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)

1. Affordable field high-throughput phenotyping - some tips
J.L. Araus, A. Elazab, J. Bort, M.D. Serret, J.E. Cairns

2. Outline
Why field phenotyping?
Some examples of traits and tools
Affordable high-throughput phenotyping

3. Outline
Why field phenotyping?
Some examples of traits and tools
Affordable high-throughput phenotyping

4. Crop breeding pillars
Araus and Cairns 2014

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. After Passioura (2006) Funct. Plant Biol. 33,

7. Outline
Why field phenotyping?
Some examples of traits and tools
Affordable high-throughput phenotyping

8. Different categories of traits

9. Some examples of traits and tools
Proximal sensing
Laboratory analyses
Near infrared reflectance spectroscopy

10. Some examples of traits and tools
Proximal sensing
Laboratory analyses
Near infrared reflectance spectroscopy

11. How to implement proximal sensing
in practice?

13. Phenomobiles

14. Rebetzke et al. 2013 FPB 40: 1-13

15. Aerial platforms

17. Zimbabwe February 2013

18. 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.

19. Outline
Why field phenotyping?
Some examples of traits and tools
Affordable high-throughput phenotyping

20. Proximal sensing: Low cost approaches

23. Digital photography
1m
green biomass

24. 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

25. Overview of the process

27. 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)

29. Casadesus and Villegas J. Integ. Plant Biol. 2013

30. Casadesus and Villegas 2013 J. Integ. Plant Biol.

31. Casadesus et al.
Ann. Appl. Biol. 2007

32. 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.

34. 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.

35. 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).

36. 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

37. Potential applications
MLN in hybrid maize field in Tanzania – Dr. B.M. Prasanna

38. 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)

39. 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

40. 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

41. 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

42. 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)

43. Durum wheat (Sula)
SI
RF
Anthesis
Grain filling

44. Genotype Sula
SI
RF
Anthesis
Grain filling

45. 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.

46. Aerial picture about three weeks after
anthesis of a maize trial with 6 different
N fertilization treatments
(Fontagro Project. Algerri, Lleida, Spain)
Experimental design

48. Canon Eos 5D
Tetracam mini MCA

49. 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)

50. Some examples of traits and tools
Proximal sensing
Laboratory analyses
Near infrared reflectance spectroscopy

52. 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

53. 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

54. NIRS prediction of δ13C and δ15N
Kleinebecker et al. 2009 New Phytologist 184: 732-739

55. 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.

56. Conclusions
There are different low-cost methodological
approaches that makes high-throughput field
phenotyping affordable for NARS

57. 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 .

58. http://www.optichinagriculture.com/

59. Organizers: Chinese Academy of Agricultural
Sciences and the OPTICHINA Project
http://www.optichinagriculture.com/

60. Many thanks….