1932

Abstract

Plant phenotyping enables noninvasive quantification of plant structure and function and interactions with environments. High-capacity phenotyping reaches hitherto inaccessible phenotypic characteristics. Diverse, challenging, and valuable applications of phenotyping have originated among scientists, prebreeders, and breeders as they study the phenotypic diversity of genetic resources and apply increasingly complex traits to crop improvement. Noninvasive technologies are used to analyze experimental and breeding populations. We cover the most recent research in controlled-environment and field phenotyping for seed, shoot, and root traits. Select field phenotyping technologies have become state of the art and show promise for speeding up the breeding process in early generations. We highlight the technologies behind the rapid advances in proximal and remote sensing of plants in fields. We conclude by discussing the new disciplines working with the phenotyping community: data science, to address the challenge of generating FAIR (findable, accessible, interoperable, and reusable) data, and robotics, to apply phenotyping directly on farms.

Loading

Article metrics loading...

/content/journals/10.1146/annurev-arplant-042916-041124
2020-04-29
2024-04-19
Loading full text...

Full text loading...

/deliver/fulltext/arplant/71/1/annurev-arplant-042916-041124.html?itemId=/content/journals/10.1146/annurev-arplant-042916-041124&mimeType=html&fmt=ahah

Literature Cited

  1. 1. 
    Araus JL, Cairns JE. 2014. Field high-throughput phenotyping: the new crop breeding frontier. Trends Plant Sci 19:52–61
    [Google Scholar]
  2. 2. 
    Arend D, Lange M, Chen J, Colmsee C, Flemming S et al. 2014. e!DAL—a framework to store, share and publish research data. BMC Bioinform 15:214
    [Google Scholar]
  3. 3. 
    Arsova B, Foster KJ, Shelden MC, Bramley H, Watt M 2019. Dynamics in plant roots and shoots minimise stress, save energy and maintain water and nutrient uptake. New Phytol 225:1111–19
    [Google Scholar]
  4. 4. 
    Asseng S, Ewert F, Rosenzweig C, Jones JW, Hatfield JL et al. 2013. Uncertainty in simulating wheat yields under climate change. Nat. Clim. Change 3:827–32
    [Google Scholar]
  5. 5. 
    Atieno J, Li Y, Langridge P, Dowling K, Brien C et al. 2017. Exploring genetic variation for salinity tolerance in chickpea using image-based phenotyping. Sci. Rep. 7:1300
    [Google Scholar]
  6. 6. 
    Atkinson JA, Pound MP, Bennett MJ, Wells DM 2019. Uncovering the hidden half of plants using new advances in root phenotyping. Curr. Opin. Biotechnol. 55:1–8
    [Google Scholar]
  7. 7. 
    Avramova V, Nagel KA, AbdElgawad H, Bustos D, DuPlessis M et al. 2016. Screening for drought tolerance of maize hybrids by multi-scale analysis of root and shoot traits at the seedling stage. J. Exp. Bot. 67:2453–66
    [Google Scholar]
  8. 8. 
    Bao Y, Aggarwal P, Robbins NE II, Sturrock CJ, Thompson MC et al. 2014. Plant roots use a patterning mechanism to position lateral root branches toward available water. PNAS 111:9319–24
    [Google Scholar]
  9. 9. 
    Baute J, Herman D, Coppens F, De Block J, Slabbinck B et al. 2016. Combined large-scale phenotyping and transcriptomics in maize reveals a robust growth regulatory network. Plant Physiol 170:1848–67
    [Google Scholar]
  10. 10. 
    Bechar A. 2016. Agricultural robots for field operations: concepts and components. Biosyst. Eng. 149:94–111
    [Google Scholar]
  11. 11. 
    Behmann J, Mahlein A-K, Rumpf T, Römer C, Plümer L 2015. A review of advanced machine learning methods for the detection of biotic stress in precision crop protection. Precis. Agric. 16:239–60
    [Google Scholar]
  12. 12. 
    Bendig J, Bolten A, Bennertz S, Broscheit J, Eichfuss S, Bareth G 2014. Estimating biomass of barley using crop surface models (CSMs) derived from UAV-based RGB imaging. Remote Sens 6:10395–412
    [Google Scholar]
  13. 13. 
    Bendig J, Yu K, Aasen H, Bolten A, Bennertz S et al. 2015. Combining UAV-based plant height from crop surface models, visible, and near infrared vegetation indices for biomass monitoring in barley. Int. J. Appl. Earth Obs. Geoinf. 39:79–87
    [Google Scholar]
  14. 14. 
    Bhosale SU, Rymen B, Beemster GTS, Melchinger AE, Reif JC 2007. Chilling tolerance of Central European maize lines and their factorial crosses. Ann. Bot. 100:1315–21
    [Google Scholar]
  15. 15. 
    Biskup B, Scharr H, Schurr U, Rascher UWE 2007. A stereo imaging system for measuring structural parameters of plant canopies. Plant Cell Environ 30:1299–308
    [Google Scholar]
  16. 16. 
    Bolger AM, Poorter H, Dumschott K, Bolger ME, Arend D et al. 2019. Computational aspects underlying genome to phenome analysis in plants. Plant J 97:182–98
    [Google Scholar]
  17. 17. 
    Cabrera-Bosquet L, Fournier C, Brichet N, Welcker C, Suard B, Tardieu F 2016. High-throughput estimation of incident light, light interception and radiation-use efficiency of thousands of plants in a phenotyping platform. New Phytol 212:269–81Provides robust methodologies for estimation of radiation-use efficiency in automated plant phenotyping platforms.
    [Google Scholar]
  18. 18. 
    Cendrero P, Muller O, Albrecht H, Burkart A, Gatzke S et al. 2017. Field phenotyping: concepts and examples to quantify dynamic plant traits across scales in the field. Terrestrial Ecosystem Research Infra-structures: Challenges and Opportunities A Chabbi, HW Loescher 53–82 Boca Raton, FL: CRC
    [Google Scholar]
  19. 19. 
    Chochois V, Vogel JP, Rebetzke GJ, Watt M 2015. Variation in adult plant phenotypes and partitioning among seed and stem-borne roots across Brachypodium distachyon accessions to exploit in breeding cereals for well-watered and drought environments. Plant Physiol 168:953–67
    [Google Scholar]
  20. 20. 
    Cooper L, Meier A, Laporte M-A, Elser JL, Mungall C et al. 2017. The Planteome database: an integrated resource for reference ontologies, plant genomics and phenomics. Nucleic Acids Res 46:D1168–80
    [Google Scholar]
  21. 21. 
    Cooper M, Messina CD, Podlich D, Totir LR, Baumgarten A et al. 2014. Predicting the future of plant breeding: complementing empirical evaluation with genetic prediction. Crop. Pasture Sci. 65:311–36
    [Google Scholar]
  22. 22. 
    Coppens F, Wuyts N, Inzé D, Dhondt S 2017. Unlocking the potential of plant phenotyping data through integration and data-driven approaches. Curr. Opin. Syst. Biol. 4:58–63
    [Google Scholar]
  23. 23. 
    Ćwiek-Kupczyńska H, Altmann T, Arend D, Arnaud E, Chen D et al. 2016. Measures for interoperability of phenotypic data: minimum information requirements and formatting. Plant Methods 12:44
    [Google Scholar]
  24. 24. 
    de Oliveira Silva FM, Lichtenstein G, Alseekh S, Rosado-Souza L, Conte M et al. 2018. The genetic architecture of photosynthesis and plant growth–related traits in tomato. Plant Cell Environ 41:327–41
    [Google Scholar]
  25. 25. 
    Deery DM, Rebetzke GJ, Jimenez-Berni JA, James RA, Condon AG et al. 2016. Methodology for high-throughput field phenotyping of canopy temperature using airborne thermography. Front. Plant Sci. 7:1808
    [Google Scholar]
  26. 26. 
    Delgado A, Hays DB, Bruton RK, Ceballos H, Novo A et al. 2017. Ground penetrating radar: a case study for estimating root bulking rate in cassava (Manihot esculenta Crantz). Plant Methods 13:65
    [Google Scholar]
  27. 27. 
    Donald CM. 1968. The breeding of crop ideotypes. Euphytica 17:385–403
    [Google Scholar]
  28. 28. 
    Douarre C, Schielein R, Frindel C, Gerth S, Rousseau D 2016. Deep learning based root-soil segmentation from X-ray tomography images. bioRxiv 071662. https://doi.org/10.1101/071662
    [Crossref]
  29. 29. 
    Dreccer MF, Molero G, Rivera-Amado C, John-Bejai C, Wilson Z 2019. Yielding to the image: how phenotyping reproductive growth can assist crop improvement and production. Plant Sci 282:73–82
    [Google Scholar]
  30. 30. 
    Drusch M, Moreno J, Del Bello U, Franco R, Goulas Y et al. 2017. The FLuorescence EXplorer Mission Concept—ESA's Earth Explorer 8. IEEE Trans. Geosci. Remote Sens. 55:1273–84
    [Google Scholar]
  31. 31. 
    Duan L, Han J, Guo Z, Tu H, Yang P et al. 2018. Novel digital features discriminate between drought resistant and drought sensitive rice under controlled and field conditions. Front. Plant Sci. 9:492
    [Google Scholar]
  32. 32. 
    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
    [Google Scholar]
  33. 33. 
    Fennimore SA, Cutulle M. 2019. Robotic weeders can improve weed control options for specialty crops. Pest Manag. Sci. 75:1767–74
    [Google Scholar]
  34. 34. 
    Fiorani F, Schurr U. 2013. Future scenarios for plant phenotyping. Annu. Rev. Plant Biol. 64:267–91
    [Google Scholar]
  35. 35. 
    Fischer RA, Rebetzke GJ. 2018. Indirect selection for potential yield in early-generation, spaced plantings of wheat and other small-grain cereals: a review. Crop Pasture Sci 69:439–59
    [Google Scholar]
  36. 36. 
    Flood PJ, Kruijer W, Schnabel SK, van der Schoor R, Jalink H et al. 2016. Phenomics for photosynthesis, growth and reflectance in Arabidopsis thaliana reveals circadian and long-term fluctuations in heritability. Plant Methods 12:14
    [Google Scholar]
  37. 37. 
    Furbank RT, Jimenez-Berni JA, George-Jaeggli B, Potgieter AB, Deery DM 2019. Field crop phenomics: enabling breeding for radiation use efficiency and biomass in cereal crops. New Phytol 223. https://doi.org/10.1111/nph.15817 Presents a recent overview of how field phenotyping contributes to physiological breeding for traits related to radiation-use efficiency, photosynthesis, and crop biomass.
    [Crossref] [Google Scholar]
  38. 38. 
    Furbank RT, Tester M. 2011. Phenomics—technologies to relieve the phenotyping bottleneck. Trends Plant Sci 16:635–44
    [Google Scholar]
  39. 39. 
    Gamuyao R, Chin JH, Pariasca-Tanaka J, Pesaresi P, Catausan S et al. 2012. The protein kinase Pstol1 from traditional rice confers tolerance of phosphorus deficiency. Nature 488:535–39
    [Google Scholar]
  40. 40. 
    Gente R, Koch M. 2015. Monitoring leaf water content with THz and sub-THz waves. Plant Methods 11:15
    [Google Scholar]
  41. 41. 
    Gibbs JA, Pound M, French AP, Wells DM, Murchie E, Pridmore T 2017. Approaches to three-dimensional reconstruction of plant shoot topology and geometry. Funct. Plant Biol. 44:62–75
    [Google Scholar]
  42. 42. 
    Giles CD, George TS, Brown LK, Mezeli MM, Richardson AE et al. 2017. Does the combination of citrate and phytase exudation in Nicotiana tabacum promote the acquisition of endogenous soil organic phosphorus?. Plant Soil 412:43–59
    [Google Scholar]
  43. 43. 
    Gioia T, Galinski A, Ha Lenz, Müller C, Lentz J et al. 2016. GrowScreen-PaGe, a non-invasive, high-throughput phenotyping system based on germination paper to quantify crop phenotypic diversity and plasticity of root traits under varying nutrient supply. Funct. Plant Biol. 44:76–93
    [Google Scholar]
  44. 44. 
    Gioia T, Nagel KA, Beleggia R, Fragasso M, Ficco DB et al. 2015. Impact of domestication on the phenotypic architecture of durum wheat under contrasting nitrogen fertilization. J. Exp. Bot. 66:5519–30
    [Google Scholar]
  45. 45. 
    Giuffrida MV, Chen F, Scharr H, Tsaftaris SA 2018. Citizen crowds and experts: observer variability in image-based plant phenotyping. Plant Methods 14:12
    [Google Scholar]
  46. 46. 
    Grassini P, van Bussel LGJ, Van Wart J, Wolf J, Claessens L et al. 2015. How good is good enough? Data requirements for reliable crop yield simulations and yield–gap analysis. Field Crops Res 177:49–63
    [Google Scholar]
  47. 47. 
    Gregory PJ, Bengough AG, Grinev D, Schmidt S, Thomas WTB et al. 2009. Root phenomics of crops: opportunities and challenges. Funct. Plant Biol. 36:922–29
    [Google Scholar]
  48. 48. 
    Hall AJ, Richards RA. 2013. Prognosis for genetic improvement of yield potential and water-limited yield of major grain crops. Field Crops Res 143:18–33
    [Google Scholar]
  49. 49. 
    Hatfield JL, Walthall CL. 2015. Meeting global food needs: realizing the potential via genetics × environment × management interactions. Agron. J. 107:1215–26
    [Google Scholar]
  50. 50. 
    Holman HF, Riche BA, Michalski A, Castle M, Wooster JM, Hawkesford JM 2016. High throughput field phenotyping of wheat plant height and growth rate in field plot trials using UAV based remote sensing. Remote Sens 8:1031
    [Google Scholar]
  51. 51. 
    Huang BE, Verbyla KL, Verbyla AP, Raghavan C, Singh VK et al. 2015. MAGIC populations in crops: current status and future prospects. Theor. Appl. Genet. 128:999–1017
    [Google Scholar]
  52. 52. 
    Jahnke S, Menzel MI, van Dusschoten D, Roeb GW, Buhler J et al. 2009. Combined MRI-PET dissects dynamic changes in plant structures and functions. Plant J 59:634–44
    [Google Scholar]
  53. 53. 
    Jahnke S, Roussel J, Hombach T, Kochs J, Fischbach A et al. 2016. phenoSeeder—a robot system for automated handling and phenotyping of individual seeds. Plant Physiol 172:1358–70
    [Google Scholar]
  54. 54. 
    Jimenez-Berni JA, Deery DM, Rozas-Larraondo P, Condon AG, Rebetzke GJ et al. 2018. High throughput determination of plant height, ground cover, and above-ground biomass in wheat with LiDAR. Front. Plant Sci. 9:237
    [Google Scholar]
  55. 55. 
    Jin X, Liu S, Baret F, Hemerlé M, Comar A 2017. Estimates of plant density of wheat crops at emergence from very low altitude UAV imagery. Remote Sens. Environ. 198:105–14
    [Google Scholar]
  56. 56. 
    Junker A, Muraya MM, Weigelt-Fischer K, Arana-Ceballos F, Klukas C et al. 2015. Optimizing experimental procedures for quantitative evaluation of crop plant performance in high throughput phenotyping systems. Front. Plant Sci. 5:770
    [Google Scholar]
  57. 57. 
    Keller B, Vass I, Matsubara S, Paul K, Jedmowski C et al. 2019. Maximum fluorescence and electron transport kinetics determined by light-induced fluorescence transients (LIFT) for photosynthesis phenotyping. Photosynth. Res. 140:221–33Defines the photosynthetic traits for phenotyping by the LIFT method.
    [Google Scholar]
  58. 58. 
    Khan Z, Rahimi-Eichi V, Haefele S, Garnett T, Miklavcic SJ 2018. Estimation of vegetation indices for high-throughput phenotyping of wheat using aerial imaging. Plant Methods 14:20
    [Google Scholar]
  59. 59. 
    Kim S, Oh H, Suk J, Tsourdos A 2014. Coordinated trajectory planning for efficient communication relay using multiple UAVs. Control Eng. Pract. 29:42–49
    [Google Scholar]
  60. 60. 
    Kirchgessner N, Liebisch F, Yu K, Pfeifer J, Friedli M et al. 2017. The ETH field phenotyping platform FIP: a cable-suspended multi-sensor system. Funct. Plant Biol. 44:154–68
    [Google Scholar]
  61. 61. 
    Krajewski P, Chen D, Ćwiek H, van Dijk ADJ, Fiorani F et al. 2015. Towards recommendations for metadata and data handling in plant phenotyping. J. Exp. Bot. 66:5417–27
    [Google Scholar]
  62. 62. 
    Kuijken RCP, van Eeuwijk FA, Marcelis LFM, Bouwmeester HJ 2015. Root phenotyping: from component trait in the lab to breeding. J. Exp. Bot. 66:5389–401
    [Google Scholar]
  63. 63. 
    Li X, Ingvordsen CH, Weiss M, Rebetzke GJ, Condon AG et al. 2019. Deeper roots associated with cooler canopies, higher normalized difference vegetation index, and greater yield in three wheat populations grown on stored soil water. J. Exp. Bot. 70:4963–74
    [Google Scholar]
  64. 64. 
    Liao H, Rubio G, Yan X, Cao A, Brown KM, Lynch JP 2001. Effect of phosphorus availability on basal root shallowness in common bean. Plant Soil 232:69–79
    [Google Scholar]
  65. 65. 
    Lilley JM, Kirkegaard JA. 2016. Farming system context drives the value of deep wheat roots in semi-arid environments. J. Exp. Bot. 67:3665–81
    [Google Scholar]
  66. 66. 
    Liu T, Li R, Jin X, Ding J, Zhu X et al. 2017. Evaluation of seed emergence uniformity of mechanically sown wheat with UAV RGB imagery. Remote Sens 9:1241
    [Google Scholar]
  67. 67. 
    Long SP, Zhu X-G, Naidu SL, Ort DR 2006. Can improvement in photosynthesis increase crop yields. Plant Cell Environ 29:315–30
    [Google Scholar]
  68. 68. 
    Lopez-Castaneda C, Richards RA, Farquhar GD, Williamson RE 1996. Seed and seedling characteristics contributing to variation in early vigor among temperate cereals. Crop Sci 36:1257–66
    [Google Scholar]
  69. 69. 
    Ma X, Zhu K, Guan H, Feng J, Yu S, Liu G 2019. Calculation method for phenotypic traits based on the 3D reconstruction of maize canopies. Sensors 19:1201
    [Google Scholar]
  70. 70. 
    Mahlein AK, Kuska MT, Behmann J, Polder G, Walter A 2018. Hyperspectral sensors and imaging technologies in phytopathology: state of the art. Annu. Rev. Phytopathol. 56:535–58Provides an overview of hyperspectral sensors and imaging technologies for assessing plant–pathogen interactions on leaves and canopies.
    [Google Scholar]
  71. 71. 
    Mairhofer S, Zappala S, Tracy S, Sturrock C, Bennett MJ et al. 2013. Recovering complete plant root system architectures from soil via X-ray μ-computed tomography. Plant Methods 9:8
    [Google Scholar]
  72. 72. 
    Massonnet C, Vile D, Fabre J 2010. Probing the reproducibility of leaf growth and molecular phenotypes: a comparison of three Arabidopsis accessions cultivated in ten laboratories. Plant Physiol 152:2142–57
    [Google Scholar]
  73. 73. 
    Maxwell K, Johnson GN. 2000. Chlorophyll fluorescence—a practical guide. J. Exp. Bot. 51:659–68
    [Google Scholar]
  74. 74. 
    Meng R, Saade S, Kurtek S, Berger B, Brien C et al. 2017. Growth curve registration for evaluating salinity tolerance in barley. Plant Methods 13:18
    [Google Scholar]
  75. 75. 
    Metzner R, Eggert A, van Dusschoten D, Pflugfelder D, Gerth S et al. 2015. Direct comparison of MRI and X-ray CT technologies for 3D imaging of root systems in soil: potential and challenges for root trait quantification. Plant Methods 11:17
    [Google Scholar]
  76. 76. 
    Millet EJ, Kruijer W, Coupel-Ledru A, Alvarez Prado S, Cabrera-Bosquet L et al. 2019. Genomic prediction of maize yield across European environmental conditions. Nat. Genet. 51:952–56
    [Google Scholar]
  77. 77. 
    Müller-Linow M, Pinto-Espinosa F, Scharr H, Rascher U 2015. The leaf angle distribution of natural plant populations: assessing the canopy with a novel software tool. Plant Methods 11:11
    [Google Scholar]
  78. 78. 
    Munns R, James RA, Xu B, Athman A, Conn SJ et al. 2012. Wheat grain yield on saline soils is improved by an ancestral Na+ transporter gene. Nat. Biotechnol. 30:360–64Presents the classical prebreeding program from physiological understanding to phenotypic selection, to markers, to field proof of concept of improved germplasm.
    [Google Scholar]
  79. 79. 
    Murakami T. 2012. Canopy height measurement by photogrammetric analysis of aerial images: application to buckwheat (Fagopyrum esculentum Moench) lodging evaluation. Comput. Electron. Agric. 89:70–75
    [Google Scholar]
  80. 80. 
    Muraya MM, Chu J, Zhao Y, Junker A, Klukas C et al. 2017. Genetic variation of growth dynamics in maize (Zea mays L.) revealed through automated non-invasive phenotyping. Plant J 89:366–80
    [Google Scholar]
  81. 81. 
    Nagel KA, Bonnett D, Furbank R, Walter A, Schurr U, Watt M 2015. Simultaneous effects of leaf irradiance and soil moisture on growth and root system architecture of novel wheat genotypes: implications for phenotyping. J. Exp. Bot. 66:5441–52
    [Google Scholar]
  82. 82. 
    Nagel KA, Putz A, Gilmer F, Heinz K, Fischbach A et al. 2012. GROWSCREEN-Rhizo is a novel phenotyping robot enabling simultaneous measurements of root and shoot growth for plants grown in soil-filled rhizotrons. Funct. Plant Biol. 39:891–904
    [Google Scholar]
  83. 83. 
    Nakhforoosh A, Bodewein T, Fiorani F, Bodner G 2016. Identification of water use strategies at early growth stages in durum wheat from shoot phenotyping and physiological measurements. Front. Plant Sci. 7:1155
    [Google Scholar]
  84. 84. 
    Negrão S, Schmöckel SM, Tester M 2016. Evaluating physiological responses of plants to salinity stress. Ann. Bot. 119:1–11
    [Google Scholar]
  85. 85. 
    Neveu P, Tireau A, Hilgert N, Nègre V, Mineau-Cesari J et al. 2019. Dealing with multi-source and multi-scale information in plant phenomics: the ontology-driven Phenotyping Hybrid Information System. New Phytol 221:588–601
    [Google Scholar]
  86. 86. 
    Ni J, Pujar A, Youens-Clark K, Yap I, Jaiswal P et al. 2009. Gramene QTL database: development, content and applications. Database2009:bap005
    [Google Scholar]
  87. 87. 
    Nijveen H, Ligterink W, Keurentjes JJB, Loudet O, Long J et al. 2017. AraQTL—workbench and archive for systems genetics in Arabidopsis thaliana. Plant J 89:1225–35
    [Google Scholar]
  88. 88. 
    Ogbonnaya FC, Subrahmanyam NC, Moullet O, de Majnik J, Eagles HA et al. 2001. Diagnostic DNA markers for cereal cyst nematode resistance in bread wheat. Crop Pasture Sci 52:1367–74
    [Google Scholar]
  89. 89. 
    Padmarasu S, Himmelbach A, Mascher M, Stein N 2019. In situ Hi-C for plants: an improved method to detect long-range chromatin interactions. Plant Long Non-Coding RNAs: Methods and Protocols JA Chekanova, H-LV Wang 441–72 New York: Springer
    [Google Scholar]
  90. 90. 
    Paez-Garcia A, Motes CM, Scheible WR, Chen R, Blancaflor EB, Monteros MJ 2015. Root traits and phenotyping strategies for plant improvement. Plants 4:334–55
    [Google Scholar]
  91. 91. 
    Parent B, Tardieu F. 2012. Temperature responses of developmental processes have not been affected by breeding in different ecological areas for 17 crop species. New Phytol 194:760–74
    [Google Scholar]
  92. 92. 
    Pask A, Joshi AK, Manès Y, Sharma I, Chatrath R et al. 2014. A wheat phenotyping network to incorporate physiological traits for climate change in South Asia. Field Crops Res 168:156–67
    [Google Scholar]
  93. 93. 
    Pekkeriet EJ, van Henten EJ, Campen JB 2015. Contribution of innovative technologies to new developments in horticulture. Acta Hortic 1099:45–54
    [Google Scholar]
  94. 94. 
    Pérez-Torres E, Kirchgessner N, Pfeifer J, Walter A 2015. Assessing potato tuber diel growth by means of X-ray computed tomography. Plant Cell Environ 38:2318–26
    [Google Scholar]
  95. 95. 
    Pieruschka R, Schurr U. 2019. Plant phenotyping: past, present, and future. Plant Phenom 2019:7507131
    [Google Scholar]
  96. 96. 
    Pinto F, Damm A, Schickling A, Panigada C, Cogliati S et al. 2016. Sun-induced chlorophyll fluorescence from high-resolution imaging spectroscopy data to quantify spatio-temporal patterns of photosynthetic function in crop canopies. Plant Cell Environ 39:1500–12Introduces sun-induced chlorophyll fluorescence as a potential functional trait for phenotyping.
    [Google Scholar]
  97. 97. 
    Poorter H, Anten NPR, Marcelis LFM 2013. Physiological mechanisms in plant growth models: Do we need a supra-cellular systems biology approach?. Plant Cell Environ 36:1673–90
    [Google Scholar]
  98. 98. 
    Poorter H, Bühler J, van Dusschoten D, Climent J, Postma JA 2012. Pot size matters: a meta-analysis of the effects of rooting volume on plant growth. Funct. Plant Biol. 39:839–50
    [Google Scholar]
  99. 99. 
    Poorter H, Fiorani F, Pieruschka R, Wojciechowski T, van der Putten WH et al. 2016. Pampered inside, pestered outside? Differences and similarities between plants growing in controlled conditions and in the field. New Phytol 212:838–55
    [Google Scholar]
  100. 100. 
    Poorter H, Fiorani F, Stitt M, Schurr U, Finck A et al. 2012. The art of growing plants for experimental purposes: a practical guide for the plant biologist. Funct. Plant Biol. 39:821–38
    [Google Scholar]
  101. 101. 
    Poorter H, Lambers H, Evans JR 2014. Trait correlation networks: a whole-plant perspective on the recently criticized leaf economic spectrum. New Phytol 201:378–82
    [Google Scholar]
  102. 102. 
    Poorter H, Niinemets Ü, Ntagkas N, Siebenkäs A, Mäenpää M et al. 2019. A meta-analysis of plant responses to light intensity for 70 traits ranging from molecules to whole plant performance. New Phytol 223: https://doi.org/10.1111/nph.15754
    [Crossref] [Google Scholar]
  103. 103. 
    Poorter H, Niklas KJ, Reich PB, Oleksyn J, Poot P, Mommer L 2012. Biomass allocation to leaves, stems and roots: meta-analyses of interspecific variation and environmental control. New Phytol 193:30–50
    [Google Scholar]
  104. 104. 
    Postma JA, Kuppe C, Owen MR, Mellor N, Griffiths M et al. 2017. OpenSimRoot: widening the scope and application of root architectural models. New Phytol 215:1274–86
    [Google Scholar]
  105. 105. 
    Pound MP, Atkinson JA, Townsend AJ, Wilson MH, Griffiths M et al. 2017. Deep machine learning provides state-of-the-art performance in image-based plant phenotyping. GigaScience 6:1–10
    [Google Scholar]
  106. 106. 
    Raesch AR, Muller O, Pieruschka R, Rascher U 2014. Field observations with laser-induced fluorescence transient (LIFT) method in barley and sugar beet. Agriculture 4:159–69
    [Google Scholar]
  107. 107. 
    Rascher U, Alonso L, Burkart A, Cilia C, Cogliati S et al. 2015. Sun-induced fluorescence—a new probe of photosynthesis. First maps from the imaging spectrometer HyPlant. Glob. Change Biol. 21:4673–84
    [Google Scholar]
  108. 108. 
    Rebetzke GJ, Chenu K, Biddulph B, Moeller C, Deery DM et al. 2012. A multisite managed environment facility for targeted trait and germplasm phenotyping. Funct. Plant Biol. 40:1–13
    [Google Scholar]
  109. 109. 
    Rebetzke GJ, Verbyla AP, Verbyla KL, Morell MK, Cavanagh CR 2014. Use of a large multiparent wheat mapping population in genomic dissection of coleoptile and seedling growth. Plant Biotechnol. J. 12:219–30
    [Google Scholar]
  110. 110. 
    Reynolds M, Foulkes MJ, Slafer GA, Berry P, Parry MAJ et al. 2009. Raising yield potential in wheat. J. Exp. Bot. 60:1899–918
    [Google Scholar]
  111. 111. 
    Rich SM, Wasson AP, Richards RA, Katore T, Prashar R et al. 2016. Wheats developed for high yield on stored soil moisture have deep vigorous root systems. Funct. Plant Biol. 43:173–88
    [Google Scholar]
  112. 112. 
    Richard CA, Hickey LT, Fletcher S, Jennings R, Chenu K, Christopher JT 2015. High-throughput phenotyping of seminal root traits in wheat. Plant Methods 11:13
    [Google Scholar]
  113. 113. 
    Richards RA, Passioura JB. 1981. Seminal root morphology and water-use of wheat. II. Genetic variation. Crop Sci 21:253–55
    [Google Scholar]
  114. 114. 
    Richards RA, Rebetzke GJ, Watt M, Condon AG, Spielmeyer W, Dolferus R 2010. Breeding for improved water productivity in temperate cereals: phenotyping, quantitative trait loci, markers and the selection environment. Funct. Plant Biol. 37:85–97
    [Google Scholar]
  115. 115. 
    Richards RA, Watt M, Rebetzke GJ 2007. Physiological traits and cereal germplasm for sustainable agricultural systems. Euphytica 154:409–25
    [Google Scholar]
  116. 116. 
    Rincent R, Charpentier J-P, Faivre-Rampant P, Paux E, Le Gouis J et al. 2018. Phenomic selection: a low-cost and high-throughput method based on indirect predictions: proof of concept on wheat and poplar. G3 8:3961–72
    [Google Scholar]
  117. 117. 
    Rocca-Serra P, Brandizi M, Maguire E, Sklyar N, Taylor C et al. 2010. ISA software suite: supporting standards-compliant experimental annotation and enabling curation at the community level. Bioinformatics 26:2354–56
    [Google Scholar]
  118. 118. 
    Roy J, Tardieu F, Tixier-Boichard M, Schurr U 2017. European infrastructures for sustainable agriculture. Nat. Plants 3:756–58
    [Google Scholar]
  119. 119. 
    Ruckelhausen A, Biber P, Dorna M, Gremmes H, Klose R et al. 2009. BoniRob: an autonomous field robot platform for individual plant phenotyping. Precision Agriculture ’09: Papers Presented at the 7th European Conference on Precision Agriculture (ECPA 7)841–47 Wageningen, Neth: Academic
    [Google Scholar]
  120. 120. 
    Schmidt MHW, Vogel A, Denton AK, Istace B, Wormit A et al. 2017. De novo assembly of a new Solanum pennellii accession using nanopore sequencing. Plant Cell 29:2336–48
    [Google Scholar]
  121. 121. 
    Schneider C. 2014. Celebrating 100 years of Dr. Norman Borlaug. CSA News. 59:4–11. https://doi.org/10.2134/csa2014-59-3-1
    [Crossref] [Google Scholar]
  122. 122. 
    Schroeder JI, Delhaize E, Frommer WB, Guerinot ML, Harrison MJ et al. 2013. Using membrane transporters to improve crops for sustainable food production. Nature 497:60–66
    [Google Scholar]
  123. 123. 
    Schwacke R, Ponce-Soto GY, Krause K, Bolger AM, Arsova B et al. 2019. MapMan4: a refined protein classification and annotation framework applicable to multi-omics data analysis. Mol. Plant 12:879–92
    [Google Scholar]
  124. 124. 
    Seren Ü, Grimm D, Fitz J, Weigel D, Nordborg M et al. 2016. AraPheno: a public database for Arabidopsis thaliana phenotypes. Nucleic Acids Res 45:D1054–59
    [Google Scholar]
  125. 125. 
    Shakoor N, Lee S, Mockler TC 2017. High throughput phenotyping to accelerate crop breeding and monitoring of diseases in the field. Curr. Opin. Plant Biol. 38:184–92
    [Google Scholar]
  126. 126. 
    Singh A, Ganapathysubramanian B, Singh AK, Sarkar S 2016. Machine learning for high-throughput stress phenotyping in plants. Trends Plant Sci 21:110–24
    [Google Scholar]
  127. 127. 
    Thoen MPM, Davila Olivas NH, Kloth KJ, Coolen S, Huang P-P et al. 2017. Genetic architecture of plant stress resistance: multi-trait genome-wide association mapping. New Phytol 213:1346–62
    [Google Scholar]
  128. 128. 
    Thomas S, Behmann J, Steier A, Kraska T, Muller O et al. 2018. Quantitative assessment of disease severity and rating of barley cultivars based on hyperspectral imaging in a non-invasive, automated phenotyping platform. Plant Methods 14:45
    [Google Scholar]
  129. 129. 
    Thomas S, Wahabzada M, Kuska MT, Rascher U, Mahlein A-K 2017. Observation of plant–pathogen interaction by simultaneous hyperspectral imaging reflection and transmission measurements. Funct. Plant Biol. 44:23–34
    [Google Scholar]
  130. 130. 
    Togninalli M, Seren Ü, Meng D, Fitz J, Nordborg M et al. 2017. The AraGWAS Catalog: a curated and standardized Arabidopsis thaliana GWAS catalog. Nucleic Acids Res 46:D1150–56
    [Google Scholar]
  131. 131. 
    Trachsel S, Kaeppler SM, Brown KM, Lynch JP 2011. Shovelomics: high throughput phenotyping of maize (Zea mays L.) root architecture in the field. Plant Soil 341:75–87
    [Google Scholar]
  132. 132. 
    Trachsel S, Kaeppler SM, Brown KM, Lynch JP 2013. Maize root growth angles become steeper under low N conditions. Field Crops Res 140:18–31
    [Google Scholar]
  133. 133. 
    Tracy SR, Nagel KA, Postma JA, Fassbender H, Wasson A, Watt M 2020. Crop improvement from phenotyping roots: Highlights reveal expanding opportunities. Trends Plant Sci 25:105–18
    [Google Scholar]
  134. 134. 
    Tripodi P, Massa D, Venezia A, Cardi T 2018. Sensing technologies for precision phenotyping in vegetable crops: current status and future challenges. Agronomy 8:57
    [Google Scholar]
  135. 135. 
    Tsaftaris SA, Scharr H. 2019. Sharing the right data right: a symbiosis with machine learning. Trends Plant Sci 24:99–102
    [Google Scholar]
  136. 136. 
    Tuberosa R. 2012. Phenotyping for drought tolerance of crops in the genomics era. Front. Physiol. 3:347
    [Google Scholar]
  137. 137. 
    van Dusschoten D, Metzner R, Kochs J, Postma JA, Pflugfelder D et al. 2016. Quantitative 3D analysis of plant roots growing in soil using magnetic resonance imaging. Plant Physiol 170:1176–88
    [Google Scholar]
  138. 138. 
    van Eeuwijk FA, Bink MCAM, Chenu K, Chapman SC 2010. Detection and use of QTL for complex traits in multiple environments. Curr. Opin. Plant Biol. 13:193–205
    [Google Scholar]
  139. 139. 
    van Eeuwijk FA, Bustos-Korts D, Millet EJ, Boer MP, Kruijer W et al. 2019. Modelling strategies for assessing and increasing the effectiveness of new phenotyping techniques in plant breeding. Plant Sci 282:23–39Provides a quantitative framework for using primary traits or proxies for statistical modeling of phenotypic responses.
    [Google Scholar]
  140. 140. 
    van Eeuwijk FA, Bustos-Korts DV, Malosetti M 2016. What should students in plant breeding know about the statistical aspects of genotype × environment interactions?. Crop Sci 56:2119–40
    [Google Scholar]
  141. 141. 
    Vergauwen D, De Smet I 2017. From early farmers to Norman Borlaug—the making of modern wheat. Curr. Biol. 27:R858–62
    [Google Scholar]
  142. 142. 
    Walter A, Finger R, Huber R, Buchmann N 2017. Opinion: Smart farming is key to developing sustainable agriculture. PNAS 114:6148–50
    [Google Scholar]
  143. 143. 
    Walter A, Silk WK, Schurr U 2009. Environmental effects on spatial and temporal patterns of leaf and root growth. Annu. Rev. Plant Biol. 60:279–304
    [Google Scholar]
  144. 144. 
    Wasson AP, Nagel KA, Tracy S, Watt M 2020. Beyond digging: noninvasive root and rhizosphere phenotyping. Trends Plant Sci 25:119–20
    [Google Scholar]
  145. 145. 
    Wasson AP, Rebetzke GJ, Kirkegaard JA, Christopher J, Richards RA, Watt M 2014. Soil coring at multiple field environments can directly quantify variation in deep root traits to select wheat genotypes for breeding. J. Exp. Bot. 65:6231–49
    [Google Scholar]
  146. 146. 
    Wasson AP, Richards RA, Chatrath R, Misra SC, Prasad SVS et al. 2012. Traits and selection strategies to improve root systems and water uptake in water-limited wheat crops. J. Exp. Bot. 63:3485–98
    [Google Scholar]
  147. 147. 
    Watson A, Ghosh S, Williams MJ, Cuddy WS, Simmonds J et al. 2018. Speed breeding is a powerful tool to accelerate crop research and breeding. Nat. Plants 4:23–29
    [Google Scholar]
  148. 148. 
    Watt M, Kirkegaard J, Rebetzke G 2005. A wheat genotype developed for rapid leaf growth copes well with the physical and biological constraints of unploughed soil. Funct. Plant Biol. 32:695–706
    [Google Scholar]
  149. 149. 
    Watt M, Moosavi S, Cunningham SC, Kirkegaard JA, Rebetzke GJ, Richards RA 2013. A rapid, controlled-environment seedling root screen for wheat correlates well with rooting depths at vegetative, but not reproductive, stages at two field sites. Ann. Bot. 112:447–55
    [Google Scholar]
  150. 150. 
    Wen W, Jin M, Li K, Liu H, Xiao Y et al. 2018. An integrated multi-layered analysis of the metabolic networks of different tissues uncovers key genetic components of primary metabolism in maize. Plant J 93:1116–28
    [Google Scholar]
  151. 151. 
    Wilkinson MD, Dumontier M, Aalbersberg IJ, Appleton G, Axton M et al. 2016. The FAIR Guiding Principles for scientific data management and stewardship. Sci. Data 3:160018
    [Google Scholar]
  152. 152. 
    Wissuwa M, Kretzschmar T, Rose TJ 2016. From promise to application: root traits for enhanced nutrient capture in rice breeding. J. Exp. Bot. 67:3605–15Explains, using root traits, how phenotypes for proxies and yield components must be repeatable and heritable and contribute to yield in target environment.
    [Google Scholar]
  153. 153. 
    Wu S, Alseekh S, Cuadros-Inostroza Á, Fusari CM, Mutwil M et al. 2016. Combined use of genome-wide association data and correlation networks unravels key regulators of primary metabolism in Arabidopsis thaliana. PLOS Genet 12:e1006363
    [Google Scholar]
  154. 154. 
    Xiao Y, Liu H, Wu L, Warburton M, Yan J 2017. Genome-wide association studies in maize: praise and stargaze. Mol. Plant 10:359–74
    [Google Scholar]
  155. 155. 
    Yang M-D, Huang K-S, Kuo Y-H, Tsai PH, Lin L-M 2017. Spatial and spectral hybrid image classification for rice lodging assessment through UAV imagery. Remote Sens 9:583
    [Google Scholar]
  156. 156. 
    Zhang J, Naik HS, Assefa T, Sarkar S, Reddy RVC et al. 2017. Computer vision and machine learning for robust phenotyping in genome-wide studies. Sci. Rep. 7:44048
    [Google Scholar]
  157. 157. 
    Zhao JS, Bodner G, Rewald B, Leitner D, Nagel KA, Nakhforoosh A 2017. Root architecture simulation improves the inference from seedling root phenotyping towards mature root systems. J. Exp. Bot. 68:965–82
    [Google Scholar]
  158. 158. 
    Zhao Y, Gong L, Huang Y, Liu C 2016. A review of key techniques of vision-based control for harvesting robot. Comput. Electron. Agric. 127:311–23
    [Google Scholar]
  159. 159. 
    Zhou C, Liang D, Yang X, Xu B, Yang G 2018. Recognition of wheat spike from field based phenotype platform using multi-sensor fusion and improved maximum entropy segmentation algorithms. Remote Sens 10:246
    [Google Scholar]
  160. 160. 
    Zhou N, Siegel ZD, Zarecor S, Lee N, Campbell DA et al. 2018. Crowdsourcing image analysis for plant phenomics to generate ground truth data for machine learning. PLOS Comput. Biol. 14:e1006337
    [Google Scholar]
  161. 161. 
    Zhu X-G, Long SP, Ort DR 2010. Improving photosynthetic efficiency for greater yield. Annu. Rev. Plant Biol. 61:235–61
    [Google Scholar]
/content/journals/10.1146/annurev-arplant-042916-041124
Loading
/content/journals/10.1146/annurev-arplant-042916-041124
Loading

Data & Media loading...

  • Article Type: Review Article
This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error