Plant Phenotyping

Abstract

Every plant science experiment starts with a design that will be adapted to answer a specific biological question and involves evaluation of phenotypic traits. Plant phenotyping has advanced from manual measurements of physiologically relevant parameters to high‐throughput phenotyping platforms that use robotics and imaging sensors. Yet, this game‐changing technology has its own challenges, namely data analysis and interpretation. The improved quality of the sensors used in the phenotying experiment provides increased understanding, however the insight provided on the research question is limited by the experimental design. Aspects such as replication or spatial variability are important to consider when designing the experiment conducted in highly controlled environment as well as under field conditions. With wider availability of cameras and other sensors, we are able to record increasing number of plant traits. This results in the phenotypic bottleneck moving from data acquisition to data analysis. Throughout this article, we present practical considerations and potential shortcomings of phenotyping systems and suggest some solutions to the challenges of plant phenotyping through streamlined and reproducible data analysis pipelines.

Key Concepts

  • Plant phenotypes are complex, resulting from the interaction between genotype and environment.
  • The phenotype can be divided into traits, for example, biomass can be dissected into leaf area, branches/tillers, fruits.
  • The relationship between traits depends on the environment, genotype and treatment.
  • Each phenotyping method is optimised to answer a specific research question.
  • Exploring the relationships between phenotypes and their changes across genotypes/treatments increases our understanding of the underlying physiological processes.
  • Experimental design should include an optimal number of replicates and sample randomisation to ensure a successful interpretation of phenotypic results.
  • Phenotyping results require detailed statistical analysis to be adequately interpreted.

Keywords: plant phenotype; trait; experimental design; plant growth; variability; high‐throughput phenotyping; data analysis

Figure 1. The array of the most prevalent phenotyping systems for answering a research question. The control over the uniformity of conditions for the experiments decreases with the increasing agronomic relevance. While one has the highest control of the conditions in experimental systems using, for example, agar plates and hydroponics, these experimental setups are carrying little relevance to growth conditions typically used in agricultural settings. The control of conditions, including humidity, light, temperature and nutrient availability increases with darker shades of yellow, whereas the agronomic relevance of measured traits such as plant growth, morphometry, photosynthesis and yield increases with darker shades of green.
Figure 2. Guidelines for choosing the growing system to address your biological question. The most commonly used phenotypes are listed in green boxes, while the phenotyping systems are indicated in the yellow boxes.
Figure 3. Imaging of plant size throughout time using RGB imaging. (a) Critical factors to consider when taking RGB pictures, namely contrasting background (typically blue gives the best contrast with plants), size reference, reference for white balance and positioning of light to prevent shade casting. (b) For an accurate estimation of plant size with 3D architecture, two side view images (typically at a 90° angle from each other) and one top view image are required. (c) Increase of area over time can be plotted for individual plants using image‐based phenotyping. The increase in area for individual plants is represented by separate grey curves. Depending on the developmental stage used for the experiment, the growth of the plant can be divided into early stage, where the plants grow exponentially, followed by mid‐stage where linear growth is more prevalent, and late stage, where the growth slows down. Dependent on the experimental interest, the differences between the genotypes/treatments can be compared across the entire growth period or individual intervals. (d) Estimation of plant growth by fitting, for example, linear or exponential functions to the increase of area over time. The graphs represent an observed area at specific time points with green dots, while the fitted function is indicated by the blue dashed line. The formulas for individual functions are presented above the graphs, where LGR stands for Linear Growth Rate, and RGR for Relative Growth Rate, as the projected increase in area at one time point (t), using an exponential model is relative to the area at the previous time point (t−1).
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References

Alahmad S, El Hassouni K, Bassi FM, et al. (2019) A major root architecture QTL responding to water limitation in durum wheat. Frontiers in Plant Science 10: 436.

Almugbel R, Hung L‐H, Hu J, et al. (2018) Reproducible Bioconductor workflows using browser‐based interactive notebooks and containers. Journal of the American Medical Informatics Association 25: 4–12.

Araus JL and Cairns JE (2014) Field high‐throughput phenotyping: the new crop breeding frontier. Trends in Plant Science 19: 52–61.

Araus JL, Kefauver SC, Zaman‐Allah M, Olsen MS and Cairns JE (2018) Translating high‐throughput phenotyping into genetic gain. Trends in Plant Science 23: 451–466.

Armengaud P (2009) EZ‐Rhizo software: the gateway to root architecture analysis. Plant Signaling & Behavior 4: 139–141.

Atkinson JA, Pound MP, Bennett MJ and Wells DM (2019) Uncovering the hidden half of plants using new advances in root phenotyping. Current Opinion in Biotechnology 55: 1–8.

Bolhàr‐Nordenkampf HR and Öquist G (1993) Chlorophyll fluorescence as a tool in photosynthesis research. In: Hall DO, Scurlock JMO, Bolhàr‐Nordenkampf HR, Leegood RC and Long SP (eds) Photosynthesis and Production in a Changing Environment: A Field and Laboratory Manual, pp 193–206. Springer Netherlands: Dordrecht.

Bray AL and Topp CN (2018) The quantitative genetic control of root architecture in maize. Plant & Cell Physiology 59: 1919–1930.

Brien CJ, Berger B, Rabie H and Tester M (2013) Accounting for variation in designing greenhouse experiments with special reference to greenhouses containing plants on conveyor systems. Plant Methods 9: 5.

Casler MD (2015) Fundamentals of experimental design: guidelines for designing successful experiments. Agronomy Journal 107: 692–705.

Champely S (2009) pwr: Basic functions for power analysis. R package version 1.1.1. The R Foundation: Vienna

Chawade A, van Ham J, Blomquist H, et al. (2019) High‐throughput field‐phenotyping tools for plant breeding and precision agriculture. Agronomy 9: 258.

Cruz JA, Savage LJ, Zegarac R, et al. (2016) Dynamic environmental photosynthetic imaging reveals emergent phenotypes. Cell Systems 2: 365–377.

Ćwiek‐Kupczyńska H, Altmann T, Arend D, et al. (2016) Measures for interoperability of phenotypic data: minimum information requirements and formatting. Plant Methods 12: 44.

Das A, Schneider H, Burridge J, et al. (2015) Digital imaging of root traits (DIRT): a high‐throughput computing and collaboration platform for field‐based root phenomics. Plant Methods 11: 51.

Dhondt S, Wuyts N and Inzé D (2013) Cell to whole‐plant phenotyping: the best is yet to come. Trends in Plant Science 18: 428–439.

Dobrescu A, Scorza LCT, Tsaftaris SA and McCormick AJ (2017) A “Do‐It‐Yourself” phenotyping system: measuring growth and morphology throughout the diel cycle in rosette shaped plants. Plant Methods 13: 95.

Fahlgren N, Gehan MA and Baxter I (2015) Lights, camera, action: high‐throughput plant phenotyping is ready for a close‐up. Current Opinion in Plant Biology 24: 93–99.

Faragó D, Sass L, Valkai I, Andrási N and Szabados L (2018) PlantSize offers an affordable, non‐destructive method to measure plant size and color in vitro. Frontiers in Plant Science 9: 219.

Faul F, Erdfelder E, Lang A‐G and Buchner A (2007) G*Power 3: a flexible statistical power analysis program for the social, behavioral, and biomedical sciences. Behavior Research Methods 39: 175–191.

Fiorani F and Schurr U (2013) Future scenarios for plant phenotyping. Annual Review of Plant Biology 64: 267–291.

Fletcher A, Christopher J, Hunter M, Rebetzke G and Chenu K (2018) A low‐cost method to rapidly and accurately screen for transpiration efficiency in wheat. Plant Methods 14: 77.

Galkovskyi T, Mileyko Y, Bucksch A, et al. (2012) GiA Roots: software for the high throughput analysis of plant root system architecture. BMC Plant Biology 12: 116.

Gehan MA, Fahlgren N, Abbasi A, et al. (2017) PlantCV v2: image analysis software for high‐throughput plant phenotyping. PeerJ 5: e4088.

Ghosal S, Blystone D, Singh AK, et al. (2018) An explainable deep machine vision framework for plant stress phenotyping. Proceedings of the National Academy of Sciences of the United States of America 115: 4613–4618.

Goodman SN, Fanelli D and Ioannidis JPA (2016) What does research reproducibility mean? Science Journal of Translational Medicine 8: 341ps12.

Hamblin MT, Buckler ES and Jannink J‐L (2011) Population genetics of genomics‐based crop improvement methods. Trends in Genetics 27: 98–106.

Head ML, Holman L, Lanfear R, Kahn AT and Jennions MD (2015) The extent and consequences of p‐hacking in science. PLoS Biology 13: e1002106.

Heymans A, Couvreur V, LaRue T, Paez‐Garcia A (2019) GRANAR, a new computational tool to better understand the functional importance of root anatomy. bioRxiv

Ho J, Tumkaya T, Aryal S, Choi H and Claridge‐Chang A (2019) Moving beyond P values: data analysis with estimation graphics. Nature Methods 16: 565–566.

Holland JB, Nyquist WE and Cervantes‐Martínez CT (2010) Estimating and interpreting heritability for plant breeding: an update. Plant Breeding Reviews: 9–112.

Houle D, Govindaraju DR and Omholt S (2010) Phenomics: the next challenge. Nature Reviews. Genetics 11: 855–866.

Julkowska MM, Hoefsloot HCJ, Mol S, et al. (2014) Capturing Arabidopsis root architecture dynamics with ROOT‐FIT reveals diversity in responses to salinity. Plant Physiology 166: 1387–1402.

Julkowska MM, Koevoets IT, Mol S, et al. (2017) Genetic components of root architecture remodeling in response to salt stress. Plant Cell 29: 3198–3213.

Julkowska MM, Saade S, Agarwal G, et al. (2018) MVAPP—multivariate analysis application for streamlined data analysis and curation. Plant Physiology 23: 44.

Kharkina TG, Ottosen C‐O and Rosenqvist E (1999) Effects of root restriction on the growth and physiology of cucumber plants. Physiologia Plantarum 105: 434–441.

Krzywinski M and Altman N (2013a) Power and sample size. Nature Methods 10: 1139.

Krzywinski M and Altman N (2013b) Significance, P values and t‐tests. Nature Methods 10: 1041–1042.

Krzywinski M and Altman N (2014a) Comparing samples—part I. Nature Methods 11: 215.

Krzywinski M and Altman N (2014b) Points of significance: nonparametric tests. Nature Methods 11: 467–468.

Kuhlgert S, Austic G, Zegarac R, et al. (2016) MultispeQ Beta: a tool for large‐scale plant phenotyping connected to the open PhotosynQ network. Royal Society Open Science 3: 160592.

Kuijken RCP, van Eeuwijk FA, Marcelis LFM and Bouwmeester HJ (2015) Root phenotyping: from component trait in the lab to breeding. Journal of Experimental Botany 66: 5389–5401.

Landl M, Schnepf A, Vanderborght J, et al. (2018) Measuring root system traits of wheat in 2D images to parameterize 3D root architecture models. Plant and Soil 425: 457–477.

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

Lobet G, Pagès L and Draye X (2011) A novel image‐analysis toolbox enabling quantitative analysis of root system architecture. Plant Physiology 157: 29–39.

Maes WH and Steppe K (2019) Perspectives for remote sensing with unmanned aerial vehicles in precision agriculture. Trends in Plant Science 24: 152–164.

Mahlein A‐K (2016) Plant disease detection by imaging sensors‐‐parallels and specific demands for precision agriculture and plant phenotyping. Plant Disease 100: 241–251.

Ma L, Shi Y, Siemianowski O, et al. (2019) Hydrogel‐based transparent soils for root phenotyping in vivo. Proceedings of the National Academy of Sciences of the United States of America 116: 11063–11068.

Massonnet C, Vile D, Fabre J, et al. (2010) Probing the reproducibility of leaf growth and molecular phenotypes: a comparison of three Arabidopsis accessions cultivated in ten laboratories. Plant Physiology 152: 2142–2157.

Medrano H, Tomás M, Martorell S, et al. (2015) From leaf to whole‐plant water use efficiency (WUE) in complex canopies: limitations of leaf WUE as a selection target. The Crop Journal 3: 220–228.

Minervini M, Giuffrida MV, Perata P and Tsaftaris SA (2017) Phenotiki: an open software and hardware platform for affordable and easy image‐based phenotyping of rosette‐shaped plants. The Plant Journal 90: 204–216.

Mishra Y, Jänkänpää HJ, Kiss AZ, et al. (2012) Arabidopsis plants grown in the field and climate chambers significantly differ in leaf morphology and photosystem components. BMC Plant Biology 12: 6.

Mohamed A, Monnier Y, Mao Z, et al. (2017) An evaluation of inexpensive methods for root image acquisition when using rhizotrons. Plant Methods 13: 11.

Murchie EH and Lawson T (2013) Chlorophyll fluorescence analysis: a guide to good practice and understanding some new applications. Journal of Experimental Botany 64: 3983–3998.

Mutka AM and Bart RS (2014) Image‐based phenotyping of plant disease symptoms. Frontiers in Plant Science 5: 734.

Negrão S, Schmöckel SM and Tester M (2017) Evaluating physiological responses of plants to salinity stress. Annals of Botany 119: 1–11.

Paez‐Garcia A, Motes CM, Scheible W‐R, et al. (2015) Root traits and phenotyping strategies for plant improvement. Plants 4: 334–355.

Peñuelas J and Filella I (1998) Visible and near‐infrared reflectance techniques for diagnosing plant physiological status. Trends in Plant Science 3: 151–156.

Pieruschka R and Schurr U (2019) Plant phenotyping: past, present, and future. Plant Phenomics 2019: 7507131.

Pilbeam DJ (2015) Breeding crops for improved mineral nutrition under climate change conditions. Journal of Experimental Botany 66: 6079.

Pommier C, Michotey C, Cornut G, et al. (2019) Applying FAIR principles to plant phenotypic data management in GnpIS. Plant Phenomics 2019: 1671403.

Poorter H, Bühler J, van Dusschoten D, Climent J and Postma JA (2012a) Pot size matters: a meta‐analysis of the effects of rooting volume on plant growth. Functional Plant Biology 39: 839–850.

Poorter H, Fiorani F, Stitt M, et al. (2012b) The art of growing plants for experimental purposes: a practical guide for the plant biologist. Functional Plant Biology 39: 821–838.

Poorter H, Fiorani F, Pieruschka R, et al. (2016) Pampered inside, pestered outside? Differences and similarities between plants growing in controlled conditions and in the field. The New Phytologist 212: 838–855.

Rellán‐Álvarez R, Lobet G, Lindner H, et al. (2015) GLO‐Roots: an imaging platform enabling multidimensional characterization of soil‐grown root systems. eLife. DOI: 10.7554/elife.07597.

Robbins NE 2nd and Dinneny JR (2015) The divining root: moisture‐driven responses of roots at the micro‐ and macro‐scale. Journal of Experimental Botany 66: 2145–2154.

Ryan AC, Dodd IC, Rothwell SA, et al. (2016) Gravimetric phenotyping of whole plant transpiration responses to atmospheric vapour pressure deficit identifies genotypic variation in water use efficiency. Plant Science 251: 101–109.

Sankaran S, Khot LR, Espinoza CZ, et al. (2015) Low‐altitude, high‐resolution aerial imaging systems for row and field crop phenotyping: a review. European Journal of Agronomy 70: 112–123.

Schnepf A, Leitner D, Landl M, et al. (2018) CRootBox: a structural‐functional modelling framework for root systems. Annals of Botany 121: 1033–1053.

Shi R, Junker A, Seiler C and Altmann T (2018) Phenotyping roots in darkness: disturbance‐free root imaging with near infrared illumination. Functional Plant Biology 45: 400–411.

Silva‐Navas J, Moreno‐Risueno MA, Manzano C, et al. (2015) D‐Root: a system for cultivating plants with the roots in darkness or under different light conditions. The Plant Journal 84: 244–255.

Strock CF, Schneider HM, Galindo‐Castañeda T, et al. (2019) Laser ablation tomography for visualization of root colonization by edaphic organisms. Journal of Experimental Botany. DOI: 10.1093/jxb/erz271.

Takagi H, Tamiru M, Abe A, et al. (2015) MutMap accelerates breeding of a salt‐tolerant rice cultivar. Nature Biotechnology 33: 445–449.

Tardieu F, Cabrera‐Bosquet L, Pridmore T and Bennett M (2017) Plant phenomics, from sensors to knowledge. Current Biology 27: R770–R783.

Taylor SH and Long SP (2019) Phenotyping photosynthesis on the limit‐‐a critical examination of RACiR. The New Phytologist 221: 621–624.

Tilling AK, O'Leary GJ, Ferwerda JG, et al. (2007) Remote sensing of nitrogen and water stress in wheat. Field Crops Research 104: 77–85.

Tovar JC, Hoyer JS, Lin A, et al. (2018) Raspberry Pi‐powered imaging for plant phenotyping. Applications in Plant Sciences 6: e1031.

Verslues PE, Agarwal M, Katiyar‐Agarwal S, Zhu J and Zhu J‐K (2006) Methods and concepts in quantifying resistance to drought, salt and freezing, abiotic stresses that affect plant water status. The Plant Journal 45: 523–539.

Walter A, Liebisch F and Hund A (2015) Plant phenotyping: from bean weighing to image analysis. Plant Methods 11: 14.

Weissgerber TL, Milic NM, Winham SJ and Garovic VD (2015) Beyond bar and line graphs: time for a new data presentation paradigm. PLoS Biology 13: e1002128.

Zhu J, Ingram PA, Benfey PN and Elich T (2011) From lab to field, new approaches to phenotyping root system architecture. Current Opinion in Plant Biology 14: 310–317.

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Negrão, Sónia, and Julkowska, Magdalena M(Mar 2020) Plant Phenotyping. In: eLS. John Wiley & Sons Ltd, Chichester. http://www.els.net [doi: 10.1002/9780470015902.a0028894]