Functional Plant Biology Functional Plant Biology Society
Plant function and evolutionary biology
RESEARCH ARTICLE

Development and evaluation of a field-based high-throughput phenotyping platform

Pedro Andrade-Sanchez A E , Michael A. Gore B C , John T. Heun A , Kelly R. Thorp B , A. Elizabete Carmo-Silva B D , Andrew N. French B , Michael E. Salvucci B and Jeffrey W. White B
+ Author Affiliations
- Author Affiliations

A Department of Agricultural and Biosystems Engineering, University of Arizona, Maricopa Agricultural Center, 37860 W. Smith-Enke Road, Maricopa, AZ 85138, USA.

B US Department of Agriculture, Agricultural Research Service, Arid-Land Agricultural Research Center, 21881 North Cardon Lane, Maricopa, AZ 85138, USA.

C Present address: Department of Plant Breeding and Genetics, Cornell University, Ithaca, NY 14853, USA.

D Present address: Rothamsted Research, Plant Biology and Crop Science Department, Harpenden, Hertsfordshire, AL5 2JQ, UK.

E Corresponding author. Email: pandrade@ag.arizona.edu

Functional Plant Biology 41(1) 68-79 https://doi.org/10.1071/FP13126
Submitted: 4 May 2013  Accepted: 18 July 2013   Published: 5 September 2013

Abstract

Physiological and developmental traits that vary over time are difficult to phenotype under relevant growing conditions. In this light, we developed a novel system for phenotyping dynamic traits in the field. System performance was evaluated on 25 Pima cotton (Gossypium barbadense L.) cultivars grown in 2011 at Maricopa, Arizona. Field-grown plants were irrigated under well watered and water-limited conditions, with measurements taken at different times on 3 days in July and August. The system carried four sets of sensors to measure canopy height, reflectance and temperature simultaneously on four adjacent rows, enabling the collection of phenotypic data at a rate of 0.84 ha h–1. Measurements of canopy height, normalised difference vegetation index and temperature all showed large differences among cultivars and expected interactions of cultivars with water regime and time of day. Broad-sense heritabilities (H2)were highest for canopy height (H2 = 0.86–0.96), followed by the more environmentally sensitive normalised difference vegetation index (H2 = 0.28–0.90) and temperature (H2 = 0.01–0.90) traits. We also found a strong agreement (r2 = 0.35–0.82) between values obtained by the system, and values from aerial imagery and manual phenotyping approaches. Taken together, these results confirmed the ability of the phenotyping system to measure multiple traits rapidly and accurately.

Additional keywords: cotton, genetics, Gossypium barbadense, phenomics, proximal sensing.


References

Allen RG, Pereira LS, Raes D, Smith M (1998) ‘Crop evapotranspiration – guide-lines for computing crop water requirements. FAO irrigation and drainage paper 56.’ (Food and Agriculture Organization of the United Nations: Rome)

Box GEP, Cox DR (1964) An analysis of transformations. Journal of the Royal Statistical Society. Series B. Methodological 26, 211–252.

Brown PW (1989) ‘Accessing the Arizona Meteorological Network (AZMET) by vomputer. Extension report no. 8733.’ (University of Arizona: Tucson)

Carmo-Silva AE, Gore MA, Andrade-Sanchez P, French AN, Hunsaker DJ, Salvucci ME (2012) Decreased CO2 availability and inactivation of Rubisco limit photosynthesis in cotton plants under heat and drought stress in the field. Environmental and Experimental Botany 83, 1–11.
Decreased CO2 availability and inactivation of Rubisco limit photosynthesis in cotton plants under heat and drought stress in the field.CrossRef | 1:CAS:528:DC%2BC38XnvFOrsbw%3D&md5=89e0f03e6efb33c5adc3dfaad3833a7aCAS |

Comar A, Burger P, de Solan B, Baret F, Daumard F, Hanocq JF (2012) A semi-automatic system for high throughput phenotyping wheat cultivars in-field conditions: description and first results. Functional Plant Biology 39, 914–924.
A semi-automatic system for high throughput phenotyping wheat cultivars in-field conditions: description and first results.CrossRef |

Furbank RT, Tester M (2011) Phenomics – technologies to relieve the phenotyping bottleneck. Trends in Plant Science 16, 635–644.
Phenomics – technologies to relieve the phenotyping bottleneck.CrossRef | 1:CAS:528:DC%2BC3MXhsFOhu7%2FJ&md5=099c62d008e03f9befe0d41b0e830cecCAS | 22074787PubMed |

Fussell J, Rundquist D, Harrington JA (1986) On defining remote sensing. Photogrammetric Engineering and Remote Sensing 52, 1507–1511.

Göttsche F-M, Olesen FS (2001) Modelling of diurnal cycles of brightness temperature extracted from METEOSAT data. Remote Sensing of Environment 76, 337–348.
Modelling of diurnal cycles of brightness temperature extracted from METEOSAT data.CrossRef |

Holland JB, Nyquist WE, Cervantes-Martínez CT (2003) Estimating and interpreting heritability for plant breeding: an update. Plant Breeding Reviews 22, 9–112.

Houle D, Govindaraju DR, Omholt S (2010) Phenomics: the next challenge. Nature Reviews. Genetics 11, 855–866.
Phenomics: the next challenge.CrossRef | 1:CAS:528:DC%2BC3cXhsVejs7jK&md5=36e8324a024a825766f83caf50682fd0CAS | 21085204PubMed |

Hunt R (1979) Plant growth analysis: the rationale behind the use of the fitted mathematical function. Annals of Botany 43, 245–249.

Kimes D (1981) Remote sensing of temperature profiles in vegetation canopies using multiple view angles and inversion techniques. IEEE Transactions on Geoscience and Remote Sensing GE-19, 85–90.
Remote sensing of temperature profiles in vegetation canopies using multiple view angles and inversion techniques.CrossRef |

Kutner MH, Nachtsheim CJ, Neter J, Li W (2004) ‘Applied linear statistical models.’ 4th edn. (McGraw-Hill: Boston)

Lan Y, Zhang H, Lacey R, Hoffman W, Wu W (2009) Development of an integration sensor and instrumentation system for measuring crop conditions. Agricultural Engineering International: CIGR Journal 11, 1–16.

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

Milton EJ (1987) Principles of field spectroscopy. International Journal of Remote Sensing 8, 1807–1827.
Principles of field spectroscopy.CrossRef |

Montes JM, Melchinger AE, Reif JC (2007) Novel throughput phenotyping platforms in plant genetic studies. Trends in Plant Science 12, 433–436.
Novel throughput phenotyping platforms in plant genetic studies.CrossRef | 1:CAS:528:DC%2BD2sXhtFWgsLvM&md5=567cc494eb0cf427b9b327c45b939e95CAS | 17719833PubMed |

Piepho H-P, Möhring J (2007) Computing heritability and selection response from unbalanced plant breeding trials. Genetics 177, 1881–1888.
Computing heritability and selection response from unbalanced plant breeding trials.CrossRef | 18039886PubMed |

Qi J, Pinter P, Clarke TR, Kimball BA, Moran MS (1997) Diagnostic assessments of plant condition using multiangular remote sensing measurements and BRDF models. Journal of Remote Sensing 1, 25–29.

Ruixiu S, Wilkerson JB, Wilhelm LR, Tompkins FD (1989) A microcomputer-based morphometer for bush-type plants. Computers and Electronics in Agriculture 4, 43–58.
A microcomputer-based morphometer for bush-type plants.CrossRef |

Rundquist D, Perk R, Leavitt B, Keydan G, Gitelson A (2004) Collecting spectral data over cropland vegetation using machine-positioning versus hand-positioning of the sensor. Computers and Electronics in Agriculture 43, 173–178.
Collecting spectral data over cropland vegetation using machine-positioning versus hand-positioning of the sensor.CrossRef |

Scotford IM, Miller PCH (2004) Estimating tiller density and leaf area index of winter wheat using spectral reflectance and ultrasonic sensing techniques. Biosystems Engineering 89, 395–408.
Estimating tiller density and leaf area index of winter wheat using spectral reflectance and ultrasonic sensing techniques.CrossRef |

White JW, Conley MM (2013) A flexible, low-cost cart for proximal sensing. Crop Science 53, 1646–1649.
A flexible, low-cost cart for proximal sensing.CrossRef |

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. Field Crops Research 133, 101–112.
Field-based phenomics for plant genetics research.CrossRef |

Wu WR, Li WM, Tang DZ, Lu HR, Worland AJ (1999) Time-related mapping of quantitative trait loci underlying tiller number in rice. Genetics 151, 297–303.


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