OPCE-STR.vp

Acta Bot. Croat. 73 (1), 149–158, 2014 CODEN: ABCRA 25
ISSN 0365-0588

eISSN 1847-8476

Phenotyping of rice in salt stress environment using

high-throughput infrared imaging

ZAMIN S. SIDDIQUI2*, JUNG-IL CHO1, SUNG-HAN PARK1, TAEK-RYOUN KWON1,
BYUNG-OK AHN1, GANG-SEOB LEE1, MI-JEONG JEONG1, KYUNG-WHAN KIM1,
SEONG-KON LEE1, SOO-CHUL PARK1

1 National Academy of Agricultural Sciences, Rural Development Administration,
Suwon 441-707, Republic of Korea

2 Stress and Crop Physiology Laboratory, Department of Botany, University of Karachi,
Karachi 75270, Pakistan

Abstract – Phenotyping of rice (Oryza sativa L. cv. Donggin) in salt stress environment
using infrared imaging was conducted. Results were correlated with the most frequently
used physiological parameters such as stomatal conductance, relative water content and
photosynthetic parameters. It was observed that stomatal conductance (R2 = –0.618) and
relative water content (R2 = –0.852) were significantly negatively correlated with average
plant temperature (thermal images), while dark-adapted quantum yield (Fv/Fm, R

2 =
–0.325) and performance index (R2 = –0.315) were not consistent with plant temperature.
Advantages of infrared thermography and utilization of this technology for the selection of
stress tolerance physiotypes are discussed in detail.

Keywords: Infrared imaging, phenotype, rice, salt stress

Introduction

Plants of the same genotype may have different phenotypes, depending on the growing
environment. Phenotyping is a technique dealing with plant visible characteristics (pheno-
types), or trait analysis. Conventional phenotyping has been hard, time consuming and de-
structive. Recent development of high-tech imaging systems and their computation enables
modern, fast and non-destructive phenotyping research. Depending on the plant traits,
high-throughput phenotyping techniques can be useful because they can reduce phenotyp-
ing time from weeks to minutes, or even seconds. High technology in phenomics acceler-
ates the procedure for selecting plant varieties that perform better in the field when affected
by drought or salt. In the past, physiological attributes like stomatal conductance, osmotic
potential, dark-adapted quantum yield and biomass allocation were frequently used in
phenotyping techniques under salt and drought stress environments (DAVIS et al. 2005, BURKE

ACTA BOT. CROAT. 73 (1), 2014 149

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et al. 2006, SIDDIQUI et al. 2008, MUNNS et al. 2010, RICHARDS et al. 2010, SIDDIQUI 2013).
However, these physiological attributes were time consuming, laborious, and destructive.
More common physiological approaches and modern high-tech infrared phenotyping tech-
niques are being used to identify stress tolerant plants. Therefore, it is important to correlate
the two techniques and to standardize the protocol for testing favourable phenotypes under
salt stress environment (MUNNS et al. 2010). Although literature showing phenotyping using
an infrared (IR) camera is available, these techniques need to be closely monitored and
should be correlated with physiological data that might have some role in the regulation of
plant temperature in abiotic stress environment (MERLOT et al. 2002, JONES et al. 2009,
COLLINS et al. 2010). Mainly, high-tech phenotyping through an IR camera is based on the
temperature/heat produced in stress-plant (MUNNS et al. 2010). Therefore, the present study
has been designed to examine the phenotyping of rice cv. Donggin by highly sensitive IR
thermal camera and find out its correlation with some physiological parameters in salt stress
environment. Stomatal regulation and plant water status are important aspects in stress envi-
ronment and these physiological attributes stabilize the temperature inside the plant leaf.
The hypothesis that physiological parameters related to plant temperature are affected by
the salt stress was tested using infrared imaging.

Material and methods

Germination and growth

Seeds of rice (Oryza sativa L. cv. Donggin) were collected from National Center for Ge-
netically Modified Crops, National Academy of Agricultural Science, Rural Development
Administration, South Korea. Seeds were washed with distilled water several times before
sowing, then allowed to germinate in 90 mm diameter Petri dishes. Six-day old equal size
seedlings were transferred to a hydroponic system. After transplanting, the seedlings were
left for a further four days in half-strength Yoshida nutrient solution prior to the imposition
of NaCl (Tab. 1). Plants were treated with 75, 150 and 225 mM NaCl in full-strength

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SIDDIQUI Z. S., CHO J.-I., PARK S.-H., KWON T.-R., AHN B.-O., LEE K.-S. et al.

Tab. 1. Yoshida solution composition.

Chemical Amount (g/5 L)

NH4NO3 475
NaH2PO4 ´ H2O 201
K2SO4 357
CaCl2 443
MgSO4 ´ 7 H2O 1620
MnCl2 ´ 4 H2O 7.50
(NH4)6Mo7O24 ´ 4 H2O 0.37
H3BO3 4.67
ZnSO4 ´ 7 H2O 0.18
CuSO4 ´ 5 H2O 0.16
FeCl3 ´ 6 H2O 38.5
C6H8O7 ´ H2O 59.5
1 M H2SO4 250 mL

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Yoshida nutrient solution, while control plants were treated with only Yoshida solution.
Treated and control plants were grown in a growth chamber (EYELA) at a temperature of
25–28 ± 2 °C, 60–80% humidity and a photoperiod of 14/10 hours (day/night). Light inten-
sity varied from 200–350 mmol photon m–2 s–1. Experiments were replicated four times.

Relative water content

Six randomly selected leaves from each treatment and control were sampled and 4 × 2
cm2 mid-vein and the edge sections were cut with scissors. After fresh weight measurement,
each sample was placed in a 90 mm air-tight plastic Petri plate containing distilled water.
After 12-hours hydration in the dark, the leaf samples were taken out of the water and their
surfaces were well dried quickly and lightly with filter/tissue paper and immediately
weighed to obtain fully turgid weight (TW). Leaf samples were then oven dried at 80 °C for
24 h and weighed to determine dry weight (DW). Relative water content (RWC) was calcu-
lated using the following formula:

RWC (%) = [(FW – DW) / (TW – DW)] × 100

where FW is sample fresh weight, TW is sample turgid weight, and DW is sample dry
weight.

Stomatal conductance and PSII quantum yield

Stomatal conductance of twenty randomly selected leaves of each treatment and control
were examined using a leaf porometer (Model SC-1, Decagon, USA). Measurements of
chlorophyll a fluorescence emissions from the twenty randomly selected leaves were moni-
tored with a fluorescence monitoring system (Company Handy PEA) in the pulse amplitude
modulation mode. A leaf adapted to dark conditions for 30 minutes using leaf-clips was ini-
tially exposed to a modulated measuring beam of far-red light (LED source with a typical
peak at wavelength 735 nm). Original (F0) and maximum (Fm) fluorescence yields were
measured under weak modulated red light (< 0.5 mmol m–2 s–1) with 1.6 s pulses of saturat-
ing light (> 6.8 mmol m–2 s–1, photosynthetically active radiation). The variable fluorescence
yield (Fv) was calculated by the equation of Fm – F0. The ratio of variable to maximum fluo-
rescence (Fv/Fm), calculated as maximum quantum yield of PSII photochemistry as well as
photosynthesis performance index were determined as described by MAXWELL and JOHNSON
(2000).

IR thermal images

We used FLIR-SC-620 (FLIR Systems, USA) for thermal imaging experiments. The
system was optimized 30 minutes before measurements. To test the temperature difference
between treated and untreated plants in salt stress environment, plants of each treatment and
control were examined. Plant images were taken using a rectangular box of an area about 46
× 30 cm2. Temperature of 24 ± 2 °C inside the box and relative humidity of 60–70% were re-
corded. The images were taken at 10 a.m. using a FLIR SC-620 series camera with 640 ×
480 pixel IR resolution. Images from each treatment and control were directly extracted
from the camera into computer and a report was generated using ThermaCAM Researcher
Pro 2.10 software.

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PHENOTYPING OF RICE IN SALT STRESS ENVIRONMENT

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Statistical analysis

All data from treated and control plants were subjected to analysis of variance using
SPSS 17.0 software. The values were expressed as the mean of four replicates ± standard er-
ror (SE). Student t-test (p < 0.05) was used to check statistical significance. Correlation
analysis was computed between average plant temperatures (IR image) and physiological
attributes.

Results

Infrared thermography phenotyping technique was used to identify plant response in salt
stress environment showing significant difference between salt stress and unstressed plants
(Fig. 1). For this study, plants were subjected to various salt concentrations and therefore
plant temperatures and color patterns were recorded. Image colors represent the temperature
pattern and were in the following order: blue (less temperature) < green < yellow < red (high
temperature). Plants in a saline environment showed substantially less blue color expression
than those in a non-saline environment. It was observed that blue color intensity changed
from blue to green, then yellow and red color as salinity increased as compared to the con-
trol. Leaves of each treated and control plant showed substantial variations in color and tem-
perature. However, maximum leaf temperature was recorded in a 225 mM NaCl treated

152 ACTA BOT. CROAT. 73 (1), 2014

SIDDIQUI Z. S., CHO J.-I., PARK S.-H., KWON T.-R., AHN B.-O., LEE K.-S. et al.

Fig. 1. Infrared images of plants treated with NaCl in the concentration range 0–225 mM, observed
by a FLIR-SC-620 camera. Images were analyzed by ThermaCAM Researcher Pro 2.10 soft-
ware. First row shows the whole plants set treated with salt in comparison to the control. Sec-
ond row represents a single plant treated with same salt and control solutions. Third row
shows the roots of treated and control plants.

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plant compared to control (Tab. 2). Likewise, the roots of salt stress plants also showed sig-
nificant variations in color. Roots of highly salt stressed plants (225 mM NaCl) showed
higher temperatures than the control plants. Root size was also greatly reduced in salt treated
plants.

Performance index of salt treated and untreated plants were examined and were ex-
pressed on a graph (Fig. 2). Performance indices gradually declined due to salt stress, as
compared to the control. The lowest performance index values as compared to the control

ACTA BOT. CROAT. 73 (1), 2014 153

PHENOTYPING OF RICE IN SALT STRESS ENVIRONMENT

Tab. 2. Temperatures of salt-treated and control plants and leaves calculated based on the IR thermal
images. Student t-test was done to compare control and salt treated samples. Different letters
present significantly different values at p < 0.05.

NaCl
Plants temperature (°C) Leaf temperature (°C)

Min Max Avg Min Max Avg
Control 23.8a 32.9a 28.4a 27.6a 28.2a 27.9a

75 mM 22.9b 32.8a 27.9b 27.7a 29.3b 28.5b

150 mM 25.1c 33.7b 29.4c 29.4b 30.4c 29.9c

225 mM 26.1d 32.8a 29.5c 29.4b 31.4d 30.4d

Student t-test was done to compare control and salt treated samples. Similar alphabets are non-signifi-
cantly differed at p < 0.05. Avg – average.

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Fig. 2. Relative water content, stomatal conductance, performance index and dark-adapted quantum
yield (Fv/Fm) in saline-treated plants in comparison to the control. Values are mean ± SE, n =
4. Different letters represent statistically significant values (p < 0.05).

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were found in a 225 mM NaCl treated plant. The relative water content as compared to the
control plant decreased upon salt stress in a dose-dependent manner (Fig. 2). Maximum de-
crease in relative water content as compared to the control was found in 225 mM NaCl
treated sample. Likewise, stomatal conductances in plants as compared to the control were
significantly decreased in a salt-treated sample (Fig. 2). Moreover, the study showed that the
decrease in stomatal conductance was related to salt concentrations resulting in a maximum
decrease at 225 mM NaCl treatment. Similarly, dark-adapted quantum yield (Fv/Fm) was re-
duced in a salt-treated plant. Maximum decrease as compared to the control was observed in
a 225 mM-treated plant while decrease in quantum yield was somewhat similar and was
non-significant in 75 and 150 mM treated plants.

Correlations between the average IR image temperature pattern and physiological attrib-
utes like relative water content, stomatal conductance, performance index and dark-adapted
quantum yield were significant (Fig. 3). Significant negative correlation was observed be-
tween average image temperature and relative water content (R2 = –0.852) as well as
stomatal conductance (R2 = –0.612) while correlation between plant temperature and per-
formance index (R2 = –0.315) as well as dark-adapted quantum yield (R2 = –0.325) was not
significant.

Discussion

Comparison and correlation between conventional and modern phenotyping of rice
plants in salt stress environment were conducted. Conventional phenotyping provided phy-
siological attributes like relative water content, stomatal conductance, dark-adapted quan-

154 ACTA BOT. CROAT. 73 (1), 2014

SIDDIQUI Z. S., CHO J.-I., PARK S.-H., KWON T.-R., AHN B.-O., LEE K.-S. et al.

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Fig. 3. Correlations between average plant temperature and relative water contents, stomatal con-
ductance, performance index and dark-adapted quantum yield (Fv/Fm) tested in saline and
non-saline environment. R2 stands for linear regression values.

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tum yield and performance index. A modern approach using an IR thermal camera provided
thermal variations in plants upon salt stress. It was found that physiological attributes like
relative water content and stomatal conductance were significantly correlated with IR ther-
mal-image temperature. On the other hand, performance index and quantum yield were not
significantly correlated with the result obtained by IR thermography. Among several pa-
rameters obtained from the chlorophyll fluorescence measurements, the dark-adapted quan-
tum yield (Fv/Fm) and the performance index were selected for the comparison between salt
stress and unstressed plant. The reason for this choice was that the Fv/Fm ratio is the most ex-
tensively used photosystem II (PSII) efficiency indicator. This parameter has been shown to
correlate with a number of functional PSII complexes. Many studies have used this ratio as
an indicator for stress tolerance or sensitivity (PENUELAS and BOADA 2003). Performance in-
dex was introduced to quantify the effects of environmental factors like chilling, heat,
drought, chromate, ozone, or urban injuries to photosynthesis (HERMANS et al. 2003, DE
RONDE et al. 2004, STRAUSS et al. 2006). In this study, neither parameter was significantly
correlated with the result obtained by IR thermography. The reason behind this may be that
IR sensing is based on heat generation of plant which is linked with water status rather than
photosynthetic performance.

Stomatal conductance, relative water content and dark-adapted quantum yield, perfor-
mance index are affected by salt stress and these physiological attributes are linked with the
leaf temperature (MUNNS et al. 2010). Generally, water loss from the leaf needs a substantial
amount of energy to convert each molecule of water from liquid to vapor. This energy is
then taken away from the leaf in the evaporating water for cooling purpose (JONES et al.
2009). Thus, for a given environmental or stress condition, leaf transpiration is an important
determinant of leaf temperature. In the case of stress caused by either salt or drought, an im-
mediate plant response is a reduction in transpiration to reduce water loss, and an increase in
the leaf temperature (WOO et al. 2008, MUNNS et al. 2010). Moreover, GARRITY and
O’TOOLE (1994) have shown that IR thermography could be used to determine leaf and can-
opy temperature, although as an indirect estimation of plant water status. It is presumed that
a direct and an indirect relationship between physiological parameters and IR images are
based on the type and nature of stress, plants and research area. For instance, genotypes with
a higher tolerance to stress uptake soil water efficiently by maintaining higher stomatal con-
ductance and therefore can be identified as plants with cooler leaves (JONES et al. 2009,
BERGER et al. 2010, LU et al. 2011). Further, SIRAULT et al. (2009) has suggested that leaf
temperature is an indicator of stomatal conductance and it was increased with high salt con-
centration (MUNNS et al. 2010). The ranking of the genotypes based on the growth study and
thermal IR measurements was consistent (JAMES et al. 2008) and both have been success-
fully deployed in wheat breeding for both drought and heat screening (FISCHER et al. 1998,
REYNOLDS et al. 1998, BRENNAN et al. 2007).

In this study, high salt concentration (225 mM NaCl) caused a substantial reduction in
stomatal conduction and relative leaf water content and subsequently increased average
plant leaf temperature. It was shown that dark-adapted quantum yield and IR images were
non-significantly correlated. Hence, it could be stated that decrease in stomatal conductance
and relative water content in leaf could generate more heat, causing leaf temperature to in-
crease in a given leaf area. Leaf temperature is a proximate indicator of stomatal conduc-
tance and water status which are often analyzed with an IR sensor (SIRAULT et al. 2009,
MUNNS et al. 2010). Physiological attributes like stomatal conductance and IR images were
found consistent in previous studies (JONES et al. 2009, MUNNS et al. 2010).

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PHENOTYPING OF RICE IN SALT STRESS ENVIRONMENT

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Dark-adapted quantum yield, Fv/Fm (a measure of the intrinsic photochemical efficiency
of light harvesting in photosystem II) is the most easily measured and commonly used fluo-
rescence parameter in the stress studies (BAKER et al. 2008). Photosynthesis ability of a plant
under stress condition is attributed to stomata factors, which not only regulate leaf water
content but also maintain carbon dioxide concentration inside the leaf (BROUGNOLY and
LAUTERI 1991, TOURNEUX and PELTIER 1995, KHAN and PANDA 2008, SIDDIQUI et al. 2008).
Water status in a plant is highly sensitive to salinity and therefore it is dominant in determin-
ing plant responses to stress (STEPIEN and KLOBUS 2006). Dark-adapted quantum yield re-
sponses to salt or drought stress environment are rather slow and can be detectable in a large
tray experiment using very small seedlings (WOO et al. 2008, JANSEN et al. 2009). Since this
experiment was carried out using small trays, it was presumed that dark-adapted quantum
yield may not produce significant change in leaf temperature and thus it could not be de-
tected by IR thermal images sensing. Therefore, based on the working principal of the IR
camera, it could be suggested that IR may not be related to the photosynthesis performance
of a plant under saline environment. Meanwhile, plant temperature allows the indication of
the degree of stress in a crop on the basis of relative water content and stomatal conductance.
In stress, plant temperature and water stress are perhaps linked to soil water availability, leaf
water potential, and stomatal conductance. IR thermography was proved to be related to
soil- and plant-based measures of water stress. It was also observed that IR thermography
can be potentially used for identifying the differences between genotypes and single crop in
variable plant irrigation and stress environment (ROMANO et al. 2011, ZIA et al. 2011). How-
ever, high technology utilization in field experiment needs to be developed in order to iden-
tify the best protocol to optimize the data accuracy.

Conclusion

Correlation analysis between conventional and modern phenotyping showed that plant or
leaf temperature variation could be a useful tool to identify stress tolerant physiotype/geno-
type in the stress environment. A modern analysis performed by highly sensitive IR camera (IR
thermography techniques) may be less time consuming, non-destructive and cover a larger scale.

Acknowledgments

This work was supported by a grant from the Next-Generation Bio-Green 21 Program
(No. PJ009008), Rural Development Administration, Suwon, Republic of Korea. F. Z. S. is
thankful to Department of Botany, University of Karachi, Pakistan for enabling a post-doc-
toral study period abroad.

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