A semi-automatic system for high throughput phenotyping wheat cultivars in-field conditions: description and first results
Alexis Comar A B E , Philippe Burger C , Benoit de Solan A B , Frédéric Baret B , Fabrice Daumard C D and Jean-François Hanocq BA ARVALIS Institut du végétal, 3 rue Joseph et Marie Hackin, 75116 Paris, France.
B INRA – UAPV, UMR EMMAH, Domaine Saint-Paul, Site Agroparc, 84914 Avignon, France.
C INRA – INPT, UMR 1248 AGIR, F-31320 Castanet-Tolosan, France.
D Laboratoire de Météorologie Dynamique, Equipe Fluorescence et Télédétection, Ecole Polytechnique, 91128 Palaiseau Cedex, France.
E Corresponding author. Email: alexis.comar@etd.univ-avignon.fr
Submitted: 27 February 2012 Accepted: 10 July 2012 Published: 20 August 2012
Abstract
A semi-automatic system was developed to monitor micro-plots of wheat cultivars in field conditions for phenotyping. The system is based on a hyperspectral radiometer and 2 RGB cameras observing the canopy from ~1.5 m distance to the top of the canopy. The system allows measurement from both nadir and oblique views inclined at 57.5° zenith angle perpendicularly to the row direction. The system is fixed to a horizontal beam supported by a tractor that moves along the micro-plots. About 100 micro-plots per hour were sampled by the system, the data being automatically collected and registered thanks to a centimetre precision geo-location. The green fraction (GF, the fraction of green area per unit ground area as seen from a given direction) was derived from the images with an automatic segmentation process and the reflectance spectra recorded by the radiometers were transformed into vegetation indices (VI) such as MCARI2 and MTCI. Results showed that MCARI2 is a good proxy of the GF, the MTCI as observed from 57° inclination is expected to be mainly sensitive to leaf chlorophyll pigments. The frequent measurements achieved allowed a good description of the dynamics of each micro-plot along the growth cycle. It is characterised by two periods: the first period corresponding to the vegetative stages exhibits a small rate of change of VI with time; followed by the senescence period characterised by a high rate of change. The dynamics were simply described by a bilinear model with its parameters providing high throughput metrics (HTM). A variance analysis achieved over these HTMs showed that several HTMs were highly heritable, particularly those corresponding to MCARI2 as observed from nadir, and those corresponding to the first period. Potentials of such semi-automatic measurement system are discussed for in field phenotyping applications.
Additional keywords: dynamics, green fraction, heritability, hyperspectral, vegetation indices.
References
Babar MA, Reynolds MP, Van Ginkel M, Klatt AR, Raun WR, Stone ML (2006) Spectral reflectance to estimate genetic variation for in-season biomass, leaf chlorophyll, and canopy temperature in wheat. Crop Science 46, 1046–1057.| Spectral reflectance to estimate genetic variation for in-season biomass, leaf chlorophyll, and canopy temperature in wheat.Crossref | GoogleScholarGoogle Scholar |
Baret F, Guyot G (1986) Suivi de la maturation de couverts de ble par radiometrie dans les domaines visible et proche infrarouge. Agronomie 6, 509–516.
| Suivi de la maturation de couverts de ble par radiometrie dans les domaines visible et proche infrarouge.Crossref | GoogleScholarGoogle Scholar |
Baret F, Guyot G (1991) Potentials and limits of vegetation indices for LAI and APAR assessment. Remote Sensing of Environment 35, 161–173.
| Potentials and limits of vegetation indices for LAI and APAR assessment.Crossref | GoogleScholarGoogle Scholar |
Baret F, De Solan B, Lopez-Lozano R, Ma K, Weiss M (2010) GAI estimates of row crops from downward looking digital photos taken perpendicular to rows at 57.5° zenith angle. Theoretical considerations based on 3D architecture models and application to wheat crops. Agricultural and Forest Meteorology 150, 1393–1401.
| GAI estimates of row crops from downward looking digital photos taken perpendicular to rows at 57.5° zenith angle. Theoretical considerations based on 3D architecture models and application to wheat crops.Crossref | GoogleScholarGoogle Scholar |
Benson DA, Karsch-Mizrachi I, Clark K, Lipman DJ, Ostell J, Sayers EW (2012) GenBank. Nucleic Acids Research 40, D48–D53.
| GenBank.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BC3MXhs12hur3P&md5=a9286081fe2e602af5246a5241f8117dCAS |
Boissard P, Guerif M, Pointel JG, Guinot JP (1989) Application of SPOT data to wheat yield estimation. Advances in Space Research 9, 143–154.
| Application of SPOT data to wheat yield estimation.Crossref | GoogleScholarGoogle Scholar |
Boyes DC, Zayed AM, Ascenzi R, McCaskill AJ, Hoffman NE, Davis KR, Gorlach J (2001) Growth stage-based phenotypic analysis of Arabidopsis: a model for high throughput functional genomics in plants. The Plant Cell 13, 1499–1510.
Clément A, Vigouroux B (2003) Unsupervised segmentation of scenes containing vegetation (Forsytia) and soil by hierarchical analysis of bi-dimensional histograms. Pattern Recognition Letters 24, 1951–1957.
| Unsupervised segmentation of scenes containing vegetation (Forsytia) and soil by hierarchical analysis of bi-dimensional histograms.Crossref | GoogleScholarGoogle Scholar |
Dash J, Curran PJ (2004) The MERIS terrestrial chlorophyll index. International Journal of Remote Sensing 25, 5403–5413.
| The MERIS terrestrial chlorophyll index.Crossref | GoogleScholarGoogle Scholar |
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 | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BC3MXhsFOhu7%2FJ&md5=59208026cca4aa61f90a1b57818115edCAS |
Gaju O, Allard V, Martre P, Snape JW, Heumez E, LeGouis J, Moreau D, Bogard M, Griffiths S, Orford S, Hubbart S, Foulkes MJ (2011) Identification of traits to improve the nitrogen-use efficiency of wheat genotypes. Field Crops Research 123, 139–152.
| Identification of traits to improve the nitrogen-use efficiency of wheat genotypes.Crossref | GoogleScholarGoogle Scholar |
Haboudane D, Miller JR, Pattey E, Zarco-Tejada PJ, Strachan IB (2004) Hyperspectral vegetation indices and novel algorithms for predicting green LAI of crop canopies: modeling and validation in the context of precision agriculture. Remote Sensing of Environment 90, 337–352.
| Hyperspectral vegetation indices and novel algorithms for predicting green LAI of crop canopies: modeling and validation in the context of precision agriculture.Crossref | GoogleScholarGoogle Scholar |
Höpe A, Hauer KO (2010) Three dimensional appearance characterization of diffuse standard reflection materials. Metrologia 47, 295
| Three dimensional appearance characterization of diffuse standard reflection materials.Crossref | GoogleScholarGoogle Scholar |
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. Functional Plant Biology 36, 978–989.
| Thermal infrared imaging of crop canopies for the remote diagnosis and quantification of plant responses to water stress in the field.Crossref | GoogleScholarGoogle Scholar |
Lucht W, Roujean JL (2000) Considerations in the parametric modeling of BRDF and albedo from multiangular satellite sensor observations. Remote Sensing Reviews 18, 343–379.
| Considerations in the parametric modeling of BRDF and albedo from multiangular satellite sensor observations.Crossref | GoogleScholarGoogle Scholar |
Mistele B, Schmidhalter U (2010) Tractor-based quadrilateral spectral reflectance measurements to detect biomass and total aerial nitrogen in winter wheat. Agronomy Journal 102, 499–506.
| Tractor-based quadrilateral spectral reflectance measurements to detect biomass and total aerial nitrogen in winter wheat.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BC3cXltVWquro%3D&md5=a162c1e7a0d923b799e6962b9957b437CAS |
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 | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BD2sXhtFWgsLvM&md5=cffc77d4c1f14013ee051e8f9881e8ebCAS |
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. Journal of Experimental Botany 61, 3499–3507.
| New phenotyping methods for screening wheat and barley for beneficial responses to water deficit.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BC3cXhtVert7jJ&md5=626f450aea4d5653be21fc53f83e2d18CAS |
Nelder JA, Mead R (1965) A simplex method for function minimization. Computer Journal 7, 308–313.
| A simplex method for function minimization.Crossref | GoogleScholarGoogle Scholar |
Roujean JL, Leroy M, Deschamps PY (1992) A bidirectional reflectance model of the Earth’s surface for the correction of remote sensing data. Journal of Geophysical Research 97, 20455–20468.
| A bidirectional reflectance model of the Earth’s surface for the correction of remote sensing data.Crossref | GoogleScholarGoogle Scholar |
Vincent L, Soille P (1991) Watersheds in digital spaces – an efficient algorithm based on immersion simulations. IEEE Transactions on Pattern Analysis and Machine Intelligence 13, 583–598.
| Watersheds in digital spaces – an efficient algorithm based on immersion simulations.Crossref | GoogleScholarGoogle Scholar |
Winterhalter L, Mistele B, Jampatong S, Schmidhalter U (2011) High throughput phenotyping of canopy water mass and canopy temperature in well-watered and drought stressed tropical maize hybrids in the vegetative stage. European Journal of Agronomy 35, 22–32.
| High throughput phenotyping of canopy water mass and canopy temperature in well-watered and drought stressed tropical maize hybrids in the vegetative stage.Crossref | GoogleScholarGoogle Scholar |
