Nothing Special   »   [go: up one dir, main page]

skip to main content
research-article

Temporal dynamics of maize plant growth, water use, and leaf water content using automated high throughput RGB and hyperspectral imaging

Published: 01 September 2016 Publication History

Abstract

We conduct a greenhouse phenotyping study on two maize genotypes with two water regimes.Plant projected area accurately predicts shoot fresh weight, dry weight, and leaf area.Daily water consumption is derived and found to be determined by water treatments.Water use efficiency is derived and determined by plant genotype.Leaf spectra from hyperspectral images accurately predicts plant leaf water content. Automated collection of large scale plant phenotype datasets using high throughput imaging systems has the potential to alleviate current bottlenecks in data-driven plant breeding and crop improvement. In this study, we demonstrate the characterization of temporal dynamics of plant growth and water use, and leaf water content of two maize genotypes under two different water treatments. RGB (Red Green Blue) images are processed to estimate projected plant area, which are correlated with destructively measured plant shoot fresh weight (FW), dry weight (DW) and leaf area. Estimated plant FW and DW, along with pot weights, are used to derive daily plant water consumption and water use efficiency (WUE) of the individual plants. Hyperspectral images of plants are processed to extract plant leaf reflectance and correlate with leaf water content (LWC). Strong correlations are found between projected plant area and all three destructively measured plant parameters (R20.95) at early growth stages. The correlations become weaker at later growth stages due to the large difference in plant structure between the two maize genotypes. Daily water consumption (or evapotranspiration) is largely determined by water treatment, whereas WUE (or biomass accumulation per unit of water used) is clearly determined by genotype, indicating a strong genetic control of WUE. LWC is successfully predicted with the hyperspectral images for both genotypes (R2=0.81 and 0.92). Hyperspectral imaging can be a very powerful tool to phenotype biochemical traits of the whole maize plants, complementing RGB for plant morphological trait analysis.

References

[1]
G.A. Blackburn, Hyperspectral remote sensing of plant pigments, J. Exp. Bot., 58 (2007) 855-867.
[2]
M.T. Campbell, A.C. Knecht, B. Berger, C.J. Brien, D. Wang, H. Walia, Integrating image-based phenomics and association analysis to dissect the genetic architecture of temporal salinity responses in rice, Plant Physiol., 168 (2015) 1476-1489.
[3]
D. Chen, K. Neumann, S. Friedel, B. Kilian, M. Chen, T. Altmann, C. Klukas, Dissecting the phenotypic components of crop plant growth and drought responses based on high-throughput image analysis, Plant Cell, 26 (2014) 4636-4655.
[4]
A.G. Condon, R.A. Richards, G.J. Rebetzke, G.D. Farquhar, Breeding for high water-use efficiency, J. Exp. Bot., 55 (2004) 2447-2460.
[5]
N. Fahlgren, M. Feldman, M. Gehan, M.S. Wilson, C. Shyu, D.W. Bryant, S.T. Hill, C.J. McEntee, S.N. Warnasooriya, I. Kumar, T. Ficor, S. Turnipseed, K.B. Gilbert, T.P. Brutnell, J.C. Carrington, T.C. Mockler, I. Baxter, A versatile phenotyping system and analytics platform reveals diverse temporal responses to water availability in Setaria, Molecular Plant, 8 (2015) 1-16.
[6]
T. Fearn, Assessing calibrations: SEP, RPD, RER and R2, NIR News, 13 (2002) 12-14.
[7]
R.T. Furbank, M. Tester, Phenomics - technologies to relieve the phenotyping bottleneck, Trends Plant Sci., 16 (2011) 635-644.
[8]
M.R. Golzarian, R.A. Frick, K. Rajendran, B. Berger, S. Roy, M. Tester, D.S. Lun, Accurate inference of shoot biomass from high-throughput images of cereal plants, Plant Methods, 7 (2011) 2.
[9]
A.A. Gowen, C.P. O'Donnell, P.J. Cullen, J.M. Frias, Hyperspectral imaging - an emerging process analytical tool for food quality and safety control, Trends Food Sci. Technol., 18 (2007) 590-598.
[10]
C. Granier, L. Aguirrezabal, K. Chenu, S.J. Cookson, M. Dauzat, P. Hamard, J. Thioux, G. Rolland, S. Bouchier-Combaud, A. Lebaudy, B. Muller, T. Simonneau, F. Tardieu, PHENOPSIS, an automated platform for reproducible phenotyping of plant response to soil water deficit in Arabidopsis thaliana permitted the identification of an accession with low sensitivity to soil water deficit, New Phytol., 169 (2006) 623-635.
[11]
P. Grassini, K.M. Eskridge, K.G. Cassman, Distinguishing between yield advances and yield plateaus in historical crop production trends, Nat. Commun., 4 (2013) 2918.
[12]
J.F. Humplík, D. Lazár, A. Husičková, L. Spíchal, Automated phenotyping of plant shoots using imaging methods for analysis of plant stress responses - a review, Plant Methods, 11 (2015) 29.
[13]
M. Jansen, F. Gilmer, B. Biskup, K.A. Nagel, U. Rascher, A. Fischbach, S. Briem, G. Dreissen, S. Tittmann, S. Braun, I. De Jaeger, M. Metzlaff, U. Schurr, H. Scharr, A. Walter, 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 (2009) 902-914.
[14]
H.G. Jones, R. Serraj, B.R. Loveys, L. Xiong, A. Wheaton, A.H. Price, Thermal infrared imaging of crop canopies for the remote diagnosis and quantification of plant response to water stress in the field, Funct. Plant Biol., 36 (2009) 978-989.
[15]
J.Y. Kim, D.M. Glenn, Measurement of photosynthetic response to plant water stress using a multi-modal sensing system, Trans. ASABE, 58 (2015) 233-240.
[16]
A. Konishi, A. Eguchi, F. Hosoi, K. Omasa, 3D monitoring spatio-temporal effects of herbicide on a whole plant using combined range of chlorophyll a fluorescence imaging, Funct. Plant Biol., 36 (2009) 874-879.
[17]
L. Li, Q. Zhang, D. Huang, A review of imaging techniques for plant phenotyping, Sensors, 14 (2014) 20078-20111.
[18]
D.L. Mangus, A. Sharda, N. Zhang, Development and evaluation of thermal infrared imaging system for high spatial and temporal resolution crop water stress monitoring of corn within a greenhouse, Comput. Electron. Agric., 121 (2016) 149-159.
[19]
G.E. Meyer, J.C. Neto, Verification of color vegetation indices for automated crop imaging applications, Comput. Electron. Agric., 63 (2008) 282-293.
[20]
B.H. Mevik, R. Wehren, K.H. Liland, Pls: Partial Least Squares and Principal Component Regression, 2013.
[21]
R.F. Muñoz-Huerta, R.G. Guevara-Gonzalez, L.M. Contreras-Medina, I. Torres-Pacheco, J. Prado-Olivarez, R.V. Ocampo-Velazquez, A review of methods for sensing the nitrogen status in plants: advantages, disadvantages and recent advances, Sensors, 13 (2013) 10823-10843.
[22]
E.H. Neilson, A.M. Edwards, C.K. Blomstedt, B. Berger, B.L. Moller, R.M. Gleadow, 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 (2015) 1817-1832.
[23]
K.H. Norris, R.F. Barnes, J.E. Moore, J.S. Shenk, Predicting forage quality by infrared reflectance spectroscopy, J. Anim. Sci., 43 (1976) 889-897.
[24]
R Core Team, R: A Language and Environment for Statistical Computing, R Foundation for Statistical Computing, Vienna, Austria, 2015.
[25]
G. Romano, S. Zia, W. Spreer, C. Sanchez, J. Cairns, J.L. Araus, J. Müller, Use of thermography for high throughput phenotyping of tropical maize adaption in water stress, Comput. Electron. Agric., 79 (2011) 67-74.
[26]
S. Sankaran, L.R. Khot, C.Z. Espinoza, S. Jarolmasjed, V.R. Santhuvalli, G.J. Vandemark, P.N. Miklas, A.H. Carter, M.O. Pumphrey, N.R. Knowles, M.J. Pavek, Low-altitude, high-resolution aerial imaging systems for row and field crop phenotyping: a review, Eur. J. Agron., 70 (2015) 112-123.
[27]
J.S. Shenk, J.J. Workman, M.O. Westerhaus, Application of NIR spectroscopy to agricultural products, in: Handbook of Near-Infrared Analysis (3rd Edition), CRC Press, Boca Raton, FL, 2008.
[28]
R. Tuberrosa, Phenotyping for drought tolerance of crops in the genomics era, Front. Physiol., 3 (2012) 347.
[29]
W. Yang, Z. Guo, C. Huang, L. Duan, G. Chen, N. Jiang, W. Fang, H. Feng, W. Xie, X. Lian, G. Wang, Q. Luo, Q. Zhang, Q. Liu, L. Xiong, Combining high-throughput phenotyping and genome-wide association studies to reveal natural genetic variation in rice, Nat. Commun., 5 (2014) 5087.

Cited By

View all
  • (2024)A non-destructive method to quantify the nutritional status of Cannabis sativa L. using in situ hyperspectral imaging in combination with chemometricsComputers and Electronics in Agriculture10.1016/j.compag.2024.108656218:COnline publication date: 1-Mar-2024
  • (2023)3D reconstruction of plants using probabilistic voxel carvingComputers and Electronics in Agriculture10.1016/j.compag.2023.108248213:COnline publication date: 1-Oct-2023
  • (2023)Deep learning models based on hyperspectral data and time-series phenotypes for predicting quality attributes in lettuces under water stressComputers and Electronics in Agriculture10.1016/j.compag.2023.108034211:COnline publication date: 1-Aug-2023
  • Show More Cited By
  1. Temporal dynamics of maize plant growth, water use, and leaf water content using automated high throughput RGB and hyperspectral imaging

    Recommendations

    Comments

    Please enable JavaScript to view thecomments powered by Disqus.

    Information & Contributors

    Information

    Published In

    cover image Computers and Electronics in Agriculture
    Computers and Electronics in Agriculture  Volume 127, Issue C
    September 2016
    788 pages

    Publisher

    Elsevier Science Publishers B. V.

    Netherlands

    Publication History

    Published: 01 September 2016

    Author Tags

    1. Drought
    2. High throughput phenotyping
    3. Hyperspectral
    4. Image processing
    5. RGB
    6. Water use efficiency

    Qualifiers

    • Research-article

    Contributors

    Other Metrics

    Bibliometrics & Citations

    Bibliometrics

    Article Metrics

    • Downloads (Last 12 months)0
    • Downloads (Last 6 weeks)0
    Reflects downloads up to 21 Dec 2024

    Other Metrics

    Citations

    Cited By

    View all
    • (2024)A non-destructive method to quantify the nutritional status of Cannabis sativa L. using in situ hyperspectral imaging in combination with chemometricsComputers and Electronics in Agriculture10.1016/j.compag.2024.108656218:COnline publication date: 1-Mar-2024
    • (2023)3D reconstruction of plants using probabilistic voxel carvingComputers and Electronics in Agriculture10.1016/j.compag.2023.108248213:COnline publication date: 1-Oct-2023
    • (2023)Deep learning models based on hyperspectral data and time-series phenotypes for predicting quality attributes in lettuces under water stressComputers and Electronics in Agriculture10.1016/j.compag.2023.108034211:COnline publication date: 1-Aug-2023
    • (2023)Multi-view triangulation without correspondencesComputers and Electronics in Agriculture10.1016/j.compag.2023.107688206:COnline publication date: 1-Mar-2023
    • (2023)NLCS - A novel coordinate system for spatial analysis on hyperspectral leaf images and an improved nitrogen index for soybean plantsComputers and Electronics in Agriculture10.1016/j.compag.2022.107550204:COnline publication date: 1-Jan-2023
    • (2022)Non-destructive analysis of plant physiological traits using hyperspectral imagingComputers and Electronics in Agriculture10.1016/j.compag.2022.106806195:COnline publication date: 1-Apr-2022
    • (2021)Development of an automated plant phenotyping system for evaluation of salt tolerance in soybeanComputers and Electronics in Agriculture10.1016/j.compag.2021.106001182:COnline publication date: 1-Mar-2021
    • (2021)Estimation of water content in corn leaves using hyperspectral data based on fractional order Savitzky-Golay derivation coupled with wavelength selectionComputers and Electronics in Agriculture10.1016/j.compag.2021.105989182:COnline publication date: 1-Mar-2021
    • (2020)LeafSpecComputers and Electronics in Agriculture10.1016/j.compag.2019.105209169:COnline publication date: 1-Feb-2020
    • (2020)Sorghum Segmentation by Skeleton ExtractionComputer Vision – ECCV 2020 Workshops10.1007/978-3-030-65414-6_21(296-311)Online publication date: 23-Aug-2020
    • Show More Cited By

    View Options

    View options

    Media

    Figures

    Other

    Tables

    Share

    Share

    Share this Publication link

    Share on social media