Affordable field high-throughput phenotyping - some tips

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Remote sensing –Beyond images
Mexico 14-15 December 2013

The workshop was organized by CIMMYT Global Conservation Agriculture Program (GCAP) and funded by the Bill & Melinda Gates Foundation (BMGF), the Mexican Secretariat of Agriculture, Livestock, Rural Development, Fisheries and Food (SAGARPA), the International Maize and Wheat Improvement Center (CIMMYT), CGIAR Research Program on Maize, the Cereal System Initiative for South Asia (CSISA) and the Sustainable Modernization of the Traditional Agriculture (MasAgro)

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Affordable field high-throughput phenotyping - some tips

  1. 1. Affordable field high-throughput phenotyping - some tips J.L. Araus, A. Elazab, J. Bort, M.D. Serret, J.E. Cairns
  2. 2. Outline Why field phenotyping? Some examples of traits and tools Affordable high-throughput phenotyping
  3. 3. Outline Why field phenotyping? Some examples of traits and tools Affordable high-throughput phenotyping
  4. 4. Crop breeding pillars Araus and Cairns 2014
  5. 5. Molecular Breeding Environment Data Sequence Server Pedigree Climatic Phenotypic Data Data Molecular Markers Crop Database Model Estimation of highest value crosses Genotyping in next generations INIA Marker assisted selection Estimation of Genomic Breeding Values Genomic Selection 5
  6. 6. After Passioura (2006) Funct. Plant Biol. 33,
  7. 7. Outline Why field phenotyping? Some examples of traits and tools Affordable high-throughput phenotyping
  8. 8. Different categories of traits
  9. 9. Some examples of traits and tools Proximal sensing Laboratory analyses Near infrared reflectance spectroscopy
  10. 10. Some examples of traits and tools Proximal sensing Laboratory analyses Near infrared reflectance spectroscopy
  11. 11. How to implement proximal sensing in practice?
  12. 12. Phenomobiles
  13. 13. Rebetzke et al. 2013 FPB 40: 1-13
  14. 14. Aerial platforms
  15. 15. Zimbabwe February 2013
  16. 16. Different categories of imaging systems for remote-sensing evaluation of vegetation and examples of prototypes capable of being carried by UAPs of limited payload are shown: A) RGB/CIR cameras; B) Multispectral cameras; C) Hyperspectral VIS-VNIR imager; D) Longwave infrared cameras or thermal imaging cameras; E) Conventional digital (RGB) cameras.
  17. 17. Outline Why field phenotyping? Some examples of traits and tools Affordable high-throughput phenotyping
  18. 18. Proximal sensing: Low cost approaches
  19. 19. Digital photography 1m green biomass
  20. 20. Numerical representation of color There are a number of different systems for representing a given color. •RGB: Red, Green and Blue related with color reproduction by computer screens, etc. •IHS Intensity, Hue, Saturation Hue wheel: 0º Practical for image analysis 120º 240º •CIE-lab ~ sensitivity of human visual system Consistent distance  practical for arithmetics CIE-Lab
  21. 21. Overview of the process
  22. 22. Picture-derived Vegetation Indices calculated by BreedPix •Components of the average color of the image •H (from HIS color-space) •a* (from CIE-Lab color-space) •Counting green pixels •Green Area (% pixels with 60<Hue<120) •Greener Area (% pixels with 80<Hue<120)
  23. 23. Casadesus and Villegas J. Integ. Plant Biol. 2013
  24. 24. Casadesus and Villegas 2013 J. Integ. Plant Biol.
  25. 25. Casadesus et al. Ann. Appl. Biol. 2007
  26. 26. Validation: Pic-VIs correlate with leaf area (however, the relationship may change with phenology) The relationships between LAI and Hue, a* and u* were similar to these Casadesus and Villegas 2013 J. Integ. Plant Biol.
  27. 27. Pests and diseases monitoring Cereal leaf beetle Oulema melanopus L. (Coleoptera, Chysomelidae). Started in May. Yellow rust Puccinia striformis f. sp. tritici. A very virulent new strain in Europe named Warrior/Ambition, first cited in England in 2011. Started mid-April.
  28. 28. Correlation coefficients of Grain Yield (GY) with leaf chlorophyll content and color parameters calculated from the digital images at jointing (no infested), heading (mildly infested) and two weeks post-anthesis (severely infested) across 12 wheat genotypes. Jointing Heading Post-anthesis GY Chl GY Chl GY Chl Chl Intensity Hue -0.39* 0.1 -0.17 ― 0.27 0.2 -0.29 0.23 -0.04 ― -0.15 0.37* 0.54*** -0.04 0.87*** ― 0.01 0.66*** Saturation Lightness a* 0.13 0.19 -0.08 -0.22 0.11 0.09 -0.09 0.23 -0.12 -0.42* -0.25 0.52** -0.68*** 0.14 -0.88*** -0.50** 0.16 -0.72*** b* 0.14 -0.18 0.09 -0.55*** -0.45** -0.30 u* 0.01 -0.03 -0.14 0.38* -0.87*** -0.72*** v* 0.15 -0.15 0.15 -0.54*** -0.08 0.01 GA -0.2 0.13 0.33 -0.32 0.87*** 0.72*** GGA -0.22 0.21 0.36* -0.14 0.89*** 0.57*** Chl, flag leaf chlorophyll content (SPAD value); Intensity hue saturation (IHS) color space and each of its components; lightness, a* and b*, color component from Lab; u* and v*, color component from Luv; GA, green area; GGA, greener area. (*, P< 0.05; **, P < 0.01 and ***, P < 0.001, n = 36).
  29. 29. 10 GA GGA r2 = 0.79*** 8 -1 Grain yield (t ha ) r2 = 0.74*** 6 4 2 0 0.0 .2 .4 .6 .8 1.0 GA and GGA Relationships between G and GAA against grain yield across a set bread wheats
  30. 30. Potential applications MLN in hybrid maize field in Tanzania – Dr. B.M. Prasanna
  31. 31. Conclusions: Advantages of Pic-VIs •Very low sampling cost and high resolution •Sampling *almost+ not conditioned by weather •Calculation of Pic-VIs can be automated (a trial with hundreds of plots can be sampled and processed in the same day) •Good repeatability and representativity (taking several pictures per plot allows accounting for its spatial variability) •Validated as Vegetation Indices (before anthesis, GA, a* and u* show R2>0.8 with LAI, GAI and CDW)
  32. 32. Conclusions: Comparison between Pic-VIs •GA, GGA, a* and u* are more robust than Hue to environmental conditions •GA and GGA are almost unaffected by soil color •GA is the easiest to interpret (% soil covered by green canopy) •GGA may be useful at late grain-filling stages to exclude pixels representing senescent leaves
  33. 33. Conclusions: Limitations of Pic-VIs •As other VI, they get saturated at high LAI (e.g. at stages with much green biomass, under irrigated conditions) •As other VI, they get disturbed after anthesis by the structure of the canopy •Effect of spikes •Vertical distribution of green biomass
  34. 34. Normalized Green Red Difference Index (NGRDI) NGRDI = [(Green – Red)] / (Green + Red)] Tucker, C.J., 1979. Remote Sensing of Environment 8 Gitelson et al. 2002 Remote Sensing of Environment 80
  35. 35. NGRDI = [(Green – Red)] / (Green + Red)] • Image analysis was performed with ImageJ 1.46r (http://imagej.nih.gov/ij/). • ImageJ is a public domain Java image processing and analysis program created by NIH Image. • The original images stored by the camera were converted to its main 3 channels (red-green-blue)
  36. 36. Durum wheat (Sula) SI RF Anthesis Grain filling
  37. 37. Genotype Sula SI RF Anthesis Grain filling
  38. 38. Relationship between Normalized Difference Vegetation Index (NDVI.2, left A, B) and the Normalized Green Red Difference Index (NGRDI.2, right C, D) at anthesis versus grain yield (GY) and aerial biomass (AB) at maturity.
  39. 39. Aerial picture about three weeks after anthesis of a maize trial with 6 different N fertilization treatments (Fontagro Project. Algerri, Lleida, Spain) Experimental design
  40. 40. Canon Eos 5D Tetracam mini MCA
  41. 41. Beyond vegetation indices Other parameters could be estimated from digital images. •Total soil cover (green+dry vegetation) •Physiological status (N-content, Chl,...) from the color of the green area only. •Agronomical yield components (e.g. spikes m-2)
  42. 42. Some examples of traits and tools Proximal sensing Laboratory analyses Near infrared reflectance spectroscopy
  43. 43. Technique Parameter Cost per sample Time Equipment IRMS EA AACC Method N content Ash content NIRS-prediction 13C 18O 10€ 20€ 3€ 1.5€ 0.5€ <10 min <10 min <10 min ≈24 h ≈3 min EA-IRMS EA Muffle furnace *previously reported by Clark et al. 1995; Ferrio et al. 2001; Kleinebecker et al. 2009 13C* 18O Ash N NIR spectrometer
  44. 44. NIRS a surrogate analysis of Ci Se at as la m i o p b r nl 13C Vn l ai Se l t as i o p d m a N = 1 7 9 Y + = 0 1 .x . 49 80 2 r 0* =* .* 8 2 R= M. S 5 E 5 P 0 o /o ) 1 3 5 1N 8= Y + = 0 2 .x . 18 06 2 1r 0* 7 =* .* 8 6 R= M. S 4 E 6 P 0 1 6 1 5 NniCDscrmatioIR(SPredict 13 1 4 1 3 Ba r R e ie dn af d T yie e af la a H n d R d T yg e at la r e Hi d d a I r 1 2 Ba r R e ie dn af d T yie e af la a H n d R d T yg e at la r e Hi d d a I r 11111111111111 23456782345678 1 3 o M C i t () e d i rn / a s u D a o r e s c i i m o o n 1 3 o M C i t () e d i rn / a s u D a o r e s c i i m o o n
  45. 45. NIRS prediction of δ13C and δ15N Kleinebecker et al. 2009 New Phytologist 184: 732-739
  46. 46. NIRS prediction of ash content and δ18O Calibration statistics for global sample sets (including inbred lines and hybrids) for N, ash content and kernels and leaves Trait Nkernels Nleaves ASHkernels ASHleaves 18O kernels 18O leaves N 126 152 129 150 128 151 Mean 1.81 1.57 1.47 14.31 31.69 32.97 SD 0.24 0.22 0.24 2.89 1.43 1.25 Range 1.15-2.38 1.04-2.05 0.91-1.90 8.78-21.46 28.05-34.99 29.37-36.46 CV 13.4 14.1 16.2 20.2 4.5 3.8 SEC 0.09 0.10 0.11 0.54 0.82 0.79 R2c 0.87 0.80 0.79 0.97 0.66 0.54 SECV 0.09 0.12 0.13 0.65 1.04 1.00 R2cv 0.87 0.72 0.72 0.95 0.49 0.38 Calibration statistics for hybrid sample set for leaf and kernel N and ash content and kernel Trait Nkernels Nleaves ASHkernels ASHleaves 18O kernels N 73 86 75 84 70 Mean 1.73 1.49 1.37 14.89 31.03 SD 0.24 0.22 0.27 2.92 1.05 Range 1.15-2.24 0.92-1.95 0.91-1.80 10.02-20.82 29.06-33.53 CV 13.71 14.71 19.71 19.64 3.37 SEC 0.07 0.08 0.10 0.49 0.50 R2c 0.87 0.86 0.82 0.97 0.77 SECV 0.08 0.09 0.14 0.78 0.76 RPD 2.76 1.86 1.89 4.42 1.38 1.26 18O in Slope 0.90 0.80 0.79 0.98 0.66 0.57 18O R2cv 0.87 0.83 0.70 0.93 0.51 RPD 2.79 2.46 1.92 3.76 1.38 Slope 0.87 0.86 0.82 0.98 0.77 N, number of samples; SD, standard deviation; CV, coefficient of variation; R2c, determination coefficient of calibration; R2cv, determination coefficient of cross-validation; RPD, ratio of performance deviation; SEC, standard error of calibration; SECV, standard error of cross calibration. All correlations were significant at P<0.001 level.
  47. 47. Conclusions There are different low-cost methodological approaches that makes high-throughput field phenotyping affordable for NARS
  48. 48. Ackowledgements • • • • Affordable field-based high Throughput Phenotyping Platforms (HTPPs). Maize Competitive Grants Initiative. CIMMYT Adaptation to Climate Change of the Mediterranean Agricultural Systems – ACLIMAS.. EuropeAid/131046/C/ACT/Multi. European Commission Durum wheat improvement for the current and future Mediterranean conditionsMejora del trigo duro para las condiciones mediterráneas presentes y futuras. AGL2010-20180 Spain. Breeding to Optimise Chinese Agriculture (OPTICHINA). FP7 Cooperation, European Commission - DG Research. Grant Agreement 26604 .
  49. 49. http://www.optichinagriculture.com/
  50. 50. Organizers: Chinese Academy of Agricultural Sciences and the OPTICHINA Project http://www.optichinagriculture.com/
  51. 51. Many thanks….

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