ISRAEL JOURNAL OF PLANT SCIENCES, 64 (2017) 3-4
http://dx.doi.org/10.1080/07929978.2016.1243405
Phenotyping wheat under salt stress conditions using a 3D laser scanner
Lancelot Maphosaa*, Emily Thoday-Kennedya*, Jignesh Vakania, Andrew Phelanb, Pieter Badenhorstb,
Anthony Slaterc, German Spangenbergc and Surya Kanta
a
Agriculture Victoria, Grains Innovation Park, Horsham, Victoria, Australia; bAgriculture Victoria, Hamilton, Victoria, Australia; cAgriculture
Victoria, AgriBio, Centre for AgriBiosciences, Bundoora, Victoria, Australia
ABSTRACT
High-throughput phenotyping is a rapidly evolving field, with new technologies being developed
that need to be tested under different experimental conditions. In this study, the PlantEye, a highresolution three-dimensional (3D) laser scanner was used to phenotype wheat plants grown under
control and salt stress in controlled environment conditions. The PlantEye scans plants from
overhead, creating a data cloud from which the system computes traits such as 3D leaf area, plant
height and leaf number. Moderately high correlations were observed between automatically
calculated trait; 3D leaf area, and the manually measured traits leaf area, fresh biomass and dry
biomass, although correlations were lower than those reported in previous studies in different crop
species. As expected, salt stress caused significant reduction in plant growth, particularly leaf area
and biomass production, which resulted in significantly reduced grain number and yield. The
results here suggest that PlantEye was effective in phenotyping wheat, although improvements in
the system setup, data processing and customer support would make this phenotyping tool
suitable to be widely adopted for a range of plant species under diverse environmental conditions.
Introduction
The ability to accurately and efficiently capture data
on plant phenomes of diverse species and different
growth conditions is vital to the advancements of
crop varieties. Traditionally, selection for breeding
material has been based on laborious, manual and/or
destructive measurements under multiple environmental conditions. High-throughput phenotyping
tools and platforms are therefore needed to speed-up
the selection process. Of particular interest are systems which can hasten trait identification, by observing surrogate measurements during earlier
developmental stages, e.g. canopy cover or leaf development, which can correlate with yield and/or stress
tolerance (Furbank & Tester 2011; Araus & Cairns
2014). Equipment that can measure multiple traits at
once is also valuable, in terms of cost and time savings, especially when phenotyping large populations
across multiple sites and environmental conditions.
Utilizing non-destructive imaging and sensorbased phenotyping principles, a range of automated
ARTICLE HISTORY
Received 5 August 2016
Accepted 28 September 2016
KEYWORDS
PlantEye; laser scanning;
phenotyping; wheat; salt
stress; plant growth
high-throughput plant phenotyping systems and
image analysis software packages have been developed (Granier et al. 2006; Golzarian et al. 2011;
Berger et al. 2012; Cobb et al. 2013; Crowell et al.
2014). These systems are based on the 2D or semi3D analysis of digital color pictures taken from fixed
imaging systems. Recently developed plant imaging
systems are utilizing LIDAR (light detection and
ranging) technologies, which create 3D images of
scanned objects for trait analysis based on depth
maps and data clouds (Hosoi et al. 2011; Sirault
et al. 2013; Paulus et al. 2014). The majority of initially developed phenotyping platforms were
designed as fixed “plant-to-sensor” systems, which
limited their use to greenhouses. These fixed systems involve moving and/or rotating plants towards
imaging stations (sensors) during scanning and
imaging (Berger et al. 2012; Sirault et al. 2013; Hairmansis et al. 2014; Parent et al. 2015). While providing valuable information, high-throughput “plant-tosensor” phenotyping systems require considerable
CONTACT Surya Kant
surya.kant@ecodev.vic.gov.au
* Equal first authorship
This paper has been contributed in honor of Professor Uzi Kafkafi.
© Koninklijke Brill NV, Leiden, 2017
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L. MAPHOSA ET AL.
amounts of space and infrastructure. On the other
hand, “sensor-to-plant” phenotyping systems involve
imaging/scanning devices mounted on mobile infrastructure, which move to scan and image stationary
plants (Fanourakis et al. 2014; Kjær & Ottosen 2015;
Vadez et al. 2015; French et al. 2016).
The PlantEye is a high-resolution, 3D laser scanner
developed by Phenospex Ltd (www.phenospex.com),
with a “sensor-to-plant” concept. Previous studies
have tested the PlantEye in both controlled environment and field conditions using different crop species
(Kjær & Ottosen 2015; Vadez et al. 2015). Kjær and
Ottosen (2015) reported the PlantEye produced reliable calculated growth parameters, which correlated
well with destructive measurements of rapeseed cultivars grown in the greenhouse. The PlantEye scanner
has also been deployed in the field to phenotype potted plants, with high correlations observed between
projected growth parameters and manual measurements in peanut, cowpea and pearl millet (Vadez et al.
2015). The PlantEye was also used to phenotype pearl
millet lines bred for different rainfall environments
(Vadez et al. 2015). However, no studies have shown
the use of the PlantEye to analyze crop plants grown
under control and abiotic stress conditions.
Abiotic stresses, such as salt (NaCl) stress, can
severely affect plant development and reproduction,
ultimately reducing yield potentials in crop species
(Roy et al. 2011). Salt stress reduces plant growth and
therefore yield, through both shoot ion-independent
(osmotic) and shoot ion-dependent (ionic/NaC accumulation) pathways (Munns 2005; Munns & Tester
2008; Wang et al. 2013; Roy et al. 2014). Unlike some
abiotic stresses, phenotyping for salt stress can be relatively easy. Two-dimensional imaging systems have
been used to show differences between control and
stressed plants for a range of abiotic stresses including
salt stress (Rajendran et al. 2009; Golzarian et al. 2011;
Hairmansis et al. 2014; Campbell et al. 2015). While
one 3D imaging system has been used to differentiate
between control and water-stressed plants (Paulus
et al. 2014), the application of PlantEye to explore the
range of trait changes under stress conditions has yet
to be tested. This study aimed to establish correlations
between plant traits calculated from the PlantEye phenotyping system and manually obtained measurements in wheat, and to test whether the system can
efficiently phenotype and differentiate control and
salt-stressed plants.
Materials and methods
Experimental setup
Plant material and growth conditions
Bread wheat (Triticum aestivum L.) plants were grown
in 4.5-l pots filled with cereal potting mix (BioGro,
Mount Gambier, SA, Australia). The commercial wheat
variety Yitpi (released by Waite Institute, University of
Adelaide, SA, Australia) was used for the experiment.
The following nutrients were added to 1000 l of
potting mix: 2 kg of isobutylidenediurea (IBDU white
granules with 31% N; Richgro, Canning Vale, WA,
Australia), 2 kg of Nutricote (N : P : K at 18 : 4.8 : 9.1;
Scotts Australia, Bella Vista, NSW, Australia), 41 kg of
Macracote (N : P : K at 16 : 9 : 12; Scotts Australia), 1 kg
of trace elements (Ca : Mg : S : B : Cu : Fe : Mn : Mo : Zn
at 6 : 3 : 12 : 0.1 : 1 : 17 : 2.5 : 0.05 : 1), 225 g of iron,
5 kg of lime (CaCO3 and MgCO3 at 6 : 1) and 2 kg of
wetting agent (SaturAid; Debco, Tyabb, VIC, Australia).
A total of 80 pots were prepared, with 40 pots as
control and 40 pots subjected to salt stress. Two
plants per pot were grown in a temperature-controlled greenhouse (25 C/15 C day/night max/min)
with natural lighting, at Agriculture Victoria, Hamilton,
VIC, Australia from July to November 2015.
Salt treatment
Pots were watered with 150 ml of water every alternate day. At the emergence of the third leaf in plants
(25 days after sowing (DAS); Zadoks score (Z) 13;
(Zadoks et al. 1974), NaCl was added to each salttreated pot in 30 mM increments every alternate day,
until the final concentration of 150 mM was reached
(31 DAS), after which salt-treated plants were watered
every alternate day with 150 mM salt solution. Salt
treatment was completed 108 DAS, when plants were
senescing and grains forming a hard dough (Z85),
after having received 150 mM NaCl for 76 days. From
108 DAS until harvest at 140 DAS, plants were occasionally supplied with water to ensure grain filling for
final yield results.
Plant scanning
The PlantEye (PlantEye F300, Phenospex, Heerlen, The
Netherlands, www.phenospex.com), is a high-resolution, 3D laser scanner. The PlantEye acquires depth
maps and a 3D point cloud of scanned plants, by projecting a near-infrared (940 nm) laser line onto the
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ISRAEL JOURNAL OF PLANT SCIENCES
plant canopy, which is reflected back and captured
using the inbuilt camera (Figure 1A). Depth maps/profiles are single xz axes scan (green area in Figure 1A)
which plots the height from the PlantEye scanner
across the width of the x axis. Profiles are then converted to histograms showing the number of points
specific distances away from the scanner. Multiple
57
profiles are then merged together to form a 3D model
of the plant, with 3D images visualized via a webbased interface. An example of the 3D reconstruction
of a wheat plant is shown in Figure 1B. The PlantEye
phenotyping system then automatically computes a
diverse set of morphological plant parameters, such
as total leaf area (3D), leaf/plant count, leaf angle distribution, leaf coverage, plant height, biomass estimation, fill factor and growth rates, as well as providing
the raw information as 3D point clouds (www.pheno
spex.com). To allow for portability, the PlantEye scanner can be mounted on a mobile gantry, where the
scanner moves in the y axis at pre-set but adjustable
speed (Figure 1C).
The PlantEye scanner used in this study was
obtained from Phenospex as a pre-mounted rental
unit. Pots were placed on flat-top tables underneath
the scanner mount, then the scanner moved over the
plants in the direction of the y axis (Figure 1C). The
scanner was mounted 100 cm above the pot rim, with
the scan width set to 65 cm. Metal barcodes (25 cm
high) were used to differentiate each pot (also
referred to as experimental sector or unit). Barcodes
could either be spaced between individual plants for
single readings or placed either side of a group of
pots for average results (Figure 1C). Before the first
scan, all pots were marked to allow placement on the
tables, and therefore scanning, in the same position
and orientation at each time point. Plants were
scanned weekly for six consecutive weeks from 52
DAS to 88 DAS (Z22 to Z79), then once at physiological maturity (140 DAS; Z92).
Manual observations and destructive harvesting
Figure 1. Overview of the PlantEye scanning system. (A) Scheme
of how the PlantEye scanning unit captures the 2D laser line
reflection (red) images projected of the canopy (green) to reconstruct a 3D object as it moves in the direction of the y axis. (B)
3D image of wheat reconstituted from the data cloud and height
maps collected from scans by the PlantEye. (C) Portable PlantEye
scanning system for two potential set ups, based on the location
of the metal barcodes, which determine scanning start/stop locations, between pots. The rear set up demonstrates scanning individual plants (rye grass), with the metal barcodes spaced
between each pot. The front set up demonstrates scanning multiple pots (wheat) for averaged results, with the metal barcodes
spaced on either end of the group.
Manual observations and destructive harvesting were
conducted at three time points: peak vegetative
phase (Z22; 58 DAS) where 20 control and 20 saltstressed pots were harvested; heading (Z65; 74 DAS)
where 10 control and 10 salt-stressed pots were harvested; and at physiological maturity (Z92; 140 DAS)
where 10 control and 10 salt-stressed pots were harvested. Plant height was measured with a ruler from
the base of the plant to the top of the canopy or tallest spike tip. The number of leaves per pot were
counted and total leaf area was determined using a
leaf area meter (LI-3100C, Li-Cor Corporation, Lincoln,
NE, USA). All above-ground plant organs were harvested to measure fresh biomass. Samples were then
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L. MAPHOSA ET AL.
Prior to the salt-stress study presented here, a preliminary study was conducted to provide experience with
the PlantEye system and set up a phenotyping protocol. A total of 40 plants were grown, as described
above, all under control conditions. Plants were
scanned weekly for four consecutive weeks, 74 DAS to
88 DAS (Z65 to Z79), with 20 plants destructively harvested at Z65, then scanned once at physiological
maturity (140 DAS; Z92) when these remaining 20
plants were harvested, as described above.
Statistical analysis
Comparisons were done by correlation analysis
between automated and manually measured traits,
using GENSTAT statistical software version 17.0 (VSN
International Ltd, Hemel Hempstead, UK).
Results
Correlation between automated measurements by
PlantEye and manual observations
Projected 3D leaf area obtained from the PlantEye and
manually measured leaf area at growth stages Z22 and
Z65 correlated well (total R2 D 0.86) for both control
and salt-stressed plants (Figure 2A). Projected 3D leaf
area also correlated well with both fresh and dry biomass for all plants at heading (Figure 2B and 2C), in
which control plants (fresh biomass: R2 D 0.83; dry biomass R2 D 0.83) had higher correlations than saltstressed plants (fresh: R2 D 0.71; dry R2 D 0.65). Similar
correlations to the control plants above for all 3D leaf
area comparisons were also observed in the preliminary experiment (data not shown).
Fill factor measures the proportion of the 3D experimental scan sector that is taken up by plant material,
presented as a ratio of plant material to empty space.
Fill factor correlated well with manually measured leaf
area (total R2 D 0.86) for both control and salt-stressed
plants (Figure 3A). Control and salt-stressed plants
also showed a good correlation between fill factor
A
Projected 3D leaf area (00) (cm2)
Preliminary experiment
and fresh biomass, although the correlation for saltstressed plants was lower than for control plants
(Figure 3B).
Control
400
150 mM NaCl
350
300
250
R² = 0.80
200
R² = 0.82
150
100
50
Total R2 = 0.86
0
0
B
Projected 3D leaf area (00) (cm2)
oven-dried at 60 C for 72 h and weighed to obtain dry
biomass. At heading and maturity, the total number of
tillers were counted. After dry biomass measurements,
the total grain number was counted using a Contador2 seed counter (Hoffman Manufacturing Inc., Jefferson, OR, USA) and total grain weight measured.
200
400
600
Measured leaf area (cm2)
800
400
350
300
R² = 0.83
250
200
150
100
50
R² = 0.71
Total R2 = 0.85
0
0
C
Projected 3D leaf area (00) (cm2)
58
20
40
Measured fresh biomass (g)
60
400
350
300
R² = 0.83
250
200
150
100
R² = 0.65
50
Total R2 = 0.73
0
0
2
4
6
Measured dry biomass (g)
8
Figure 2. Correlations between manually measured traits and
projected leaf area. Correlations between projected 3D leaf area
and (A) manually measured leaf area, (B) manually measured
fresh biomass, and (C) manually measured dry biomass. Plants
were measured at peak tillering (Z22) and heading (Z65). n D 30.
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ISRAEL JOURNAL OF PLANT SCIENCES
A
B
Control
0.10
59
150 mM NaCl
0.08
Fill factor
R² = 0.83
0.06
R² = 0.80
0.04
R² = 0.82
0.02
R² = 0.71
Total R2 = 0.86
0.00
0
200
400
600
Measured leaf area (cm2)
800
0
Total R2 = 0.85
20
40
Measured fresh biomass (g)
60
Figure 3. Correlations between manually measured traits and fill factor. Correlations between fill factor and (A) manually measured leaf
area, and (B) manually measured fresh biomass. Plants were measured at peak tillering (Z22) and heading (Z65). n D 30.
Projected 3D leaf area (00) (cm2)
Effects of salt-stress treatment
Plant growth and development was observed once a
week for six weeks using the PlantEye, from tillering
to two weeks post-anthesis, then again at maturity.
Projected 3D leaf area changed over time, with distinct differences observed between control and salttreated plants (Figure 4). Both control and salt-treated
plants followed the same trend, with 3D leaf area
increasing from 52 DAS to 80 DAS, when the maximum 3D leaf area was reached (Figure 4). Projected
3D leaf area then decreased during reproductive
growth and maturity, with a sharp decrease between
80 DAS and 88 DAS. Salt-stressed plants had significantly reduced 3D leaf area compared to control
plants, producing less than half the maximum 3D leaf
area of control plants (Figure 4).
Similarly, differences between control and saltstressed plants were also observed for manually
obtained measurements at Z22, Z65, and Z92 stages.
Control plants were taller (not significantly) during
vegetative and early reproductive growth, and
became significantly taller than salt-stressed plants
when mature (Table 1). Salt-stressed plants produced
significantly less fresh and dry biomass, a reduced
number of leaves and tillers, as well as reduced leaf
area compared to control plants, during vegetative
growth (Z22), at anthesis (Z65) and at maturity (Z92).
This translated into a significant difference in yield
400
Control
350
150 mM NaCl
300
250
200
150
100
50
Salt treatment completed
0
40
60
80
100
120
140
DAS
Figure 4. Projected plant growth rate. Average projected 3D leaf area (cm2) obtained from the PlantEye. DAS D days after sowing.
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L. MAPHOSA ET AL.
Table 1. Manually measured growth characteristics, biomass and yield traits per pot at three growth stages in wheat plants grown
under control and 150 mM salt-stress conditions.
Z22 (58 DAS)
Control
Salt stress
Z65 (74 DAS)
Control
Salt stress
Z92 (140 DAS)
Control
Salt stress
Plant height (cm)
Leaf number
Leaf area (cm2)
Fresh weight (g)
Dry weight (g)
Tiller number
Grain yield (g)
Grain number
43.9 § 4.3
44.5 § 6.4
ns
25 § 4
17 § 3
306.9 § 75.7
187.5 § 50.5
17.4 § 4.2
10.2 § 2.4
1.5 § 0.5
1.0 § 0.4
–
–
–
–
–
–
58.2 § 5.6
56.2 § 7.3
ns
43 § 4
25 § 7
634.6 § 94.6
294.3 § 79.5
48.4 § 6.1
23.4 § 5.1
6.5 § 1.0
4.0 § 0.9
12 § 1
6§2
–
–
–
–
56.2 § 4.6
49.4 § 3.6
–
–
–
–
52.0 § 4.9
16.5 § 10.5
45.0 § 4.4
13.5 § 7.5
25 § 2
8§6
18.1 § 3.1
6.2 § 3.5
466 § 108
152 § 69
Data are mean § SD;
significant at 0.001; ns, not significant.
between salt-treated and control plants, with a 67%
reduction in both grain yield and grain number for
salt-stressed plants (Table 1).
Discussion
Plant phenotyping is rapidly progressing with largescale and high-throughput phenotyping platforms
incorporating technologies such as 3D scanning
(Fahlgren et al. 2015; Vazquez-Arellano et al. 2016).
The PlantEye platform employed in this study is an
example of a modern phenotyping sensor, although
its suitability for a range of crop species and experimental conditions has yet to be fully investigated.
This 3D laser system has previously been used to
image plant species including rapeseed (Kjær &
Ottosen 2015), peanut, cowpea, and pearl millet
(Vadez et al. 2015). Here, we discuss the applications
of a rental PlantEye scanner system to phenotype
wheat plants.
For all traits, calculated from the PlantEye or manually measured, clear differences between control and
salt-stressed plants were observed (Figure 4 and
Table 1). This was particularly apparent for 3D leaf
area, and manually measured leaf area, fresh biomass
and dry biomass. Salt stress is known to cause significant declines in leaf expansion rates resulting in
reductions in leaf area, leaf number and overall
biomass (Munns et al. 1995; Bernstein et al. 2009), as
seen in this study. The wheat plants in this study were
grown until maturity, which allowed for imaging and
data collection from peak vegetative growth to postanthesis and maturity. The monitoring of plant growth
dynamics such as 3D leaf area, for both control and
salt-stressed plants, showed a continued increase during early phenological stages, which peaked at
heading and decreased thereafter, which was also
reported by Fischer and Kohn (1966) for leaf area
index. The point at which leaf area starts to decrease
is thought to coincide with the onset of leaf rolling
and senescence (Austin et al. 1980); therefore, the
PlantEye may be used to indirectly measure senescence, although this would need further research.
In addition to developing a protocol for scanning
control and salt-treated wheat plants using the
PlantEye scanner, this study aimed to establish correlations between manually measured traits and those
calculated by the system. Overall, slightly lower correlations between automated and manual measurements were observed in this study than reported in
the two previous PlantEye studies (Kjær & Ottosen
2015; Vadez et al. 2015). In this study the highest correlation between an automatic and manual trait was
for leaf area, R2 D 0.86 (Figure 2A). The correlations
reported by Kjær and Ottosen (2015) were R2 D 0.97
for manual leaf area, fresh biomass and dry biomass
and by Vadez et al. (2015), R2 D 0.86–0.94 depending
on the species and growth conditions. When grown in
pots, wheat plants need support as some leaves might
drop below pot height (Figure 1B), resulting in them
being outside of the scanning range (all values below
set pot height are disregarded by the software), and
thus contributing to underestimations by the PlantEye
system. This has also been reported as an issue for
pearl millet grown in pots (Vadez et al. 2015). While a
solution to ensuring leaves do not drop below pot
height would be to provide support such as cages,
this would then provide confounding issues for the
PlantEye data computation. The system would assume
the cage is part of the plant, including it in all plant
trait calculations, unless specialized data processing
could be applied to remove the cage data points,
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ISRAEL JOURNAL OF PLANT SCIENCES
such as the processing done for image-based phenotyping platforms (Hartmann et al. 2011). Another trait
automatically calculated, fill factor, is ratio of the 3D
scan space filled with plant material compared to
empty space. Similar to 3D leaf area, fill factor was correlated with measured leaf area and fresh biomass
(Figure 3). Wheat plants were grown as a community
(two plants per pot), which may have enhanced issues
such as leaf overlap or plant organs growing out of
the scanning range. Similar lower correlations were
also observed by Vadez et al. (2015) when plants, particularly peanut and cowpea, were grown as a community compared to when scanned individually.
Abiotic stresses such as salinity and water stress
induce leaf rolling (Tatar et al. 2010; Kadioglu et al.
2012), which would reduce the visible leaf area, resulting in underestimations of leaf area and lower correlations with manual measurements. In this study, plants
subjected to salt stress had lower correlations
between automatic measurements and manual measurements than control plants, suggesting that salt
stress had an effect on the ability of the PlantEye to
effectively calculate leaf area. As the PlantEye phenotyping system uses a non-visible wavelength, no color
distinction is plausible. This means that no distinctions
can be made between healthy leaf material and areas
of necrosis, thus potentially giving an inaccurate idea
of the amount of actively photosynthetic material
present. This may be important in abiotic stress studies, such as salinity, where stress can cause leaf necrosis (Volkmar et al. 1998; Chen et al. 2002; Widodo et al.
2009) and senescence in older leaves (James et al.
2002). Salt-stressed plants in this study were noted to
have an increased rate and severity of senescence,
even while maintaining green leaf tissue. The difference between these two tissue types was therefore
indistinguishable using the PlantEye scanner.
Although clear differences between control and
salt treatments were established using the PlantEye
for 3D leaf area, the correlations with manual measurements were lower than previous reports where
younger plants of up to seven weeks of age were phenotyped (Kjær and Ottosen 2015; Vadez et al. 2015). A
single overhead PlantEye scanner, as used in this
study and the two previous published studies, may be
suitable for capturing data associated with smaller/
younger plants. For larger, more mature or architecturally complex plants, it may be worth considering
using two PlantEye scanners in “DualScan” mode,
61
mounted on angles rather than horizontal. The
mounting could be having two scanners overhead at
angles 30 and 330 . The “Dual Scan” mode would
permit the PlantEye system to combine data from two
scanners, allowing for greater scanning coverage of
plants, potentially providing a more comprehensive
and detailed 3D cloud map.
The PlantEye used in this study was a rental system,
and the outcomes of this study suggest that it was
effective in wheat phenotyping, although improvements in the system and software support are
required. This study has demonstrated some underlying issues of the PlantEye system as discussed above,
particularly plant age. This might limit the effective
application of the system in controlled environment
and field studies, where reliable and precise phenotyping data are needed on the growth of young as
well as older plants. We recommend that the “Dual
Scan” set up using two PlantEye scanners may resolve
these issues to account for a wider array of plant species and growth habits, as well as stress responses.
Overall, the PlantEye phenotyping system provides a
basis for modern, “sensor-to-plant” phenotyping.
Disclosure statement
No potential conflict of interest was reported by the authors.
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