Abstract
Development of high-throughput phenotyping technologies has progressed considerably in the last 10 years. These technologies provide precise measurements of desired traits among thousands of field-grown plants under diversified environments; this is a critical step towards selection of better performing lines as to yield, disease resistance, and stress tolerance to accelerate crop improvement programs. High-throughput phenotyping techniques and platforms help unraveling the genetic basis of complex traits associated with plant growth and development and targeted traits. This review focuses on the advancements in technologies involved in high-throughput, field-based, aerial, and unmanned platforms. Development of user-friendly data management tools and softwares to better understand phenotyping will increase the use of field-based high-throughput techniques, which have potential to revolutionize breeding strategies and meet the future needs of stakeholders.
Similar content being viewed by others
Data Availability
Not applicable.
Code Availability
Not applicable.
References
Adam E, Deng H, Odindi J, Abdel-Rahman EM, Mutanga O (2017) Detecting the early stage of phaeosphaeria leaf spot infestations in maize crop using in situ hyperspectral data and guided regularized random forest algorithm. J Spectrosc 2017:1–8
Al-Tamimi N, Brien C, Oakey H, Berger B, Saade S, Ho YS, Schmöckel SM, Tester M, Negrão S (2016) Salinity tolerance loci revealed in rice using high-throughput non-invasive phenotyping. Nat Commun 7:13342
Allah MZ, Vergara O, Araus JL, Tarekegne A, Magorokosho C, Tejada PJZ, Hornero A (2015) Unmanned aerial platform-based multi‑spectral imaging for field phenotyping of maize. Plant Methods 1–10
Andrade-Sanchez P, Gore MA, Heun JT, Thorp KR, Carmo-Silva AE, French AN, Salvucci ME, White JW (2014) Development and evaluation of a field-based high-throughput phenotyping platform. Funct Plant Biol 41:68
Antal TK, Matorin DN, Ilyash LV, Volgusheva AA, Osipov V, Konyuhov IV, Krendeleva TE, Rubin AB (2009) Probing of photosynthetic reactions in four phytoplanktonic algae with a PEA fluorometer. Photosynth Res 102:67–76
Apan A, Held A, Phinn S, Markley J (2004) Detecting sugarcane ‘orange rust’ disease using EO-1 Hyperion hyperspectral imagery. Int J Remote Sens 25:489–498
Arend D, Lange M, Pape J-M, Weigelt-Fischer K, Arana-Ceballos F, Mücke I, Klukas C, Altmann T, Scholz U, Junker A (2016) Quantitative monitoring of Arabidopsis thaliana growth and development using high-throughput plant phenotyping. Sci Data 3:160055
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 191:895–907
Asseng S, Aylmore LAG, MacFall JS, Hopmans JW, Gregory PJ (2000) Computer-assisted tomography and magnetic resonance imaging. In: Smit AL, Bengough AG, Engels C, van Noordwijk M, Pellerin S, van de Geijn SC (eds) Root methods. Springer, Berlin, pp 343–363
Awlia M, Nigro A, Fajkus J, Schmoeckel SM, Negrão S, Santelia D, Trtílek M, Tester M, Julkowska MM, Panzarová K (2016) High-throughput non-destructive phenotyping of traits that contribute to salinity tolerance in Arabidopsis thaliana. Front Plant Sci 7:1414
Bai G, Blecha S, Ge Y, Walia H, Phansak P (2017) Characterizing wheat response to water limitation using multispectral and thermal imaging. Trans ASABE 60:1457–1466
Bai G, Ge Y, Hussain W, Baenziger PS, Graef G (2016) A multi-sensor system for high throughput field phenotyping in soybean and wheat breeding. Comput Electron Agric 128:181–192
Bailey DL, Karp JS, Surti S (2005) Physics and instrumentation in PET. In: Bailey DL, Townsend DW, Valk PE, Maisey MN (eds) Positron emission tomography. Springer-Verlag, London, pp 13–39
Baker NR, Rosenqvist E (2004) Applications of chlorophyll fluorescence can improve crop production strategies: an examination of future possibilities. J Exp Bot 55:1607–1621
Baluja J, Diago MP, Balda P, Tardaguila J (2012) Assessment of vineyard water status variability by thermal and multispectral imagery using an unmanned aerial vehicle (UAV). Irrig Sci 30:511–522
Banerjee K, Krishnan P, Mridha N (2018) Application of thermal imaging of wheat crop canopy to estimate leaf area index under different moisture stress conditions. Biosyst Eng 166:13–27
Bauriegel E, Herppich W (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
Beauchêne K, Leroy F, Fournier A, Huet C, Bonnefoy M, Lorgeou J, de Solan B, Piquemal B, Thomas S, Cohan J-P (2019) Management and characterization of abiotic stress via PhénoField®, a high-throughput field phenotyping platform. Front Plant Sci 10:904
Berger B, Parent B, Tester M (2010) High-throughput shoot imaging to study drought responses. J Exp Bot 61:3519–3528
Berger B, de Regt B, Tester M (2012) High-throughput phenotyping of plant shoots. Methods Mol Biol 918:9–20
Berni JAJ, Member S, Zarco-tejada PJ, Suárez L, Fereres E (2009) Thermal and narrowband multispectral remote sensing for vegetation monitoring from an unmanned aerial vehicle. IEEE Trans Geosci Remote Sens 47:722–738
Biju S, Fuentes S, Gupta D (2018) The use of infrared thermal imaging as a non-destructive screening tool for identifying drought-tolerant lentil genotypes. Plant Physiol Biochem 127:11–24
Blancon J, Dutartre D, Tixier MH, Weiss M, Comar A, Praud S, Baret F (2019) A high-throughput model-assisted method for phenotyping maize green leaf area index dynamics using unmanned aerial vehicle imagery. Front Plant Sci 10:685
Bock CH, Poole GH, Parker PE, Gottwald TR (2010) Plant disease severity estimated visually, by digital photography and image analysis, and by hyperspectral imaging. CRC Crit Rev Plant Sci 29:59–107
Bresson J, Varoquaux F, Bontpart T, Touraine B, Vile D (2013) The PGPR strain Phyllobacterium brassicacearum STM196 induces a reproductive delay and physiological changes that result in improved drought tolerance in Arabidopsis. New Phytol 200:558–569
Bresson J, Vasseur F, Dauzat M, Labadie M, Varoquaux F, Touraine B, Vile D (2014) Interact to Survive: Phyllobacterium brassicacearum improves arabidopsis tolerance to severe water deficit and growth recovery. PLoS ONE 9:e107607
Bühler J, Huber G, Schmid F, Blümler P (2011) Analytical model for long-distance tracer-transport in plants. J Theor Biol 270:70–79
Bürling K, Hunsche M, Noga G (2011) Use of blue-green and chlorophyll fluorescence measurements for differentiation between nitrogen deficiency and pathogen infection in winter wheat. J Plant Physiol 168:1641–1648
Burrell T, Fozard S, Holroyd GH, French AP, Pound MP, Bigley CJ, James Taylor C, Forde BG (2017) The Microphenotron: a robotic miniaturized plant phenotyping platform with diverse applications in chemical biology. Plant Methods 13:10
Busemeyer L, Mentrup D, Möller K, Wunder E, Alheit K, Hahn V, Maurer HP, Reif JC, Würschum T, Müller J, Rahe F, Ruckelshausen A (2013) BreedVision-a multi-sensor platform for non-destructive field-based phenotyping in plant breeding. Sensors 13:2830–2847
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 54:312–320
Calderón R, Navas-Cortés JA, Zarco-Tejada PJ (2015) Early detection and quantification of verticillium wilt in olive using hyperspectral and thermal imagery over large areas. Remote Sens 7:5584–5610
Casadesús J, Kaya Y, Bort J, Nachit MM, Araus JL, Amor S, Ferrazzano G, Maalouf F, Maccaferri M, Martos V, Ouabbou H, Villegas D (2007) Using vegetation indices derived from conventional digital cameras as selection criteria for wheat breeding in water-limited environments. Ann Appl Biol 150:227–236
de Castro AI, Ehsani R, Ploetz RC, Crane JH, Buchanon S (2015) Detection of laurel wilt disease in Avocado using low altitude aerial imaging. PLoS ONE 10:e0124642
Chaerle L, Hagenbeek D, De Bruyne E, Valcke R, Van Der Straeten D (2004) Thermal and chlorophyll-fluorescence imaging distinguish plant-pathogen interactions at an early stage. Plant Cell Physiol 45:887–896
Chaerle L, Leinonen I, Jones HG, Van Der Straeten D (2007) Monitoring and screening plant populations with combined thermal and chlorophyll fluorescence imaging. J Exp Bot 58:773–784
Chaerle L, Van Der Straeten D (2000) Imaging techniques and the early detection of plant stress. Trends Plant Sci 5:495–501
Chao H, Cao Y, Chen Y (2010) Autopilots for small unmanned aerial vehicles: a survey. Int J Control Autom Syst 8:36–44
Chapman S, Merz T, Chan A, Jackway P, Hrabar S, Dreccer M, Holland E, Zheng B, Ling T, Jimenez-Berni J (2014) Pheno-Copter: a low-altitude, autonomous remote-sensing robotic helicopter for high-throughput field-based phenotyping. Agronomy 4:279–301
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 Online 26:4636–4655
Cheng S-X, Kong W-W, Zhang C, Liu F, He Y (2014) Variety recognition of Chinese cabbage seeds by hyperspectral imaging combined with machine learning. Guang Pu Xue Yu Guang Pu Fen Xi 34:2519–2522
Clark RT, Famoso AN, Zhao K, Shaff JE, Craft EJ, Bustamante CD, Mccouch SR, Aneshansley DJ, Kochian LV (2013) High-throughput two-dimensional root system phenotyping platform facilitates genetic analysis of root growth and development. Plant Cell Environ 36:454–466
Clauw P, Coppens F, De Beuf K, Dhondt S, Van Daele T, Maleux K, Storme V, Clement L, Gonzalez N, Inzé D (2015) Leaf responses to mild drought stress in natural variants of Arabidopsis. Plant Physiol 167:800–816
Coelho LP (2013) Mahotas: open source software for scriptable computer vision. J Open Res Softw 1:e3
Comar A, Burger P, de Solan B, Baret F, Daumard F, Hanocq J-F (2012) A semi-automatic system for high throughput phenotyping wheat cultivars in-field conditions: description and first results. Funct Plant Biol 39:914
Cossani CM, Reynolds MP (2012) Physiological traits for improving heat tolerance in wheat. Plant Physiol 160:1710–1718
Costa JM, Grant OM, Chaves MM (2013) Thermography to explore plant–environment interactions. J Exp Bot 64:3937–3949
Coupel-Ledru A, Lebon É, Christophe A, Doligez A, Cabrera-Bosquet L, Péchier P, Hamard P, This P, Simonneau T (2014) Genetic variation in a grapevine progeny (Vitis vinifera L. cvs Grenache × Syrah) reveals inconsistencies between maintenance of daytime leaf water potential and response of transpiration rate under drought. J Exp Bot 65:6205–6218
Crain JL, Wei Y, Barker J, Thompson SM, Alderman PD, Reynolds M, Zhang N, Poland J (2016) Development and deployment of a portable field phenotyping platform. Crop Sci 56:965
Cseri A, Sass L, Törjék O, Pauk J, Vass I, Dudits D (2013) Monitoring drought responses of barley genotypes with semi-robotic phenotyping platform and association analysis between recorded traits and allelic variants of some stress genes. AJCS 7:1560–1570
Deikman J, Petracek M, Heard JE (2012) Drought tolerance through biotechnology: improving translation from the laboratory to farmers’ fields. Curr Opin Biotechnol 23:243–250
Delalieux S, Van Aardt J, Keulemans W, Coppin P (2005) Detection of biotic stress (Venturia inaequalis) in apple trees using hyperspectral analysis. 4th EARSeL Work. Imaging Spectrosc. pp 1–13
Diamond J (1997) Guns, germs and steel: the fates of human societies. W. W Norton, New York
Donnini S, Guidi L, Degl’Innocenti E, Zocchi G (2013) Image changes in chlorophyll fluorescence of cucumber leaves in response to iron deficiency and resupply. J Plant Nutr Soil Sci 176:734–742
Dunford R, Michel K, Gagnage M, Piégay H, Trémelo M-L (2009) Potential and constraints of Unmanned Aerial Vehicle technology for the characterization of Mediterranean riparian forest. Int J Remote Sens 30:4915–4935
Feng X, Yu C, Chen Y, Peng J, Ye L, Shen T, Wen H, He Y (2018) Non-destructive determination of shikimic acid concentration in transgenic maize exhibiting glyphosate tolerance using chlorophyll fluorescence and hyperspectral imaging. Front Plant Sci 9:468
Feng X, Zhan Y, Wang Q, Yang X, Yu C, Wang H, Tang Z, Jiang D, Peng C, He Y (2020) Hyperspectral imaging combined with machine learning as a tool to obtain high-throughput plant salt-stress phenotyping. Plant J 101:1448–1461
Feng X, Zhao Y, Zhang C, Cheng P, He Y (2017) Discrimination of transgenic maize kernel using NIR hyperspectral imaging and multivariate data analysis. Sensors 17:1894
Fiorani F, Schurr U (2013) Future scenarios for plant phenotyping. Annu Rev Plant Biol 64:267–291
Foucher P, Revollon P, Vigouroux B, Chassériaux G (2004) Morphological image analysis for the detection of water stress in potted Forsythia. Biosyst Eng 89:131–138
Friedli M, Kirchgessner N, Grieder C, Liebisch F, Mannale M, Walter A (2016) Terrestrial 3D laser scanning to track the increase in canopy height of both monocot and dicot crop species under field conditions. Plant Methods 12:9
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 223:1714–1727
Gamborg OL, Phillips GC (1995) Plant cell, tissue and organ culture : fundamental methods. Kluwer Academic Publishers
García-Tejero I, Ortega-Arévalo C, Iglesias-Contreras M, Moreno J, Souza L, Tavira S, Durán-Zuazo V (2018) Assessing the crop-water status in Almond (Prunus dulcis Mill.) trees via thermal imaging camera connected to smartphone. Sensors 18:1050
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 7:2
Gonzalez-Dugo V, Zarco-Tejada P, Nicolás E, Nortes PA, Alarcón JJ, Intrigliolo DS, Fereres E (2013) Using high resolution UAV thermal imagery to assess the variability in the water status of five fruit tree species within a commercial orchard. Precis Agric 14:660–678
González-Flor C, Serrano L, Gorchs G, Pons JM (2014) Assessment of grape yield and composition using reflectance-based indices in rainfed vineyards. Publ Agron J 106:1309–1316
Granier C, Aguirrezabal L, Chenu K, Cookson SJ, Dauzat M, Hamard P, Thioux J-J, Rolland G, Bouchier-Combaud S, Lebaudy A, Muller B, Simonneau T, Tardieu F (2006) PHENOPSIS, an automated platform for reproducible phenotyping of plant responses to soil water deficit in Arabidopsis thaliana permitted the identification of an accession with low sensitivity to soil water deficit. New Phytol 169:623–635
Gregory PJ, Bengough AG, Grinev D, Schmidt S, Thomas W, Thomas (Bill) TB W, Wojciechowski T, Young IM (2009) Root phenomics of crops: opportunities and challenges. Funct Plant Biol 36:922
Gutiérrez S, Diago MP, Fernández-Novales J, Tardaguila J (2018) Vineyard water status assessment using on-the-go thermal imaging and machine learning. PLoS ONE 13:e0192037
Gutierrez M, Reynolds MP, Klatt AR (2010) Association of water spectral indices with plant and soil water relations in contrasting wheat genotypes. J Exp Bot 61:3291–3303
Hairmansis A, Berger B, Tester M, Roy SJ (2014) Image-based phenotyping for non-destructive screening of different salinity tolerance traits in rice. Rice 7:1–10
Harbinson J, Prinzenberg AE, Kruijer W, Aarts MG (2012) High throughput screening with chlorophyll fluorescence imaging and its use in crop improvement. Curr Opin Biotechnol 23:221–226
Harris BN, Sadras VO, Tester M (2010) A water-centred framework to assess the effects of salinity on the growth and yield of wheat and barley. Plant Soil 336:377–389
Harshavardhan VT, Van Son L, Seiler C, Junker A, Weigelt-Fischer K, Klukas C, Altmann T, Sreenivasulu N, Bäumlein H, Kuhlmann M (2014) AtRD22 and AtUSPL1, Members of the plant-specific BURP domain family involved in Arabidopsis thaliana drought tolerance. PLoS ONE 9:e110065
Hartmann A, Czauderna T, Hoffmann R, Stein N, Schreiber F (2011) HTPheno: an image analysis pipeline for high-throughput plant phenotyping. BMC Bioinf 12:148
Hayes JE, Pallotta M, Baumann U, Berger B, Langridge P, Sutton T (2013) Germanium as a tool to dissect boron toxicity effects in barley and wheat. Funct Plant Biol 40:618–627
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:e97047
Hu Y, Knapp S, Schmidhalter U (2020) Advancing high-throughput phenotyping of wheat in early selection cycles. Remote Sens 12:574
Humplík JF, Lazár D, Husičková A, Spíchal L (2015) Automated phenotyping of plant shoots using imaging methods for analysis of plant stress responses—a review. Plant Methods 11:29
Jahnke S, Menzel MI, van Dusschoten D, Roeb GW, Bühler J, Minwuyelet S, Blümler P, Temperton VM, Hombach T, Streun M, Beer S, Khodaverdi M, Ziemons K, Coenen HH, Schurr U (2009) Combined MRI-PET dissects dynamic changes in plant structures and functions. Plant J 59:634–644
Jansen M, Gilmer F, Biskup B, Nagel KA, Rascher U, Fischbach A, Briem S, Dreissen G, Tittmann S, Braun S, De Jaeger I, Metzlaff M, Schurr U, Scharr H, Walter A (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
Jeudy C, Adrian M, Baussard C, Bernard C, Bernaud E, Bourion V, Busset H, Cabrera-Bosquet L, Cointault F, Han S, Lamboeuf M, Moreau D, Pivato B, Prudent M, Trouvelot S, Truong HN, Vernoud V, Voisin A-S, Wipf D, Salon C (2016) RhizoTubes as a new tool for high throughput imaging of plant root development and architecture: test, comparison with pot grown plants and validation. Plant Methods 12:31
Ji-Yong S, Xiao-Bo Z, Jie-Wen Z, Kai-Liang W, Zheng-Wei C, Xiao-Wei H, De-Tao Z, Holmes M (2012) Nondestructive diagnostics of nitrogen deficiency by cucumber leaf chlorophyll distribution map based on near infrared hyperspectral imaging. Sci Hortic 138:190–197
Jiang Y, Li C, Robertson JS, Sun S, Xu R, Paterson AH (2018) GPhenoVision: a ground mobile system with multi-modal imaging for field-based high throughput phenotyping of cotton. Sci Rep 8:1213
Jimenez-Berni JA, Deery DM, Rozas-Larraondo P, Condon (Tony) G A, Rebetzke GJ, James RA RA, Bovill WD, Furbank RT, Sirault XRR (2018) High throughput determination of plant height, ground cover, and above-ground biomass in wheat with LiDAR. Front Plant Sci 9:237
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
Joshi DC, Singh V, Hunt C, Mace E, van Oosterom E, Sulman R, Jordan D, Hammer G (2017) Development of a phenotyping platform for high throughput screening of nodal root angle in sorghum. Plant Methods 13:56
Kaplan H (2014) Practical applications of infrared thermal sensing and imaging equipment. Igarss. https://doi.org/10.1007/s13398-014-0173-7.2
Kicherer A, Herzog K, Bendel N, Klück H-C, Backhaus A, Wieland M, Rose J, Klingbeil L, Läbe T, Hohl C, Petry W, Kuhlmann H, Seiffert U, Töpfer R (2017) Phenoliner: a new field phenotyping platform for grapevine research. Sensors 17:1625
Kim JY (2020) Roadmap to high throughput phenotyping for plant breeding. J Biosyst Eng 45:43–55
Kirchgessner N, Liebisch F, Yu K, Pfeifer J, Friedli M, Hund A, Walter A (2017) The ETH field phenotyping platform FIP: a cable-suspended multi-sensor system. Funct Plant Biol 44:154
Kiser MR, Reid CD, Crowell AS, Phillips RP, Howell CR (2008) Exploring the transport of plant metabolites using positron emitting radiotracers. HFSP J 2:189–204
Knipling EB (1970) Physical and Physiological basis for the reflectance of visible and near-infrared radiation from vegetation. Remote Sens Environ 1:155–159
Konanz S, Kocsányi L, Buschmann C (2014) Advanced multi-color fluorescence imaging system for detection of biotic and abiotic stresses in leaves. Agriculture 4:79–95
Kong W, Zhang C, Liu F, Nie P, He Y (2013) Rice seed cultivar identification using near-infrared hyperspectral imaging and multivariate data analysis. Sensors 13:8916–8927
Krause MR, Mondal S, Crossa J, Singh RP, Pinto F, Haghighattalab A, Shrestha S, Rutkoski J, Gore MA, Sorrells ME, Poland J (2020) Aerial high-throughput phenotyping enabling indirect selection for grain yield at the early-generation seed-limited stages in breeding programs. Crop Sci 2:20259
Kuijken RCP, Van Eeuwijk FA, Marcelis LFM, Bouwmeester HJ (2015) Root phenotyping: from component trait in the lab to breeding. J Exp Bot 66:5389–5401
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
Läuchli A, Grattan SR (2007) Plant growth and development under salinity stress. In: Jenks MA, Hasegawa PM, Jain SM (eds) Advances in molecular breeding toward drought and salt tolerant crops. Springer, Netherlands, pp 1–32
Leinonen I, Jones HG (2004) Combining thermal and visible imagery for estimating canopy temperature and identifying plant stress. J Exp Bot 55:1423–1431
Li H, Feng H, Guo C, Yang S, Huang W, Xiong X, Liu J, Chen G, Liu Q, Xiong L, Liu K, Yang W (2020) High-throughput phenotyping accelerates the dissection of the dynamic genetic architecture of plant growth and yield improvement in rapeseed. Plant Biotechnol J pbi.13396
Li L, Zhang Q, Huang D (2014) A review of imaging techniques for plant phenotyping. Sensors 14:20078–20111
Liebisch F, Kirchgessner N, Schneider D, Walter A, Hund A (2015) Remote, aerial phenotyping of maize traits with a mobile multi-sensor approach. Plant Methods 11:9
Liu S, Baret F, Abichou M, Boudon F, Thomas S, Zhao K, Fournier C, Andrieu B, Irfan K, Hemmerlé M, de Solan B (2017) Estimating wheat green area index from ground-based LiDAR measurement using a 3D canopy structure model. Agric For Meteorol 247:12–20
Liu T, Li R, Zhong X, Jiang M, Jin X, Zhou P, Liu S, Sun C, Guo W (2018) Estimates of rice lodging using indices derived from UAV visible and thermal infrared images. Agric For Meteorol 252:144–154
Liu Z, Shi J, Zhang L, Huang J (2010) Discrimination of rice panicles by hyperspectral reflectance data based on principal component analysis and support vector classification. J Zhejiang Univ Sci B 11:71–78
Lopes MS, Reynolds MP (2010) Partitioning of assimilates to deeper roots is associated with cooler canopies and increased yield under drought in wheat. Funct Plant Biol 37:147–156
López-López M, Calderón R, González-Dugo V, Zarco-Tejada PJ, Fereres E (2016) Early detection and quantification of almond red leaf blotch using high-resolution hyperspectral and thermal imagery. Remote Sens. https://doi.org/10.3390/rs8040276
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 5:1121–1142
Ludovisi R, Tauro F, Salvati R, Khoury S, Mugnozza Scarascia G, Harfouche A (2017) UAV-based thermal imaging for high-throughput field phenotyping of black poplar response to drought. Front Plant Sci 8:1681
Makanza R, Zaman-Allah M, Cairns JE, Magorokosho C, Tarekegne A, Olsen M, Prasanna BM (2018) High-throughput phenotyping of canopy cover and senescence in maize field trials using aerial digital canopy imaging. Remote Sens 10:330
Le Marié C, Kirchgessner N, Marschall D, Walter A, Hund A (2014) Rhizoslides: paper-based growth system for non-destructive, high throughput phenotyping of root development by means of image analysis. Plant Methods 10:13
Mazis A, Das CS, Morgan PB, Stoerger V, Hiller J, Ge Y, Awada T (2020) Application of high-throughput plant phenotyping for assessing biophysical traits and drought response in two oak species under controlled environment. For Ecol Manage 465:118101
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 13:18
Minchin PEH, Thorpe MR (2003) Using the short-lived isotope 11C in mechanistic studies of photosynthate transport. Funct Plant Biol 30:831
Minervini M, Abdelsamea MM, Tsaftaris SA (2014) Image-based plant phenotyping with incremental learning and active contours. Ecol Inform 23:35–48
Mir RR, Reynolds M, Pinto F, Khan MA, Bhat MA (2019) High-throughput phenotyping for crop improvement in the genomics era. Plant Sci 282:60–72
Moghimi A, Yang C, Anderson JA (2020) Aerial hyperspectral imagery and deep neural networks for high-throughput yield phenotyping in wheat. Comput Electron Agric 172:105299
Moller M, Alchanatis V, Cohen Y, Meron M, Tsipris J, Naor A, Ostrovsky V, Sprintsin M, Cohen S (2006) Use of thermal and visible imagery for estimating crop water status of irrigated grapevine. J Exp Bot 58:827–838
Montes JM, Melchinger AE, Reif JC (2007) Novel throughput phenotyping platforms in plant genetic studies. Trends Plant Sci 12:433–436
Montes JM, Technow F, Dhillon BS, Mauch F, Melchinger AE (2011) High-throughput non-destructive biomass determination during early plant development in maize under field conditions. F Crop Res 121:268–273
Moreira FF, Oliveira HR, Volenec JJ, Rainey KM, Brito LF (2020) Integrating high-throughput phenotyping and statistical genomic methods to genetically improve longitudinal traits in crops. Front Plant Sci 11:681
Moses WW (2011) Fundamental limits of spatial resolution in PET. Nucl Instrum Methods Phys Res A 648(Supple):S236–S240
Munns R, James RA, Sirault XRR, Furbank RT, Jones HG (2010) New phenotyping methods for screening wheat and barley for beneficial responses to water deficit. J Exp Bot 61:3499–3507
Neilson EH, Edwards AM, Blomstedt CK, Berger B, Møller BL, Gleadow RM (2015) Utilization of a high-throughput 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 66:1817–1832
Nguyen HT, Lee B-W (2006) Assessment of rice leaf growth and nitrogen status by hyperspectral canopy reflectance and partial least square regression. Eur J Agron 24:349–356
Oerke E-C, Fröhling P, Steiner U (2011) Thermographic assessment of scab disease on apple leaves. Precis Agric 12:699–715
Omasa K, Hosoi F, Konishi A (2006) 3D LiDar imaging for detecting and understanding plant responses and canopy structure. J Exp Bot 58:881–898
Patrick A, Pelham S, Culbreath A, Holbrook CC, De Godoy IJ, Li C (2017) High throughput phenotyping of tomato spot wilt disease in peanuts using unmanned aerial systems and multispectral imaging. IEEE Instrum Meas Mag 20:4–12
Pérez-Bueno ML, Pineda M, Cabeza FM, Barón M (2016) Multicolor fluorescence imaging as a candidate for disease detection in plant phenotyping. Front Plant Sci 7:1790
Petrozza A, Santaniello A, Summerer S, Di TG, Di TD, Paparelli E, Piaggesi A, Perata P, Cellini F (2014) Physiological responses to Megafol ® treatments in tomato plants under drought stress: a phenomic and molecular approach. Sci Hortic 174:185–192
Pieruschka R, Schurr U (2019) Plant phenotyping: past, present, and future. Plant Phenomics 2019:1–6
Pourreza A, Lee W, Lu J, Roberts P (2016) Development of a multiband sensor for citrus black spot disease detection. 13th Int. Conf. Precis. Agric. pp 1–7
Prado SA, Cabrera-Bosquet L, Grau A, Coupel-Ledru A, Millet EJ, Welcker C, Tardieu F (2018) Phenomics allows identification of genomic regions affecting maize stomatal conductance with conditional effects of water deficit and evaporative demand. Plant Cell Environ 41:314–326
Prasanna BM, Araus JL, Crossa J, Cairns JE, Palacios N, Das B, Magorokosho C (2013) High-throughput and precision phenotyping for cereal breeding programs. In: Gupta P, Varshney R (eds) Cereal genomics II. Springer, Netherlands, pp 341–374
Qiu Q, Sun N, Bai H, Wang N, Fan Z, Wang Y, Meng Z, Li B, Cong Y (2019) Field-based high-throughput phenotyping for maize plant using 3D LiDAR point cloud generated with a “Phenomobile.” Front Plant Sci 10:554
Quirós Vargas JJ, Zhang C, Smitchger JA, McGee RJ, Sankaran S (2019) Phenotyping of plant biomass and performance traits using remote sensing techniques in pea (Pisum sativum L.). Sensors 19:2031
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 6:619
Rajendran K, Tester M, Roy SJ (2009) Quantifying the three main components of salinity tolerance in cereals. Plant Cell Environ 32:237–249
Rebetzke GJ, Jimenez-Berni J, Fischer RA, Deery DM, Smith DJ (2019) Review: high-throughput phenotyping to enhance the use of crop genetic resources. Plant Sci 282:40–48
Rogers HH, Bottomley PA (1987) In Situ nuclear magnetic resonance imaging of roots: influence of soil type, ferromagnetic particle content, and soil water. Agron J 79:957
Rousseau C, Belin E, Bove E, Rousseau D, Fabre F, Berruyer R, Guillaumès J, Manceau C, Jacques M-A, Boureau T (2013) High throughput quantitative phenotyping of plant resistance using chlorophyll fluorescence image analysis. Plant Methods 9:17
Rousseau C, Hunault G, Gaillard S, Bourbeillon J, Montiel G, Simier P, Campion C, Jacques MA, Belin E, Boureau T (2015) Phenoplant: a web resource for the exploration of large chlorophyll fluorescence image datasets. Plant Methods 11:24
Sagan V, Maimaitijiang M, Sidike P, Eblimit K, Peterson K, Hartling S, Esposito F, Khanal K, Newcomb M, Pauli D, Ward R, Fritschi F, Shakoor N, Mockler T (2019) UAV-based high resolution thermal imaging for vegetation monitoring, and plant phenotyping using ICI 8640 P, FLIR Vue Pro R 640, and thermoMap cameras. Remote Sens 11:330
Saito K, Matsuda F (2010) Metabolomics for functional genomics, systems biology, and biotechnology. Annu Rev Plant Biol 61:463–489
Salas Fernandez MG, Bao Y, Tang L, Schnable PS (2017) A high-throughput, field-based phenotyping technology for tall biomass crops. Plant Physiol 174:2008–2022
Satbhai SB, Göschl C, Busch W (2017) Automated high-throughput root phenotyping of Arabidopsis thaliana under nutrient deficiency conditions. Methods Mol Biol 1610:135–153
Schilling RK, Marschner P, Shavrukov Y, Berger B, Tester M, Roy SJ, Plett DC (2014) Expression of the Arabidopsis vacuolar H + -pyrophosphatase gene (AVP1) improves the shoot biomass of transgenic barley and increases grain yield in a saline field. Plant Biotechnol J 12:378–386
Schindelin J, Arganda-carreras I, Frise E, Kaynig V, Longair M, Pietzsch T, Preibisch S, Rueden C, Saalfeld S, Schmid B, Tinevez J, White DJ, Hartenstein V, Eliceiri K, Tomancak P, Cardona A (2012) Fiji: an open-source platform for biological-image analysis. Nat Methods 9(7):676–682
Schnurbusch T, Hayes J, Sutton T (2010) Boron toxicity tolerance in wheat and barley: Australian perspectives. Breed Sci 60:297–304
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 36:880–892
Shafiekhani A, Kadam S, Fritschi F, DeSouza G (2017) Vinobot and Vinoculer: two robotic platforms for high-throughput field phenotyping. Sensors 17:214
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 38:184–192
Shakoor N, Northrup D, Murray S, Mockler TC (2019) Big data driven agriculture: big data analytics in plant breeding, genomics, and the use of remote sensing technologies to advance crop productivity. Plant Phenome J 2:1–8
Shi Ji-yong S, Xiao-bo Z, Jie-wen Z, Han-ping M, Kai-liang W, Zheng-wei C, Xiao-wei H (2011) Diagnostics of nitrogen deficiency in mini-cucumber plant by near infrared reflectance spectroscopy. Afr J Biotechnol 10:19687–19692
Shi Y, Thomasson JA, Murray SC, Pugh NA, Rooney WL, Shafian S, Rajan N, Rouze G, Morgan CLS, Neely HL, Rana A, Bagavathiannan MV, Henrickson J, Bowden E, Valasek J, Olsenholler J, Bishop MP, Sheridan R, Putman EB, Popescu S, Burks T, Cope D, Ibrahim A, McCutchen BF, Baltensperger DD, Avant RV, Vidrine M, Yang C (2016) Unmanned Aerial vehicles for high-throughput phenotyping and agronomic research. PLoS ONE 11:e0159781
Skirycz A, Vandenbroucke K, Clauw P, Maleux K, De Meyer B, Dhondt S, Pucci A, Gonzalez N, Hoeberichts F, Tognetti VB, Galbiati M, Tonelli C, Van Breusegem F, Vuylsteke M, Inzé D (2011) Survival and growth of Arabidopsis plants given limited water are not equal. Nat Biotechnol 29:212–214
Slota M, Maluszynski M, Szarejko I (2017) Root Phenotyping Pipeline for Cereal Plants. In: Jankowicz-Cieslak J, Tai T, Kumlehn J, Till B (eds) Biotechnologies for plant mutation breeding. Springer International Publishing, Cham, pp 157–172
Stahl A, Wittkop B, Snowdon RJ (2020) High-resolution digital phenotyping of water uptake and transpiration efficiency. Trends Plant Sci 25:429–433
Sugiura R, Tsuda S, Tamiya S, Itoh A, Nishiwaki K, Murakami N, Shibuya Y, Hirafuji M, Nuske S (2016) Field phenotyping system for the assessment of potato late blight resistance using RGB imagery from an unmanned aerial vehicle. Biosyst Eng 148:1–10
Sun S, Li C, Paterson AH, Jiang Y, Xu R, Robertson JS, Snider JL, Chee PW (2018) In-field high throughput phenotyping and cotton plant growth analysis using LiDAR. Front Plant Sci 9:16
Svensgaard J, Roitsch T, Christensen S (2014) Development of a mobile multispectral imaging platform for precise field phenotyping. Agronomy 4:322–336
Takayama K, Nishina H (2007) Early detection of water stress in tomato plants based on projected plant area. Environ Control Biol 45:241–249
Tanger P, Klassen S, Mojica JP, Lovell JT, Moyers BT, Baraoidan M, Naredo MEB, McNally KL, Poland J, Bush DR, Leung H, Leach JE, McKay JK (2017) Field-based high throughput phenotyping rapidly identifies genomic regions controlling yield components in rice. Sci Rep 7:42839
Tatagiba SD, Damatta FMRFA (2014) Leaf gas exchange and chlorophyll a fluorescence imaging of rice leaves infected with Monographella albescens. Biochem Cell Biol 105:180–188
Thomas CL, Graham NS, Hayden R, Meacham MC, Neugebauer K, Nightingale M, Dupuy LX, Hammond JP, White PJ, Broadley MR (2016) High-throughput phenotyping (HTP) identifies seedling root traits linked to variation in seed yield and nutrient capture in field-grown oilseed rape (Brassica napus L.). Ann Bot 118:655–665
Thompson AL, Thorp KR, Conley M, Andrade-Sanchez P, Heun JT, Dyer JM, White JW (2018) Deploying a proximal sensing cart to identify drought-adaptive traits in upland cotton for high-throughput phenotyping. Front Plant Sci 9:507
Tisné S, Serrand Y, Bach L, Gilbault E, Ben Ameur R, Balasse H, Voisin R, Bouchez D, Durand-Tardif M, Guerche P, Chareyron G, Da Rugna J, Camilleri C, Loudet O (2013) Phenoscope: an automated large-scale phenotyping platform offering high spatial homogeneity. Plant J 74:534–544
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
Valle B, Simonneau T, Boulord R, Sourd F, Frisson T, Ryckewaert M, Hamard P, Brichet N, Dauzat M, Christophe A (2017) PYM: a new, affordable, image-based method using a Raspberry Pi to phenotype plant leaf area in a wide diversity of environments. Plant Methods 13:98
Vasseur F, Bontpart T, Dauzat M, Granier C, Vile D (2014) Multivariate genetic analysis of plant responses to water deficit and high temperature revealed contrasting adaptive strategies. J Exp Bot 65:6457–6469
Verel I, Visser GWM, van Dongen GA (2005) The promise of immuno-PET in radioimmunotherapy. J Nucl Med 46(Suppl 1):164S-S171
Violle C, Navas M-L, Vile D, Kazakou E, Fortunel C, Hummel I, Garnier E (2007) Let the concept of trait be functional! Oikos 116:882–892
Wahabzada M, Mahlein A-K, Bauckhage C, Steiner U, Oerke E-C, Kersting K (2015) Metro maps of plant disease dynamics–automated mining of differences using hyperspectral images. PLoS ONE 10:e0116902
van der Walt S, Schönberger JL, Nunez-Iglesias J, Boulogne F, Warner JD, Yager N, Gouillart E, Yu T (2014) Scikit-image: image processing in Python. PeerJ 2:e453
Wang H, Qian X, Zhang L, Xu S, Li H, Xia X, Dai L, Xu L, Yu J, Liu X (2018) A method of high throughput monitoring crop physiology using chlorophyll fluorescence and multispectral imaging. Front Plant Sci 9:407
Wasaya A, Zhang X, Fang Q, Yan Z (2018) Root phenotyping for drought tolerance: a review. Agronomy 8:241
White JW, Andrade-Sanchez P, Gore MA, Bronson KF, Coffelt TA, Conley MM, Feldmann KA, French AN, Heun JT, Hunsaker DJ, Jenks MA, Kimball BA, Roth RL, Strand RJ, Thorp KR, Wall GW, Wang G (2012) Field-based phenomics for plant genetics research. F Crop Res 133:101–112
Wijekoon CP, Goodwin PH, Hsiang T (2008) Quantifying fungal infection of plant leaves by digital image analysis using Scion Image software. J Microbiol Methods 74:94–101
Winterhalter L, Mistele B, Jampatong S, Schmidhalter U, Jones H, Price A, Moreau D, Bogard M, Griffiths S, Orford S, Hubbart S, Foulkes M (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
Wiwart M, Fordoński G, Zuk-Gołaszewska K, Suchowilska E (2009) Early diagnostics of macronutrient deficiencies in three legume species by color image analysis. Comput Electron Agric 65:125–132
Xie C, Shao Y, Li X, He Y (2015) Detection of early blight and late blight diseases on tomato leaves using hyperspectral imaging. Sci Rep 5:16564
Yan Yang Y, Chai R, He Y (2012) Early detection of rice blast (Pyricularia) at seedling stage in Nipponbare rice variety using near-infrared hyper-spectral image. Afr J Biotechnol 11:6809–6817
Yang W, Feng H, Zhang X, Zhang J, Doonan JH, Batchelor WD, Xiong L, Yan J (2020) Crop phenomics and high-throughput phenotyping: past decades, current challenges, and future perspectives. Mol Plant 13:187–214
Yang W, Guo Z, Huang C, Duan L, Chen G, Jiang N, Fang W, Feng H, Xie W, Lian X, Wang G, Luo Q, Zhang Q, Liu Q, Xiong L (2014) Combining high-throughput phenotyping and genome-wide association studies to reveal natural genetic variation in rice. Nat Commun 5:5087
Yazdanbakhsh N, Fisahn J (2009) High throughput phenotyping of root growth dynamics, lateral root formation, root architecture and root hair development enabled by PlaRoM. Funct Plant Biol 36:938
Zarco-Tejada PJ, Berni JAJ, Suárez L, Sepulcre-Cantó G, Morales F, Miller JR (2009) Imaging chlorophyll fluorescence with an airborne narrow-band multispectral camera for vegetation stress detection. Remote Sens Environ 113:1262–1275
Zhang X, Hause RJ, Borevitz JO (2012) Natural genetic variation for growth and development revealed by high-throughput phenotyping in Arabidopsis thaliana. G3 Genes Genomes Genet 2:29–34
Zhang X, Huang C, Wu D, Qiao F, Li W, Duan L, Wang K, Xiao Y, Chen G, Liu Q, Xiong L, Yang W, Yan J (2017) High-throughput phenotyping and QTL mapping reveals the genetic architecture of maize plant growth. Plant Physiol 173:1554–1564
Zhang X, Liu F, He Y, Li X (2012) Application of hyperspectral imaging and chemometric calibrations for variety discrimination of maize seeds. Sensors 12:17234–17246
Zhang C, Liu F, Zhang H-L, Kong W-W, He Y (2014) Identification of varieties of black bean using ground based hyperspectral imaging. Guang Pu Xue Yu Guang Pu Fen Xi 34:746–750
Zhao C, Zhang Y, Du J, Guo X, Wen W, Gu S, Wang J, Fan J (2019) Crop phenomics: current status and perspectives. Front Plant Sci 10:714
Zhu H, Chu B, Fan Y, Tao X, Yin W, He Y (2017) Hyperspectral imaging for predicting the internal quality of kiwi fruits based on variable selection algorithms and chemometric models. Sci Rep 7:7845
Funding
No funding was available.
Author information
Authors and Affiliations
Contributions
SJ and VC performed the literature survey and wrote the manuscript. RCY and NRY provided the concept and theme and corrected the manuscript.
Corresponding author
Ethics declarations
Conflict of Interest
The authors declare that they have no conflict of interest.
Ethics Approval
Not applicable.
Consent to Participate
Not applicable.
Consent for Publication
Not applicable.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Jangra, S., Chaudhary, V., Yadav, R.C. et al. High-Throughput Phenotyping: A Platform to Accelerate Crop Improvement. Phenomics 1, 31–53 (2021). https://doi.org/10.1007/s43657-020-00007-6
Published:
Issue Date:
DOI: https://doi.org/10.1007/s43657-020-00007-6