Cite as

Engineering plants for tomorrow: how high-throughput phenotyping is contributing to the development of better crops

  • Zachary C. Campbell
  • Lucia M. Acosta-Gamboa
  • Nirman Nepal
  • Argelia LorenceEmail author


High-throughput plant phenotyping has been advancing at an accelerated rate as a response to the need to fill the gap between genomic information and the plasticity of the plant phenome. During the past decade, North America has seen a stark increase in the number of phenotyping facilities, and these groups are actively contributing to the generation of high-dimensional, richly informative datasets about the phenotype of model and crop plants. As both phenomic datasets and analysis tools are made publicly available, the key to engineering more resilient crops to meet global demand is closer than ever. However, there are a number of bottlenecks that must yet be overcome before this can be achieved. In this paper, we present an overview of the most commonly used sensors that empower digital phenotyping and the information they provide. We also describe modern approaches to identify and characterize plants that are resilient to common abiotic and biotic stresses that limit growth and yield of crops. Of interest to researchers working in plant biochemistry, we also include a section discussing the potential of these high-throughput approaches in linking phenotypic data with chemical composition data. We conclude by discussing the main bottlenecks that still remain in the field and the importance of multidisciplinary teams and collaboration to overcome those challenges.


High-throughput plant phenotyping Plant phenotypes Phenomes Phenomics Abiotic stress tolerance 



This work was supported by the NSF-IOS-Plant Genome Research Project Award # 1238125, by the Plant Imaging Consortium (PIC; NSF EPSCoR Track-2 Research Infrastructure Improvement Program Awards IIA-1430427 and IIA-1430428, and by the Wheat and Rice Center for Heat Resilience (WRCHR; funded by NSF EPCoR Track 2 Award No. IIA-1736192. We also thank funds provided by the Arkansas Biosciences Institute, the major research component of the Arkansas Tobacco Settlement Proceeds Act. LMAG and NN thank the Molecular Biosciences Graduate Program at Arkansas State University for stipend support.


  1. Acosta-Gamboa LM, Liu S, Langley E et al (2017) Moderate to severe water limitation differentially affects the phenome and ionome of Arabidopsis. Funct Plant Biol 44:94–106Google Scholar
  2. Al-Tamimi N, Brien C, Oakey H et al (2016) Salinity tolerance loci revealed in rice using high-throughput non-invasive phenotyping. Nat Commun 7:1–11Google Scholar
  3. Andrade-Sanchez P, Gore MA, Heun JT et al (2014) Development and evaluation of a field-based high-throughput phenotyping platform. Funct Plant Biol 41:68–79Google Scholar
  4. Angulo C, de la O Leyva M, Finiti I et al (2015) Role of dioxygenase α-DOX2 and SA in basal response and in hexanoic acid-induced resistance of tomato (Solanum lycopersicum) plants against Botrytis cinerea. J Plant Physiol 175:163–173PubMedGoogle Scholar
  5. Arvidsson S, Pérez-Rodríguez 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 191:895–907PubMedGoogle Scholar
  6. Avila CA, Arevalo-Soliz LM, Jia L et al (2012) Loss of function of FATTY ACID DESATURASE7 in tomato enhances basal aphid resistance in a salicylate-dependent manner. Plant Physiol 158:2028–2041PubMedPubMedCentralGoogle Scholar
  7. Awlia M, Nigro A, Fajkus J et al (2016) High-throughput non-destructive phenotyping of traits that contribute to salinity tolerance in Arabidopsis thaliana. Front Plant Sci 7:1–15Google Scholar
  8. Backoulou GF, Elliott NC, Giles K et al (2011) Spatially discriminating Russian wheat aphid induced plant stress from other wheat stressing factors. Comput Electron Agric 78:123–129Google Scholar
  9. Baranowski P, Jedryczka M, Mazurek W et al (2015) Hyperspectral and thermal imaging of oilseed rape (Brassica napus) response to fungal species of the genus Alternaria. PLoS ONE 10:1–20Google Scholar
  10. Barlow KM, Christy BP, O’Leary GJ et al (2015) Simulating the impact of extreme heat and frost events on wheat crop production: a review. F Crop Res 171:109–119Google Scholar
  11. Bauer SD, Korč F, Förstner W (2011) The potential of automatic methods of classification to identify leaf diseases from multispectral images. Precis Agric 12:361–377Google Scholar
  12. Blackburn GA (2007) Hyperspectral remote sensing of plant pigments. J Exp Bot 58:855–867PubMedGoogle Scholar
  13. Bock CH, Poole GH, Parker PE, Gottwald TR (2010) Plant disease severity estimated visually, by digital photography and image analysis, and by hyperspectral imaging. Crit Rev Plant Sci 29:59–107Google Scholar
  14. Camargo AV, Lobos GA (2016) Latin America: a development pole for phenomics. Front Plant Sci 7:1729PubMedPubMedCentralGoogle Scholar
  15. Camargo A, Smith JS (2009) An image-processing based algorithm to automatically identify plant disease visual symptoms. Biosyst Eng 102:9–21Google Scholar
  16. Campbell MT, Knecht AC, Berger B et al (2015) Integrating image-based phenomics and association analysis to dissect the genetic architecture of temporal salinity responses in rice. Plant Physiol 168:1476–1489PubMedPubMedCentralGoogle Scholar
  17. Casanova JJ, O’Shaughnessy SA, Evett SR, Rush CM (2014) Development of a wireless computer vision instrument to detect biotic stress in wheat. Sensors 14:17753–17769PubMedGoogle Scholar
  18. Chen J, Hua G, Jurat-Fuentes JL et al (2007) Synergism of Bacillus thuringiensis toxins by a fragment of a toxin-binding cadherin. Proc Natl Acad Sci USA 104:13901–13906PubMedGoogle Scholar
  19. Chen D, Neumann K, Friedel S et al (2014) Dissecting the phenotypic components of crop plant growth and drought responses based on high-throughput image analysis. Plant Cell 26:4636–4655PubMedPubMedCentralGoogle Scholar
  20. Cobb JN, DeClerck G, Greenberg A et al (2013) Next-generation phenotyping: requirements and strategies for enhancing our understanding of genotype-phenotype relationships and its relevance to crop improvement. Theor Appl Genet 126:867–887PubMedPubMedCentralGoogle Scholar
  21. Crain J, Reynolds M, Poland J (2017) Utilizing high-throughput phenotypic data for improved phenotypic selection of stress-adaptive traits in wheat. Crop Sci 57:648–659Google Scholar
  22. De Diego N, Fürst T, Humplík JF et al (2017) An automated method for high-throughput screening of Arabidopsis rosette growth in multi-well plates and its validation in stress conditions. Front Plant Sci 8:1702PubMedPubMedCentralGoogle Scholar
  23. Dobrescu A, Scorza LCT, Tsaftaris SA, McCormick AJ (2017) A “Do-It-Yourself” phenotyping system: measuring growth and morphology throughout the diel cycle in rosette shaped plants. Plant Methods 13:1–12Google Scholar
  24. Fahlgren N, Feldman M, Gehan MA et al (2015a) A versatile phenotyping system and analytics platform reveals diverse temporal responses to water availability in Setaria. Mol Plant 8:1520–1535PubMedGoogle Scholar
  25. Fahlgren N, Gehan MA, Baxter I (2015b) Lights, camera, action: high-throughput plant phenotyping is ready for a close-up. Curr Opin Plant Biol 24:93–99PubMedGoogle Scholar
  26. Flynn P (2003) Biotic vs. abiotic—distinguishing disease problems from environmental stresses. Hortic Home Pest News 489:22Google Scholar
  27. Furbank RT, Tester M (2011) Phenomics—technologies to relieve the phenotyping bottleneck. Trends Plant Sci 16:635–644PubMedGoogle Scholar
  28. Gehan MA, Kellogg EA (2017) High-throughput phenotyping. Am J Bot 104:505–508PubMedGoogle Scholar
  29. Gehan MA, Fahlgren N, Abbasi A et al (2017) PlantCV v2: image analysis software for high-throughput plant phenotyping. PeerJ 5:e4088PubMedPubMedCentralGoogle Scholar
  30. Gendrin C, Roggo Y, Collet C (2008) Pharmaceutical applications of vibrational chemical imaging and chemometrics: a review. J Pharm Biomed Anal 48:533–553PubMedGoogle Scholar
  31. Goggin FL, Lorence A, Topp CN (2015) Applying high-throughput phenotyping to plant-insect interactions: picturing more resistant crops. Curr Opin Insect Sci 9:69–76Google Scholar
  32. Golzarian MR, Frick RA, Rajendran K et al (2011) Accurate inference of shoot biomass from high-throughput images of cereal plants. Plant Methods 7:1–11Google Scholar
  33. González-Pérez JL, Espino-Gudiño MC, Gudiño-Bazaldúa J et al (2013) Color image segmentation using perceptual spaces through applets for determining and preventing diseases in chili peppers. Afr J Biotechnol 12:679–688Google Scholar
  34. Gowen AA, Feng Y, Gaston E, Valdramidis V (2015) Recent applications of hyperspectral imaging in microbiology. Talanta 137:43–54PubMedGoogle Scholar
  35. Granier C, Vile D (2014) Phenotyping and beyond: modelling the relationships between traits. Curr Opin Plant Biol 18:96–102PubMedGoogle Scholar
  36. Granier C, Aguirrezabal L, Chenu K et al (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 169:623–635PubMedGoogle Scholar
  37. Hairmansis A, Berger B, Tester M, Roy SJ (2014) Image-based phenotyping for non-destructive screening of different salinity tolerance traits in rice. Rice 7:1–10Google Scholar
  38. Hernández-Rabadán DL, Ramos-Quintana F, Guerrero Juk J (2014) Integrating SOMs and a bayesian classifier for segmenting diseased plants in uncontrolled environments. Sci World J 2014:1–13Google Scholar
  39. Honsdorf N, March TJ, Berger B et al (2014) High-throughput phenotyping to detect drought tolerance QTL in wild barley introgression lines. PLoS ONE 9:e97047PubMedPubMedCentralGoogle Scholar
  40. Houle D, Govindaraju DR, Omholt S (2010) Phenomics: the next challenge. Nat Rev Genet 11:855–866PubMedGoogle Scholar
  41. Huang KY (2007) Application of artificial neural network for detecting Phalaenopsis seedling diseases using color and texture features. Comput Electron Agric 57:3–11Google Scholar
  42. Humplík JF, Lazár D, Fürst T et al (2015) Automated integrative high-throughput phenotyping of plant shoots: a case study of the cold-tolerance of pea (Pisum sativum L). Plant Methods 11:1–11Google Scholar
  43. Jansen M, Gilmer F, Biskup B et al (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 36:902–914Google Scholar
  44. Kerchev PI, Fenton B, Foyer CH, Hancock RD (2012) Plant responses to insect herbivory: interactions between photosynthesis, reactive oxygen species and hormonal signalling pathways. Plant Cell Environ 35:441–453PubMedGoogle Scholar
  45. Kirchgessner N, Liebisch F, Yu K et al (2017) The ETH field phenotyping platform FIP: a cable-suspended multi-sensor system. Funct Plant Biol 44:154–168Google Scholar
  46. Knecht AC, Campbell MT, Caprez A, Swanson DR, Walia H (2016) Image harvest: an open-source platform for high-throughput plant image processing and analysis. J Exp Bot 67:3587–3599PubMedPubMedCentralGoogle Scholar
  47. Kuhlgert S, Austic G, Zegarac R et al (2016) MultispeQ Beta: a tool for large-scale plant phenotyping connected to the open PhotosynQ network. R Soc Open Sci 3:160592PubMedPubMedCentralGoogle Scholar
  48. Li Y, Ye W, Wang M, Yan X (2009) Climate change and drought: a risk assessment of crop-yield impacts. Clim Res 39:31–46Google Scholar
  49. Lobet G, Draye X, Perilleux C (2013) An online database for plant image analysis software tools. Plant Methods 9:38PubMedPubMedCentralGoogle Scholar
  50. Mahajan S, Tuteja N (2005) Cold, salinity, and drought stress: an overview. Plant Stress Biol From Genom Syst Biol 444:137–159Google Scholar
  51. Mahlein AK, Steiner U, Hillnhütter C et al (2012) Hyperspectral imaging for small-scale analysis of symptoms caused by different sugar beet diseases. Plant Methods 8:1–13Google Scholar
  52. Malamy JE (2005) Intrinsic and environmental response pathways that regulate root system architecture. Plant Cell Environ 28:67–77PubMedPubMedCentralGoogle Scholar
  53. Masler EP, Chitwood D (2016) Heterodera glycines cysts contain an extensive array of endoproteases as well as inhibitors of proteases in H. glycines and Meloidogyne incognita infective juvenile stages. Nematology 18:489–499Google Scholar
  54. Mishra P, Cordella CBY, Rutledge DN et al (2016) Application of independent components analysis with the JADE algorithm and NIR hyperspectral imaging for revealing food adulteration. J Food Eng 168:7–15Google Scholar
  55. Mishra P, Asaari MSM, Herrero-Langreo A et al (2017) Close range hyperspectral imaging of plants: a review. Biosyst Eng 164:49–67Google Scholar
  56. Mokhtar U, Ali MAS, Hassanien AE, Hefny HA (2015) Identifying two of tomatoes leaf viruses using support vector machine. In: Information systems design and intelligent applications: proceedings of second international conference India, pp 781–782Google Scholar
  57. Nabity PD, Zavala JA, DeLucia EH (2009) Indirect suppression of photosynthesis on individual leaves by arthropod herbivory. Ann Bot 103:655–663PubMedGoogle Scholar
  58. Nabity PD, Haus MJ, Berenbaum MR, DeLucia EH (2013) Leaf-galling phylloxera on grapes reprograms host metabolism and morphology. Proc Natl Acad Sci USA 110:16663–16668PubMedGoogle Scholar
  59. Pan TT, Sun DW, Cheng JH, Pu H (2016) Regression algorithms in hyperspectral data analysis for meat quality detection and evaluation. Compr Rev Food Sci Food Saf 15:529–541Google Scholar
  60. Pandey P, Ramegowda V, Senthil-Kumar M (2015) Shared and unique responses of plants to multiple individual stresses and stress combinations: physiological and molecular mechanisms. Front Plant Sci 6:1–14Google Scholar
  61. Pandey P, Ge Y, Stoerger V, Schnable JC (2017) High throughput in vivo analysis of plant leaf chemical properties using hyperspectral imaging. Front Plant Sci 8:1–12PubMedPubMedCentralGoogle Scholar
  62. Pound MP, Fozard S, Torres Torres M et al (2017) AutoRoot: open-source software employing a novel image analysis approach to support fully-automated plant phenotyping. Plant Methods 13:1–10Google Scholar
  63. Rahaman MM, Chen D, Gillani Z et al (2015) Advanced phenotyping and phenotype data analysis for the study of plant growth and development. Front Plant Sci 6:1–15Google Scholar
  64. Rajendran K, Tester M, Roy SJ (2009) Quantifying the three main components of salinity tolerance in cereals. Plant Cell Environ 32:237–249PubMedGoogle Scholar
  65. Reuzeau C, Pen J, Frankard V et al (2010) TraitMill: a discovery engine for identifying yield-enhancement genes in cereals. Plant Gene Trait 1:1–7Google Scholar
  66. Rumpf T, Mahlein AK, Steiner U et al (2010) Early detection and classification of plant diseases with support vector machines based on hyperspectral reflectance. Comput Electron Agric 74:91–99Google Scholar
  67. 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 36:970–977Google Scholar
  68. Sirault X, Fripp J, Paproki A, et al (2013) PlantScan™: a three-dimensional phenotyping platform for capturing the structural dynamic of plant development and growth. In: nternational Conference on Functional-Structural Plant Model, pp 45–48Google Scholar
  69. Skirycz A, Vandenbroucke K, Clauw P et al (2011) Survival and growth of Arabidopsis plants given limited water are not equal. Nat Biotechnol 29:212–214PubMedGoogle Scholar
  70. Slovak R, Goschl C, Su X et al (2014) A scalable open-source pipeline for large-scale root phenotyping of Arabidopsis. Plant Cell 26:2390–2403PubMedPubMedCentralGoogle Scholar
  71. Smith CM, Clement SL (2012) Molecular bases of plant resistance to arthropods. Annu Rev Entomol 57:309–328PubMedGoogle Scholar
  72. Symonova O, Topp CN, Edelsbrunner H (2015) DynamicRoots: a software platform for the reconstruction and analysis of growing plant roots. PLoS ONE 10:1–15Google Scholar
  73. Tardieu F, Cabrera-Bosquet L, Pridmore T, Bennett M (2017) Plant phenomics, from sensors to knowledge. Curr Biol 27:R770–R783PubMedGoogle Scholar
  74. Thurau T, Ye W, Cai D (2009) Insect and nematode resistance. Biotechnol Agric For 64:177–197Google Scholar
  75. Tisné S, Serrand Y, Bach L et al (2013) Phenoscope: an automated large-scale phenotyping platform offering high spatial homogeneity. Plant J 74:534–544PubMedGoogle Scholar
  76. Vadez V, Kholová J, Hummel G et al (2015) LeasyScan: a novel concept combining 3D imaging and lysimetry for high-throughput phenotyping of traits controlling plant water budget. J Exp Bot 66:5581–5593PubMedPubMedCentralGoogle Scholar
  77. Vance CP, Uhde-Stone C, Allan DL (2003) Phosphorus acquisition and use: critical adaptations by plants for securing a nonrenewable resource. New Phytol 157:423–447Google Scholar
  78. Vigneau N, Ecarnot M, Rabatel G, Roumet P (2011) Potential of field hyperspectral imaging as a non destructive method to assess leaf nitrogen content in wheat. Field Crop Res 122:25–31Google Scholar
  79. Wahabzada M, Mahlein AK, Bauckhage C et al (2015) Metro maps of plant disease dynamics-automated mining of differences using hyperspectral images. PLoS ONE 10:1–21Google Scholar
  80. Walter A, Scharr H, Gilmer F et al (2007) Dynamics of seedling growth acclimation towards altered light conditions can be quantified via GROWSCREEN: a setup and procedure designed for rapid optical phenotyping of different plant species. New Phytol 174:447–455PubMedGoogle Scholar
  81. Wang W, Vinocur B, Altman A (2003) Plant responses to drought, salinity and extreme temperatures: towards genetic engineering for stress tolerance. Planta 218:1–14PubMedGoogle Scholar
  82. Wetterich CB, Kumar R, Sankaran S et al (2013) A comparative study on application of computer vision and fluorescence imaging spectroscopy for detection of citrus huanglongbing disease in USA and Brazil. J Spectrosc 2013:1–6Google Scholar
  83. Wu DK, Xie CY (2008) Cheng-Wei M (2008) The SVM classification leafminer-infected leaves based on fractal dimension. IEEE Int Conf Cybern Intell Syst CIS 2008:147–151Google Scholar
  84. Yang W, Guo Z, Huang C et al (2014) Combining high-throughput phenotyping and genome-wide association studies to reveal natural genetic variation in rice. Nat Commun 5:1–9Google Scholar

Copyright information

© Springer Nature B.V. 2018

Authors and Affiliations

  1. 1.Arkansas Biosciences InstituteArkansas State UniversityState UniversityUSA
  2. 2.Plant Phenomics FacilityArkansas State UniversityState UniversityUSA
  3. 3.Department of Chemistry and PhysicsArkansas State UniversityJonesboroUSA

Personalised recommendations