Imaging technologies for plant high-throughput phenotyping: a review

Abstract

Phenomics studies a variety of phenotypic plant traits and is the key to understanding genetic functions and environmental effects on plants. With the rapid development of genomics, many plant phenotyping platforms have been developed to study complex traits related to the growth, yield, and adaptation to biotic or abiotic stress, but the ability to acquire high-throughput phenotypic data has become the bottleneck in the study of plant genomics. In recent years, researchers around the world have conducted extensive experiments and research on high-throughput, image-based phenotyping techniques, including visible light imaging, fluorescence imaging, thermal imaging, spectral imaging, stereo imaging, and tomographic imaging. This paper considers imaging technologies developed in recent years for high-throughput phenotyping, reviews applications of these technologies in detecting and measuring plant morphological, physiological, and pathological traits, and compares their advantages and limitations.

Keywords

high-throughput phenotyping / imaging technology / morphological traits / pathological traits / physiological traits

References

[1] United Nations Department of Economic and Social Affairs Population Division. World population projected to reach 9.7 billion 2050. Available at the United Nations website on Oct. 2, 2018

[2] Alston J M, Beddow J M, Pardey P G. Agricultural research, productivity, and food prices in the long run. Science, 2009, 325(5945): 1209–1210

[3] Garrett K A, Dendy S P, Frank E E, Rouse M N, Travers S E. Climate change effects on plant disease: genomes to ecosystems. Annual Review of Phytopathology, 2006, 44(1): 489–509

[4] Phillips R L, Godshalk B. Mobilizing science to break yield barriers. Crop Science, 2010, 50(S1): S99–S108

[5] Araus J L, Cairns J E. Field high-throughput phenotyping: the new crop breeding frontier. Trends in Plant Science, 2014, 19(1): 52–61

[6] McMullen M D, Kresovich S, Villeda H S, Bradbury P, Li H, Sun Q, Flint-Garcia S, Thornsberry J, Acharya C, Bottoms C, Brown P, Browne C, Eller M, Guill K, Harjes C, Kroon D, Lepak N, Mitchell S E, Peterson B, Pressoir G, Romero S, Rosas M O, Salvo S, Yates H, Hanson M, Jones E, Smith S, Glaubitz J C, Goodman M, Ware D, Holland J B, Buckler E S. Genetic properties of the maize nested association mapping population. Science, 2009, 325(5941): 737–740

[7] Furbank R T, Tester M. Phenomics—technologies to relieve the phenotyping bottleneck. Trends in Plant Science, 2011, 16(12): 635–644

[8] Sankaran S, Khot L R, Espinoza C Z, Jarolmasjed S, Sathuvalli V R, Vandemark G J, Miklas P N, Carter A H, Pumphrey M O, Knowles N R, Pavek M J. Low-altitude, high-resolution aerial imaging systems for row and field crop phenotyping: a review. European Journal of Agronomy, 2015, 70: 112–123

[9] Munns R, James R A, Sirault X R, Furbank R T, Jones H G. New phenotyping methods for screening wheat and barley for beneficial responses to water deficit. Journal of Experimental Botany, 2010, 61(13): 3499–3507

[10] Busemeyer L, Mentrup D, Möller K, Wunder E, Alheit K, Hahn V, Maurer H P, Reif J C, Würschum T, Müller J, Rahe F, Ruckelshausen A. BreedVision—a multi-sensor platform for non-destructive field-based phenotyping in plant breeding. Sensors, 2013, 13(3): 2830–2847

[11] Barker J, Zhang N Q, Sharon J, Steeve R, Wang X, Wei Y, Poland J. Development of a field-based high-throughput mobile phenotyping platform. Computers and Electronics in Agriculture, 2016, 122: 74–85

[12] Crain J, Wei Y, Barker J, Thompson S M, Alderman P D, Reynolds M, Zhang N Q, Poland J. Development and deployment of a portable field phenotyping platform. Crop Science, 2016, 56(3): 965–975

[13] Kumar J, Pratap A, Kumar S. Phenomics in Crop Plants: Trends, Options and Limitations. New Delhi: Springer International, 2015

[14] Sui R X, Wilkerson J B, Wilhelm L R, Tompkins F D. A microcomputer-based morphometer for bush-type plants. Computers and Electronics in Agriculture, 1989, 4(1): 43–58

[15] Montes J M, Melchinger A E, Reif J C. Novel throughput phenotyping platforms in plant genetic studies. Trends in Plant Science, 2007, 12(10): 433–436

[16] McCarthy C, Hancock N, Raine S. Apparatus and infield evaluations of a prototype machine vision system for cotton plant internode length measurement. Journal of Cotton Science, 2010, 14(4): 221–232

[17] Lan Y, Zhang H, Lacey R, Hoffman W, Wu W. Development of an integration sensor and instrumentation system for measuring crop conditions. Development,  2009,  126(9): 1869–1878

[18] Comar A, Burger P, de Solan B D, Ferderic B, Daumard F, Hanocq J F. A semi-automatic system for high throughput phenotyping wheat cultivars in-field conditions: description and first results. Functional Plant Biology, 2012, 39(10–11): 914–924

[19] White J W, Conley M M. A flexible, low-cost cart for proximal sensing. Crop Science, 2013, 53(4): 1646–1649

[20] Andrade-Sanchez P, Gore M A, Heun J T, Thorp K R, Carmo-Silva A E, French A N, Salvucci M E, White J W. Development and evaluation of a field-based high-throughput phenotyping platform. Functional Plant Biology, 2014, 41(1): 68–79

[21] Pask A, Pietragalla J, Mullan D, Reynolds M Physiological Breeding II: A Field Guide to Wheat Phenotyping. Mexico: CIMMYT, 2012

[22] Gao M, Heijden G V D, Vos J, Eveleens B A, Marcelis L F M. Estimation of leaf area for large scale phenotyping and modeling of rose genotypes. Scientia Horticulturae, 2012, 138: 227–234

[23] Wu C Y, Niu Z, Tang Q, Huang W. Estimating chlorophyll content from hyperspectral vegetation indices: modeling and validation. Agricultural and Forest Meteorology, 2008, 148(8–9): 1230–1241

[24] Zhu J, Ingram P A, Benfey P N, Elich T. From lab to field, new approaches to phenotyping root system architecture. Current Opinion in Plant Biology, 2011, 14(3): 310–317

[25] Vila H, Hugalde I, Filippo M D. Estimation of leaf water potential by thermographic and spectral measurements in grapevine. Revista de Investigaciones Agropecuarias, 2011, 37(1): 46–53

[26] Strange R N, Scott P R. Plant disease: a threat to global food security. Annual Review of Phytopathology, 2005, 43(1): 83–116

[27] Mutka A M, Bart R S. Image-based phenotyping of plant disease symptoms. Frontiers in Plant Science, 2015, 5: 734

[28] Stoll M, Schultz H R, Berkelmann-Loehnertz B. Early pathogen detection under different water status and the assessment of spray application in vineyards through the use of thermal imagery. Precision Agriculture, 2008, 9(6): 407–417

[29] Iyer-Pascuzzi A S, Symonova O, Mileyko Y, Hao Y, Belcher H, Harer J, Weitz J S, Benfey P N. Imaging and analysis platform for automatic phenotyping and trait ranking of plant root systems. Plant Physiology, 2010, 152(3): 1148–1157

[30] Lee W S, Alchanatis V, Yang C H, Hirafuji M, Moshou D, Li C. Sensing technologies for precision specialty crop production. Computers and Electronics in Agriculture, 2010, 74(1): 2–33

[31] Gorbea E, Calatayud A. Applications of chlorophyll fluorescence imaging technique in horticultural research: a review. Scientia Horticulturae, 2013, 138: 24–35

[32] Li L, Zhang Q, Huang D. A review of imaging techniques for plant phenotyping. Sensors, 2014, 14(11): 20078–20111

[33] Kaplan H. Practical Applications of Infrared Thermal Sensing and Imaging Equipment. Bellingham: SPIE Press, 2007

[34] Gaussorgues G. La Thermographie Infrarouge: Principes Technologies Applications. France: Tech.& Doc./Lavoisier, 1999

[35] Liu R, Wei R, Wang H P, Chen G. Principles of modern radar. In: Principlesof Modern Radar, Vol. III: Radar Applications. New Jersey: SciTech Publishing, 2014: 691–748

[36] Darrigues A, Hall J, Van der Knaap E, Francis D, Dujmovic N, Gray S. Tomato analyzer-color test: a new tool for efficient digital phenotyping. Journal of the American Society for Horticultural Science, 2008, 133(4): 579–586

[37] Gonzalo M J, Brewer M T, Anderson C, Sullivan D, Gray S, Van der Knaap E. Tomato fruit shape analysis using morphometric and morphology attributes implemented in Tomato Analyzer software program. Journal of the American Society for Horticultural Science, 2009, 134(1): 77–87

[38] Wang X, Wu C, Hirafuji M. Visible light image-based method for sugar content classification of citrus. PLoS One, 2016, 11(1): e0147419

[39] Zakaluk R, Ranjan R S. Predicting the leaf water potential of potato plants using RGB reflectance. Canadian Biosystems Engineering, 2008, 50: 7.1–7.12

[40] Golzarian M R, Frick R A, Rajendran K, Berger B, Roy S, Tester M, Lun D S. Accurate inference of shoot biomass from high-throughput images of cereal plants. Plant Methods, 2011, 7(1): 2

[41] Wang C, Li X. Study on nondestructive detection method of potato grading based on multi-source information fusion. Dissertation for the Doctoral Degree. Wuhan: Huazhong Agricultural University, 2014 (in Chinese)

[42] Raza S A. Registration of thermal and visible light images of diseased plants using silhouette extraction in the wavelet domain. Elsevier Science Inc., 2015, 48(7): 2119–2128

[43] Pérez-Bueno M L, Pineda M, Cabeza F M, Barón M. Multicolor fluorescence imaging as a candidate for disease detection in plant phenotyping. Frontiers in Plant Science, 2016, 7: 1790

[44] Swarbrick P J, Schulze-Lefert P, Scholes J D. Metabolic consequences of susceptibility and resistance (race-specific and broad-spectrum) in barley leaves challenged with powdery mildew. Plant, Cell & Environment, 2006, 29(6): 1061–1076

[45] Chaerle L, Hagenbeek D, De Bruyne E, Van Der Straeten D. Chlorophyll fluorescence imaging for disease-resistance screening of sugar beet. Plant Cell, Tissue and Organ Culture, 2007, 91(2): 97–106

[46] Barbagallo R P, Oxborough K, Pallett K E, Baker N R. Rapid, noninvasive screening for perturbations of metabolism and plant growth using chlorophyll fluorescence imaging. Plant Physiology, 2003, 132(2): 485–493

[47] Bürling K, Hunsche M, Noga G J. Quantum yield of non-regulated energy dissipation in PSII (Y(NO)) for early detection of leaf rust (Puccinia triticina) infection in susceptible and resistant wheat (Triticum aestivum l.) cultivars. Precision Agriculture, 2010, 11(6): 703–716

[48] Narváez F J Y, Pedregal J S D, Prieto P A, Torres-Torriti M, Cheein F A A. LiDAR and thermal images fusion for ground-based 3D characterisation of fruit trees. Biosystems Engineering, 2016, 151: 479–494

[49] Xu H R, Ying Y B. Application of infrared thermal imaging in identification of citrus on trees.   Journal of Infrared and Millimeter Waves, 2004, 23(5): 353–356

[50] Cohen Y, Alchanatis V, Meron M, Saranga Y, Tsipris J. Estimation of leaf water potential by thermal imagery and spatial analysis. Journal of Experimental Botany, 2005, 56(417): 1843–1852

[51] Grant O M, Tronina L, Jones H G, Chaves M M. Exploring thermal imaging variables for the detection of stress responses in grapevine under different irrigation regimes. Journal of Experimental Botany, 2007, 58(4): 815–825

[52] Romano G, Zia-Khan S, Spreer W, Sanchez C, Cairns J, Araus J L, Müller J. Use of thermography for high throughput phenotyping of tropical maize adaptation in water stress. Computers and Electronics in Agriculture, 2011, 79(1): 67–74

[53] Ziakhan S, Romano G, Spreer C, Sanchez C, Cairns J, Araus J, Müller J. Infrared thermal imaging as a rapid tool for identifying water-stress tolerant maize genotypes of different phenology. Journal Agronomy & Crop Science, 2013, 199(2): 1–10

[54] Calderón R, Navas-Cortés J A, Lucena C, Zarco-Tejada P J. High-resolution airborne hyperspectral and thermal imagery for early detection of Verticillium wilt of olive using fluorescence, temperature and narrow-band spectral indices. Remote Sensing of Environment, 2013, 139: 231–245

[55] Dumont J, Hirvonen T, Heikkinen V, Mistretta M, Granlund L, Himanen K, Fauch L, Porali I, Hiltunen J, Keski-Saari S, Nygren M, Oksanen E, Hauta-Kasari M, Keinänen M. Thermal and hyperspectral imaging for Norway spruce (Picea abies) seeds screening. Computers and Electronics in Agriculture, 2015, 116: 118–124

[56] Jafari M, Minaee S, Safaie N. Detection of pre-symptomatic rose powdery-mildew and gray-mold diseases based on thermal vision. Infrared Physics & Technology, 2017, 85: 170–183

[57] Borregaard T, Nielsen H, Nørgaard L, Have H. Crop–weed discrimination by line imaging spectroscopy. Journal of Agricultural Engineering Research, 2000, 75(4): 389–400

[58] Mao W, Wang Y, Wang Y. Spectrum analysis of crop and weeds at seedling. Spectroscopy and Spectral Analysis, 2005, 25(6): 984

[59] Serrano L, González-Flor C, Gorchs G. Assessment of grape yield and composition using the reflectance based water index in mediterranean rainfed vineyards. Remote Sensing of Environment, 2012, 118: 249–258

[60] Thiel M, Rath T, Ruckelshausen A. Plant moisture measurement in field trials based on nir spectral imaging—a feasability study. Computer Image Analysis in Agriculture, 2010: 26–27

[61] Yi Q X, Bao A M, Wang Q, Zhao J. Estimation of leaf water content in cotton by means of hyperspectral indices. Computers and Electronics in Agriculture, 2013, 90: 144–151

[62] Abboud T, Bamsey M, Paul A, Graham T, Braham S, Noumeir R, Berinstain A, Ferl R. Deployment of a fully-automated green fluorescent protein imaging system in a high arctic autonomous greenhouse. In: Abboud T, ed. Plant health imaging systems for space biology and plant growth studies. Montréal: À L’ École De Technologie Supérieure, 2013: 13–37

[63] Blackburn G A. Hyperspectral remote sensing of plant pigments. Journal of Experimental Botany, 2007, 58(4): 855–867

[64] Sasaki Y, Okamoto T, Imou K, Torii T. Automatic diagnosis of plant disease: recognition between healthy and diseased leaf. IFAC Proceedings, 1998, 31(5): 145–150

[65] Sabatier D R, Moon C M, Mhora T T, Rutherford R S, Laing M D. Near-infrared reflectance (NIR) spectroscopy as a high-throughput screening tool for pest and disease resistance in a sugarcane breeding programme. International Sugar Journal, 2014, 116(1388): 580–583

[66] Moshou D, Bravo C, Obert R, West J S, Ramon H, Vougioukas S, Bochtis D. Intelligent multi-sensor system for the detection and treatment of fungal diseases in arable crops. Biosystems Engineering, 2011, 108(4): 311–321

[67] Takizawa H, Ezaki N, Mizuno S, Yamamoto S. Plant recognition by integrating color and range data obtained through stereo vision. JACIII, 2005, 9(6): 630–636

[68] Kazmi W, Foix S, Alenyà G, Andersen H J. Indoor and outdoor depth imaging of leaves with time-of-flight and stereo vision sensors: analysis and comparison. ISPRS Journal of Photogrammetry and Remote Sensing, 2014, 88: 128–146

[69] Kraft M, Natália R, De Freitas S, Munack A. Test of a 3D time of flight camera for shape measurements of plants. ResearchGate, 2018: 108–115

[70] Hargreaves C E, Gregory P J, Bengough A G. Measuring root traits in barley (Hordeum vulgare ssp. vulgare and ssp. spontaneum) seedlings using gel chambers, soil sacs and X-ray microtomography. Plant and Soil, 2008, 316(1): 285–297

[71] Li H, Wang K, Cao Q, Bian H. Measurement of plant height based on stereoscopic vision under the condition of a single camera. Intelligent Control & Automation,  2010: 6026–6030 (in Chinese)

[72] Omasa K, Hosoi F, Konishi A. 3D lidar imaging for detecting and understanding plant responses and canopy structure. Journal of Experimental Botany, 2007, 58(4): 881–898

[73] Hosoi F, Omasa K. Estimating vertical plant area density profile and growth parameters of a wheat canopy at different growth stages using three-dimensional portable lidar imaging. ISPRS Journal of Photogrammetry and Remote Sensing, 2009, 64(2): 151–158

[74] Windt C W, Vergeldt F J, de Jager P A, van As H. MRI of long-distance water transport: a comparison of the phloem and xylem flow characteristics and dynamics in poplar, castor bean, tomato and tobacco. Plant, Cell & Environment, 2006, 29(9): 1715–1729

[75] Jahnke S, Menzel M I, van Dusschoten D, Roeb G W, Bühler J, Minwuyelet S, Blümler P, Temperton V M, Hombach T, Streun M, Beer S, Khodaverdi M, Ziemons K, Coenen H H, Schurr U. Combined MRI-PET dissects dynamic changes in plant structures and functions. Plant Journal, 2009, 59(4): 634–644

[76] Han H, Yi M. MRI can reveal metabolic changes in lily bulbs in vivo during dormancy release. Life Sciences, 2012, 55(11): 1002–1006

[77] Baker N R, Rosenqvist E. Applications of chlorophyll fluorescence can improve crop production strategies: an examination of future possibilities. Journal of Experimental Botany, 2004, 55(403): 1607–1621

[78] Cheng H D, Jiang X H, Sun Y, Wang J. Color image segmentation: advances and prospects. Pattern Recognition, 2001, 34(12): 2259–2281

[79] Materka A, Strzelecki M. Texture analysis methods—a review. Technical University of Lodz, Institute of Electronics, COST B11 report, 1998, 1–33

[80] Pietrzykowski E, Stone C, Pinkard E A, Mohammed C. Effects of Mycosphaerella leaf disease on the spectral reflectance properties of juvenile Eucalyptus globules foliage. Forest Pathology, 2006, 36(5): 334–348

[81] Delalieux S, van Aardt J, Keulemans W, Keulemans W, Schrevens E, Coppin P. Detection of biotic stress (Venturia inaequalis) in apple trees using hyperspectral data: non-parametric statistical approaches and physiological implications. European Journal of Agronomy, 2007, 27(1): 130–143

[82] Rumpf T, Mahlein A K, Steiner U, Oerke E C, Dehne H W, Plümer L. Early detection and classification of plant diseases with Support Vector Machines based on hyperspectral reflectance. Computers and Electronics in Agriculture, 2010, 74(1): 91–99

[83] Moshou D, Bravo C, West J, Wahlen T, McCartney A, Ramon H. Automatic detection of ‘yellow rust’ in wheat using reflectance measurements and neural networks. Computers and Electronics in Agriculture, 2004, 44(3): 173–188

[84] Quin J, Burks T F, Ritenour M, Bonn W G. Detection of citrus canker using hyperspectral reflectance imaging with spectral information divergence. Journal of Food Engineering, 2009, 93(2): 183–191

[85] Cabrera-Bosquet L, Crossa J, von Zitzewitz J, Serret M D, Araus J L. High-throughput phenotyping and genomic selection: the frontiers of crop breeding converge. Journal of Integrative Plant Biology, 2012, 54(5): 312–320

[86] Mahlein A K, Oerke E C, Steiner U, Dehne H W. Recent advances in sensing plant diseases for precision crop protection. European Journal of Plant Pathology, 2012, 133(1): 197–209

Acknowledgements

This work was supported by China Scholarships for Study Abroad.

Compliance with ethics guidelines

Yong Zhang and Naiqian Zhang declare that they have no conflicts of interest or financial conflicts to disclose.

This article is a review and does not contain any studies with human or animal subjects performed by any of the authors.

RIGHTS & PERMISSIONS

The Author(s) 2018. Published by Higher Education Press. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0)