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Plant temperature-based indices using infrared thermography for detecting water status in sesame under greenhouse conditions
Agricultural Water Management 204 (2018) 222–233
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Plant temperature-based indices using infrared thermography for detecting water status in sesame under greenhouse conditions
T
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Azar Khorsandia, Abbas Hemmata, , Seyed Ahmad Mireeia, Rasoul Amirfattahib, Parviz Ehsanzadehc a
Biosystems Engineering Department, College of Agriculture, Isfahan University of Technology, Isfahan, 84156-83111, Iran Department of Electrical and Computer Engineering, Isfahan University of Technology, Isfahan, 84156-83111, Iran c Department of Agronomy and Plant Breeding, College of Agriculture, Isfahan University of Technology, Isfahan, PO Box 84156-83111, Iran b
A R T I C LE I N FO
A B S T R A C T
Keywords: Stomatal conductance (gs) Crop water stress index (CWSI) Stomatal conductance index (Ig) Relative water content (RWC) Water stress Drought
There have been studies on the effect of water stresses on leaf stomatal conductance (gs); however, the scientific reports on using non-contact techniques such as thermography for sesame (Sesamum indicum L.) are rare. The objectives of this study were hence to detect water status in sesame (genotype, “Naz-Takshakhe”) under greenhouse conditions using Crop Water Stress (CWSI) and stomatal conductance (Ig) Indices. One hundred and fifty pots were randomly assigned to three equal groups which were irrigated at soil water potential of −0.1 MPa (well-watered, WW), −1.0 MPa (moderate-water stressed, MWS), and −1.5 MPa (severe-water stressed, SWS). Four formulations of CWSI and two of Ig using canopy temperature (TC) from the WW treatment or temperature from a wet reference for the upper threshold and TC from the SWS treatment, temperature from a dry reference or air temperature plus 3° as the lower threshold were compared. Moreover, an additional CWSI and Ig formulations were also obtained by non-water stress baseline (NWSB) information using meteorological data. Furthermore, the relative water content (RWC) and gs were measured on the youngest and uppermost fully developed leaves of each pot. TC of MWS and SWS plants was higher than WW plants by 1.9 and 2.6 °C, respectively. A significant and linear relationship (P < 0.001) between CWSI/Ig and gs/RWC was found. Therefore, both physiological traits of gs and RWC can be estimated by temperature-based indices of CWSI/Ig. The results also showed the developed system enables us to estimate actual time variations in canopy temperatures. This study validates the effectiveness of using CWSI/Ig for non-destructive detection of water stress and estimation of relative water content in sesame.
1. Introduction Agricultural productivity is limited worldwide by various biotic and abiotic stresses (Kumar, 2013). Drought is of particular importance since it is the main abiotic stress factor which causes the highest yield losses (Manavalan and Nguyen, 2012). From the agricultural point of view, crop water stress occurs when the amount of the water provided through rainfall and irrigation is not sufficient to meet the needs of plant evapotranspiration. Like other crop stresses, water stress influences on a large number of physiological, biochemical, and molecular reactions of plants (Lisar et al., 2012; Manavalan and Nguyen, 2012). Precision irrigation can help to improve water use efficiency and to increase the crop productivity. Several methods for monitoring crop water stress have been introduced which can be classified as: soil-based and plant-based
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measurements (Alves and Pereira, 2000; Cohen et al., 2005; Cohen et al., 2012). Among these methods, soil moisture sensors, pressure chambers and leaf diffusion porometers have been widely used for measuring soil moisture, individual leaf/stem water potential and leaf stomatal conductance, respectively (Ballester et al., 2013; Idso et al., 1977; Moller et al., 2006). However, these techniques are unsuitable for automatic monitoring of crop water stress since they are destructive, labor-intensive, and time-consuming. Therefore, the applications of these methods for spatial and temporal monitoring of crop water stress in large acreage production systems are not feasible (Ballester et al., 2013). From plant physiology, if a plant is experiencing water stress, the stomata tends to close, leads to a reduction in transpiration and rising the leaf temperature (Ballester et al., 2013; Jones, 1999, 2004; Leinonen and Jones, 2004). Therefore, the increase in canopy
Corresponding author. E-mail addresses: [email protected] (A. Khorsandi), [email protected] (A. Hemmat), [email protected] (S.A. Mireei), [email protected] (R. Amirfattahi), [email protected] (P. Ehsanzadeh). https://doi.org/10.1016/j.agwat.2018.04.012 Received 9 July 2017; Received in revised form 31 March 2018; Accepted 8 April 2018 0378-3774/ © 2018 Elsevier B.V. All rights reserved.
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Nomenclature ASW a b CWSI DV DW ΔT1 ΔT2 ΔT3 EC FOV FW SWS gs MWS Ig LWIR MAD NWSB P pH
RDI RH RWC ρb TC Tdry Twet Tair TWWC TW θFC θPWP
Available soil water [cm3] Crop specific intercept for NWSB [°C] Crop specific slope for NWSB [°C kPa−1] Crop water stress index [−] Digital value [−] Dry weight [g] Temperature difference of WW canopy and air [°C] Temperature difference of plant canopy and air [°C]Temperature difference of plant canopy and air [°C] Temperature difference of assumed upper limit canopy (air temperature plus 3 °C) and air [°C] Electrical conductivity [dS m−1] Field of view [Degree] Fresh weight [g] Severe-water stressed [−] Stomatal conductance [mmol m−2 s−1] Moderate-water stressed [−] Stomatal conductance index [−] Long wave infrared [μm] Maximum allowable depletion [cm3] Non-water stressed baseline [−] Fraction of ASW that can be depleted from the root zone [%] Potential of hydrogen [−]
UAV Virrig VPD Vpot WSB WW X X1 X2 Y Y1 Y2
Regulated deficit irrigation [−] Relative humidity [%] Relative water content [%] Bulk density the of the soil [g cm−3] Plant canopy temperature [°C] Upper bound for canopy temperature [°C] Lower bound for canopy temperature [°C] Air temperature [°C] Well-watered canopy temperature [°C] Turgid weight [g] Gravimetric soil–water content at field capacity [%] Gravimetric soil–water content at permanent wilting point [%] Unmanned aerial vehicles [−] Volume of irrigation [cm3] Vapor pressure deficit [kPa] Volume of the pot [cm3] Water stress baseline [−] Well-watered [−] Canopy temperature measured by thermal camera [°C] Temperatures of wet reference in thermal images [°C] Temperatures of dry reference in thermal images [°C] Predicted canopy temperature [°C] Measured surface temperatures of wet reference [°C] Measured surface temperatures of dry reference [°C]
aloft is a satellite, which is space borne. Nevertheless, for satellite data, atmospheric correction may be needed to obtain accurate surface temperature estimates (Ramírez-Cuesta et al., 2017). Several indices have been presented for quantifying and monitoring the crop water stress in which TC (crop canopy temperature) is the main factor for evaluating the crop water status. The first indicator, which was developed for the arid climate of Arizona (where has a similar climate to the arid regions of central Iran) was known as Crop Water Stress Index (CWSI) (Jones, 1999). For calculating CWSI, TC must be normalized with well-watered and non-transpiring crop canopy temperatures as lower and upper leaf temperature bounds, respectively (DeJonge et al., 2015). S. indicum is an ancient warm season oilseed crop which is said to be partially resistant to some environmental constraints (Bedigian, 2010; Mortazavian and Kohpayegani, 2010). Sesame oil contains an unique antioxidants that cannot be found in other edible oils and make the sesame oil the high quality one. In addition to the oil, crop seed is used as a source of proteins, vitamins and minerals for humans as well as in animal feed (Boureima et al., 2012). Thus, the seed owes its great economic potential to the pharmaceutical and cosmetic industries, yet greater economic interest lies in its oil content, which is used in the production of high-quality edible oil. Sesame is usually cultivated in semi-arid region and like many other crops, it is sensitive to drought during its vegetation stage, therefore its production potential can be affected widely by water stress (Boureima et al., 2012). Although there have been studies on the effects of water stresses on leaf stomatal conductance (Yousefzadeh Najafabadi and Ehsanzadeh, 2017), there are few scientific reports on using non-contact instruments such as thermometers for sesame (Hall et al., 1979). To the best of our
temperature can be a good water stress indicator that can be measured by means of infrared thermometers or thermal cameras (Moller et al., 2006). These methods offer non-contact and non-destructive monitoring of crop water stress (Jones, 2004; Leinonen and Jones, 2004). Infrared thermometers are more limited in use since they provide a single point average temperature value of all objects within the sensor's field of view such as shaded and unshaded parts of plant canopy and/or soil surface. The accuracy of this sensor is even worse when the plant is immature because soil covers a majority of the surface (Maes and Steppe, 2012). However, thermal imaging is a potential tool for estimating plant temperature, which can be used as an indicator of stomatal closure and water deficit stress. Recently, the emergence of thermal cameras, particularly, when combined with the automated analysis of images, makes the use of thermal images much easier. The accuracy of this method is higher than that obtained using infrared thermometer because in this imaging method, by segmentation of canopy thermal images, the influence of soil background can be minimized (Maes and Steppe, 2012). However, thermal cameras are capable of measuring relative temperature rather than actual temperature. To quantify actual surface temperature, the thermal camera has to be calibrated at environmental conditions (Mangus et al., 2016). There are three broad categories of remote sensing platforms: ground based, airborne, and satellite. The platform used in this research is laboratory-instruments ground-based which is used almost exclusively for research. However, for monitoring water status in field crops, other remote sensing platforms should be used to cover large surfaces of crops in very short times by mounting thermal cameras on board drones, aircrafts or satellites. Low altitude aircraft/drone is good for acquiring high spatial resolution data. The most stable platform Table 1 Some physical and chemical properties of the experimental soil. Soil texture
Sand (%)
Clay (%)
Silt (%)
θFC (%)
θPWP (%)
ρb (g cm−3)
pH
EC (dS m−1)
Sandy clay loam
55.3
26.4
18.3
18
9
1.3
7.7
2.45
θFC is the percentage of the gravimetric soil–water content at field capacity, θPWP is the percentage of the gravimetric soil–water content at permanent wilting point, and ρb is the bulk density. 223
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well watered (WW), (b) −1.0 MPa as moderate-water stressed (MWS), and (c) −1.5 MPa as severe-water stressed (SWS) (Yousefzadeh Najafabadi and Ehsanzadeh, 2017). Each treatment had 50 replications (pots). Sesame (Sesamum indicum L.) seeds ('Naz-Takshakhe' genotype) were sown on the 13th of July 2016 into 17-cm diameter tapered conical pots. The pots with a volume (Vpot) of 1940 cm3 were filled with equal amounts of gravel (for drainage) and soil (2.5 kg of air-dried soil) with a sandy clay loam texture as potting material. Physico-chemical properties of the soil are given in Table 1. The average air temperature (Tair) and relative humidity (RH) in the greenhouse were 24 °C and 32% for daytime and 18 °C and 10% for night-time, respectively, and average solar radiation during experiments was 115 W m−2. The day length was 15 h. After complete emergence, thinning was carried out to six seedlings per pot. Up to 6 weeks after sowing, all pots were irrigated equally, when 20% of available soil water (ASW) was depleted. The experimental duration was from the 25th of August till the 8th of September. Weight method was used to determine the amount of water irrigation. By means of this method, the pots had to be irrigated approximately, every other day. The volume of the irrigation (Virrig) was calculated based on the maximum allowable depletion percentage (p) of the ASW, where 20, 73 and 86% depletions were considered as WW, MWS and SWS treatments, respectively. The soil-water potential based on depletion of ASW was determined by a soil moisture release curve (Fig. 1) which is the relationship between soil water content and water potential (Schuhmann et al., 2011). The ASW and Virrig were determined using Eqs. (1) and (2), respectively (Kirkham, 2014; Kramer and Boyer, 1995).
Fig. 1. Soil moisture release curve.
ASW = (θFc − θPWP ) × ρb × VPot
(1)
Virrig = ASW × P
(2)
where θFC and θPWP are the percentage of gravimetric soil water content at field capacity and permanent wilting point, respectively and ρb is soil bulk density (g cm−3). Cumulative irrigation water applied to a single pot of each irrigation treatments during the experiments (from the 24th of August till the 8th of September) was shown in Fig. 2. 2.2. Thermal imaging and environmental monitoring An imaging system, including imaging frame, thermal infrared and visible cameras was developed and deployed in the greenhouse for taking both thermal and visible images (Fig. 3). At the top of the frame, there was a place for mounting both cameras next to each other in such a position that the distance between the centers of camera lenses was 16 cm. Thermal images were obtained with a thermal infrared camera (SAT-G90, Guangzhou Sat Infrared Technology CO., LTD., China) with a 24° × 20° FOV lens. It was equipped with an uncooled microbolometer sensor with a 240 × 320 fixed pixel array and sensitive to long-wave infrared (LWIR) radiation of 8–14 μm. For using uncooled microbolometer thermal camera, the effects of Focal Plane Array (FPA) temperature, warm-up time, camera settings, and the environment in which the measurements are performed, should be considered (Kusnierek and Korsaeth, 2014). However, the results of other studies showed that the accuracy of uncooled microbolometer thermal camera for detecting canopy temperature is sufficient (Mangus et al., 2016; Meron et al., 2010). The camera also had an accuracy of ± 2 °C, a temperature resolution of 0.08 °C and provided an 8-bit (256 discrete-pixel intensity or digital value, DV) thermal images. These temperature resolution and image DV have been shown to be enough for this kind of studies (Maes et al., 2016; Mangus et al., 2016). The thermal camera was mounted on the frame at a 2 m height nadir to the ground throughout the study; thereby providing a spatial resolution of 2.5 mm pixel−1. At the same time, visible images were obtained by a digital camera (Canon, PowerShot A3100IS 12.1 MP). Pot images were taken at 25th, 27th, 29th of August and 3rd and 8th of September; 1, 3, 5, 11 and 15 days after
Fig. 2. Cumulative irrigation water applied to each pot of three treatments from the 24th of August till the 8th of September.
knowledge, thermography has not been applied for sesame plants; however, it can be recommended as long as there is a significant relationship between stomatal conductance and thermal indices which can be derived from thermal images. In this regard, the main objective of the study was to detect water status in sesame under greenhouse conditions using plant temperature-based indices. The other objectives were to compare gs/RWC measurements with indices extracted from thermal images and to estimate the actual canopy temperature by means of a transfer function. 2. Materials and methods 2.1. Greenhouse experiments This research was conducted on July 2016 in an experimental greenhouse of Isfahan University of Technology, central Iran (32°38′N, 51°39′E, and 1620 m altitude), as a pot experiment. The experiment was a completely randomized design with three irrigation treatments. The treatments were: (a) irrigation at soil water potential of −0.1 MPa as 224
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Fig. 3. Individual component of the imaging system including visible and thermal infrared cameras, artificial reference surfaces, solar meter, digital thermometer, digital sensor for air temperature and relative humidity and PC for data collection and processing.
Fig. 4. Images obtained by (a) digital visible camera and (b) thermal infrared camera of three pots under severe-water stressed (SWS, right bottom), well-watered (WW, left bottom), moderate-water stressed (MWS, left top), and artificial dry and wet references (right top).
1333R Datalogging) with an accuracy of ± 0.1 W m−2. For normalizing the camera temperatures with ambient conditions, artificial wet and dry references were used within the view of the camera. Reference temperatures were measured continuously by a digital thermometer (Gwinstek, GDM-451) with an accuracy of ± 0.1 °C (Fig. 3). The vapor
water stress treatments were applied. In addition to the thermal and visible images, ambient air temperature and relative humidity were measured by digital sensors (Thermo-Hygrometer, TFA) with accuracies of ± 0.1 °C and ± 2%, respectively. Solar radiation was also measured by a solar meter (TES 225
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Tair
Table 2 Lower and upper temperature limits for calculating thermal indices. Indices
Lower temperature limit Twet/ΔT1
Upper temperature limit Tdry/ ΔT3
CWSI1 CWSI2 CWSI3 CWSI4 CWSI5
Wet reference temperature Wet reference temperature Wet reference temperature WW canopy temperature temperature difference of the WW canopy and air Wet reference temperature Wet reference temperature temperature difference of the WW canopy and air
Dry reference temperature air temperature plus 3 °C SWS canopy temperature air temperature plus 3 °C 3 °C
Ig1 Ig2 Ig3
⎛ ⎞ RH ⎞ ⎛ ⎞ × ⎜0.6108 × 10⎝7.5 × 237.3 + Tair ⎠ ⎞⎟ VPD = ⎛1 − ⎛ ⎝ 100 ⎠ ⎠ ⎝ ⎝ ⎠
(3)
where VPD was calculated in kPa. Then, the VPD versus the canopy minus air temperature differences for well-irrigated plants (WW) was plotted in order to determine a non-water stressed baseline (NWSB). For solar radiation greater than 100 W m−2, the baseline can be obtained as follows (Mangus et al., 2016):
TWWC − Tair = a − b × VPD
Dry reference temperature air temperature plus 3 °C 3 °C
(4)
where TWWC is the well-watered canopy temperature (°C), and a (°C) and b (°C kPa−1) are the crop specific intercept and slope for NWSB, respectively. The temperatures of the canopy and artificial references were derived from the thermal infrared images via a supervised method. In this method, the temperatures were obtained by using the software (SATIR Report) of the thermal camera. At first, both visible and thermal images were compared with each other. Then, the plant canopy and the references were recognized and finally, the average temperatures of these regions were determined by the camera software. To evaluate the plant canopy temperature under three irrigation regimes, time variation of the canopy temperatures for WW, MWS, and SWS plants were obtained. 2.3. Plant measurements Immediately after taking the thermal and visible images, the stomatal conductance of the plant was measured on the three uppermost fully developed leaves per pot (Yousefzadeh Najafabadi and Ehsanzadeh, 2017), by a leaf diffusion porometer (SC-1, Decagon Devices, Inc., USA). The mean value of 3 measurements was assigned to each pot. Consequently, one of the leaves was chosen for the gravimetric measurement of RWC which was calculated as proposed by Smart and Bingham (1974) (Eq. (5)).
RWC (%) = Fig. 5. Stomatal conductance versus RWC.
FW − DW × 100 TW − DW
(5)
where FW is the fresh weight of the leaf (g), DW is the weight of the leaf after drying (g) and TW is the weight of the leaf after placing them in distilled water for 4 h in a dark room (g) (Smart and Bingham, 1974). 2.4. Artificial dry and wet references For normalizing the canopy surface temperature for microclimatic conditions, wet and dry artificial reference surfaces were embedded in the scene of the thermal images. For making these references, a horizontal aluminum frame was used and water absorbent non-woven polyester and viscose mixture cloth was covered around the surface of the frame (Loveys et al., 2006). To create the same light absorbing or reflecting conditions as the leaf surfaces, the color of the artificial reference surfaces were chosen in green (Maes et al., 2016). For the wet reference, the cloth was mounted on the frame in such a way that its edges were constantly absorbing water from a reservoir (Fig. 4). Therefore, these edges served as wicks, soaking up the water to replace evaporation. Both wet and dry reference clothes were selected with the same size and were placed at the same height at the middle of the plant canopies. A value of 0.98 for emissivity of both the canopy and references was used. Other studies are also used an emissivity of 0.98 for canopy surfaces of natural vegetation as well as artificial references (Berni et al., 2009b; Lopez et al., 2012; Meron et al., 2013; RamírezCuesta et al., 2017).
Fig. 6. The WW canopy minus air temperature differences versus vapor pressure deficit (VPD).
pressure deficit (VPD) during the experiment was calculated using the measured Tair (°C), RH (%) and solar radiance data from 11:00 to 14:00 h as follows (Monteith and Unsworth, 2013):
2.5. Crop water stress (CWSI) and stomatal conductance (Ig) indices The following equation was used to calculate CWSI for each pot (Eq. (6)): 226
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Fig. 7. Time variation in canopy temperature of sesame under three irrigation regimes (WW, MWS, and SWS) during 11:00 to 14:00 h on the 25th of August.
Fig. 8. Time variation of (a) SWS canopy, dry reference, and ambient air temperatures and (b) WW canopy, wet reference and ambient air temperatures during 5 imaging days, between 11:00 to 14:00 h, from the 25th of August till the 8th of September.
CWSI =
TC − Twet Tdry − Twet
CWSI5 =
(6)
where TC is the plant canopy temperature. In this study, five different approaches were used to determine CWSI (Table 2). In the first four indices, (CWSI1, CWSI2, CWSI3 and CWSI4), Tdry and Twet were calculated according to the methods given in Table 2. In addition, recent studies suggested use of non-water stress baseline (NWSB) information for calculating the CWSI (Ballester et al., 2013; El-Shikha et al., 2007; Erdem et al., 2010; Grant et al., 2006). Therefore, for calculating CWSI5, Eq. (7) was used:
ΔT1 − ΔT2 ΔT1 − ΔT3
(7)
where ΔT2 is the temperature difference of the canopy and air. ΔT1 and ΔT3 were obtained from Table 2 (Mangus et al., 2016). Three different approaches were also used to determine the stomatal conductance Index (Ig) (Jones, 1999). For calculating Ig1 and Ig2, Eq. (8) was used.
Ig = 227
Tdry − Tc Tdry − Twet
(8)
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80 and 23% under SWS treatments as compared with WW treatment, respectively (Fig. 5). In other studies with the aim of evaluating sesame under drought stress, similar reduction in gs was observed in all sesame genotypes under water stress compared to control (Boureima et al., 2012; Seyyedan Jasbi and Ehsanzadeh, 2014; Yousefzadeh Najafabadi and Ehsanzadeh, 2017). RWC versus gs is plotted in Fig. 5. A significant and linear relationship was found between gs and RWC with a goodness of fit (R2) of 0.80. This figure shows how the three irrigation treatments data are separated well along the fitted line. 3.1. Non-water stress baseline (NWSB) The variation of vapor pressure deficit (VPD) in the period of 11:00 to 14:00 h was in the range of 1.8–3.4 kPa during the experiments. Similar approach as suggested by Mangus et al. (2016) was used to predict NWSB canopy temperature. The plot of the WW canopy minus air temperature differences and VPD is shown in Fig. 6. The fitted line is considered as the NWSB for solar radiation greater than 100 W m−2. A significantly linear and inverse relationship between canopy minus air temperature differences (TC-Tair) and the vapor pressure deficit (VPD) of the air for well watered (WW) plants was observed. The specific intercept (a) and slope (b) for the NWSB line were 3 °C and −2.7 °C kPa−1, respectively (Fig. 6). In addition to obtain NWSB canopy temperature, the time variation of canopy temperatures for three irrigation treatments is provided in Fig. 7. The results indicated that the MWS canopy temperatures were located between WW and SWS; because in WW plants, leaf stomata are open and leaf water evaporates through transpiration which makes the plant cool. On the other hand, SWS plant closes its stomata to retain water, which resulted in reducing transpiration and increasing leaf temperature. Therefore, WW and SWS canopy temperatures, can be used as the low- and high-temperature bounds, respectively. Jones (1999) also reported that the temperature of a non-transpiring and fully-transpiring canopy can be considered respectively, as upper and lower bounds for canopy temperature. The time variation of the SWS canopy, dry reference, and air temperatures during 5 imaging days including 1, 3, 5, 10, and 15 days after the first irrigation treatment (applied at the 25th, 27th, 29th of August, 3rd and 8th of September, respectively) is shown in Fig. 8a. The results show that the variation of the dry reference temperature follows well the variation in SWS canopy temperature. As it shown in Fig. 8a, the high threshold of the SWS canopy temperature was approximately equal to the air temperature plus 3 °C (the maximum difference between SWS canopy temperature and Tair, which was happened on the 29th of August, around 13:20 h). However, Irmak et al. (2000) suggested a 5 °C higher than the air temperature as dry reference temperature which may be due to the differences in the environments where the experiments were conducted, greenhouse versus field. The dry reference temperature versus the SWS canopy temperature is plotted in Fig. 9. The results show a significant and linear relationship (R2 = 0.88) between both temperatures. It indicates that the dry reference can mimic the temperature of the SWS canopy successfully. However, as illustrated in Fig. 8a, the temperature of dry reference was higher than SWS canopy. This observation shows that although the SWS plants were considered as no transpiration plants, its minimum transpiration made the SWS plants cooler than the dry reference surface and led to this over-estimation of dry reference. This over-estimation can also be related to differences in overall characteristics between leaf and artificial reference surface. The time variation of the temperatures for the WW canopy, wet reference, and ambient air during 5 imaging days including 1, 3, 5, 10, and 15 days after application of irrigation treatments is shown in Fig. 8b. The wet reference temperature changes follow the WW canopies. The relationship between the wet reference and the WW canopy temperatures is presented in Fig. 9. Although a linear relationship
Fig. 9. Relationships between the dry reference and SWS canopy temperatures (solid line); the wet reference and WW canopy temperatures (dotted line).The dashed line represents the 1:1 line.
where Tdry and Twet were chosen according to Table 2: The third stomatal conductance index (Ig3) was calculated using Eq. (9):
Ig3 =
ΔT3 − ΔT2 ΔT3 − ΔT1
(9)
where ΔT1, ΔT2 and ΔT3 are the same as the ones given for CWSI5. 2.6. Estimation of actual canopy temperature To obtain actual canopy temperature, the linear line fitting method developed by Mangus et al. (2016) was followed to consider the environmental influences, such as air temperature, relative humidity, and solar radiance on measured temperatures by the camera. The wet and dry references temperatures measured by the thermal images (which is called measured temperature), and by a digital thermometer (which is called the actual temperature), were used to develop the transfer function defined by Eq. (10):
Y2 − Y1 = m(X2 − X1)
(10)
where m is the slope of the transfer function, X1 and X2 respectively, are the temperatures of the wet and dry references in thermal images, Y1 and Y2 respectively are the measured surface temperatures of the wet and dry references using thermometer. Then, the actual canopy temperatures can be predicted by Eq. (11):
Y− Y1 = m(X−X1)
(11)
where X is the canopy temperature measured by the thermal camera (which is called measured canopy temperature) and Y is the predicted canopy temperature. To compare the predicted canopy temperature with the one derived by thermal camera, the time variation curve of both predicted and measured canopy temperatures was obtained. Then, the time variation curves of canopy temperature under three irrigations regimes (WW, MWS and SWS) were found. 3. Results and discussion In this study, gs and RWC were used as conventional plant-based methods, for expressing the plant water status. The results showed that the gs and RWC significantly decreased by 50 and 16% under MWS and 228
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Fig. 10. Relationships between the stomatal conductance (gs) and CWSI calculated using (a) artificial dry reference temperature as Tdry (CWSI1), (b) air temperature plus 3 °C as Tdry (CWSI2), (c) SWS canopy temperature as Tdry and artificial wet reference temperature as Twet (CWSI3), (d) air temperature plus 3° C as Tdry and WW canopy temperature as Twet (CWSI4), and (e) Twet obtained from NWSB and air temperature plus 3 °C as Tdry (CWSI5).
229
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Fig. 11. Relationships between the gs and Ig calculated by using (a) an artificial dry reference temperature for Tdry (Ig1), (b) air temperature plus 3 °C for Tdry and the artificial wet reference temperature for Twet (Ig2), and (c) Twet obtained from NWSB and air temperature plus 3° C as Tdry (Ig3).
(R2 = 0.53) between both temperatures was found, however, the wet reference was always cooler than the WW canopy. The average difference was approximately 3 °C. This could be due to different characteristics of leaf and artificial reference surface such as their emissivities. Therefore, the artificial wet reference can be used as a lowtemperature bound in plant thermal imaging. The temperature of artificial wet reference, also was used by other researchers (Jones et al., 2002; Meron et al., 2013), as an indicator of the temperature of a freely transpiring leaf with fully opened stomata. Their observations are consistent with the results obtained in this research.
temperatures reached to a higher temperature than the dry reference. The CWSI2 had also a strong correlation (R2 = 0.73) with gs (Fig. 10b). In addition, the higher slope of the fitted line showed that the CWSI2 had more sensitivity to the changes in the stomatal conductance. Fig. 10a–e also illustrates that the data points are located in three distinct clusters by irrigation treatments. The CWSI3 has also a linear and significant correlation (R2 = 0.67) with the gs (Fig. 10c) and was properly able to estimate the crop water status. Both CWSI1 and CWSI3 have similar slopes (Fig. 10a, c). This was due to the fact that the temperatures of dry reference and the SWS canopy were very close to each other (Fig. 8a). Since, SWS canopy temperature was used as Tdry in CWSI3, therefore, this index was equal to 1 for SWS plants. Therefore, the upper limit of the canopy temperature can be replaced by the dry reference. Similar to the CWSI3, a linear and significant relationship with R2 value of 0.68 was found for CWSI4 (Fig. 10d). Since, WW canopy temperature was used as Twet in CWSI4; therefore, this index was equal to 0 for WW plants. Although, using the artificial wet and dry references was a robust technique for determining the CWSI (Fig. 11a and b), the wet and dry references are not practical within a prolonged monitoring study and
3.2. Crop water stress (CWSI) and stomatal conductance (Ig) indices The different crop water stress (CWSI) and stomatal conductance (Ig) indices were examined in order to recommend the optimum index for estimating sesame water status (Figs. 10 and 11). There was a strong and significant correlation (R2 = 0.74) between gs and CWSI1 (Fig. 10a). Since the temperature of dry reference was close to the SWS canopy (Figs. 8a and 9), the calculated CWSI1 ranged from 0 to approximately 1. The calculated CWSI1 in some points led to the values greater than 1, due to in those moments that the SWS canopy 230
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Fig. 12. Linear relationship of RWC with (a) CWSI5 and (b) Ig3.
Fig. 14. Relationship between TC measured by the thermal camera and predicted by the transfer function. The dashed line represents the 1:1 line.
Fig. 13. Relationship between the artificial wet and dry reference temperatures measured by the thermometer (actual temperature) with the same reference temperatures measured by the thermal camera (measured temperature). Both dashed lines show ± 95% confidence limits.
and they were well able to estimate the crop water status in sesame. Berni et al. (2009a) reported that there was a linear relationship between CWSI and gs in olive. In addition, Yu et al. (2015) reported that there was a logarithmic relationship between CWSI and gs in Firmiana plataninfolia. The three Ig indices versus the gs were plotted in Fig. 11. Both Ig1 and Ig2, have significant and linear relationships with the gs with R2 values of 0.67 and 0.73, respectively. Moreover, both had the ability to satisfactorily predict the crop water status. Similar to CWSI1 and CWSI2, Ig2 had a higher R2 value than the Ig1 (Fig. 11a and b). In addition, Ig3 significantly correlated with the gs and had the higher R2 of 0.75. In general, the Ig was directly correlated with the gs which means that by increasing the gs, the Ig would increase as well. Among all indices, the CWSI5 and Ig3, with high correlation with gs (Figs. 10e and 11c), were plotted versus RWC in Fig. 12a and b. The RWC had significant and linear relationships with both CWSI5 and Ig3 (R2 = 0.65); however, the former and latter had inverse and direct correlations with the RWC, respectively. Thus, the physiological trait of RWC can also be estimated by the temperature-based indices of CWSI/Ig
the researchers have been looking for other approaches to calculate the CWSI without relying on these references. For this purpose, the CWSI5 was calculated by using NWSB’s information and the air temperature plus 3 °C. The results showed a significant and linear relationship (R2 = 0.75) between the gs and CWSI5 (Fig. 10e). Some of CWSI5 values were less than 0, since the values of ΔT1 (canopy minus air temperature differences for WW treatment) in Eq. (8) were very close to the values of ΔT2 (canopy minus air temperature differences for WW treatment as measured by the thermal camera and thermometer, respectively). That led to the CWSI5 values to be less than 0 at some points. The comparison of the four first CWSIs with the CWSI5 showed that the CWSI5 calculated by the empirically determined NWSB can present a robust technique for determining the CWSI without relying on artificial reference temperatures. Similar results were reported by other researchers (Taghvaeian et al., 2014a; Taghvaeian et al., 2014b). In general, the relationships of gs with the five CWSI showed that all five indices had significantly strong and inverse relationships with gs, 231
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Fig. 15. Time variation of predicted and measured TC for plants under three different irrigation regimes (WW, MWS, and SWS) from 11:00 till 14:00 h on the 25th of August 2016.
relationships between CWSI/Ig indices and gs/RWC for sesame. The results showed that thermography is a rapid and accurate method for remote sensing of plant water status in sesame. The average value of VPD in this study was 2.5 kPa that represented a difference of approximately 3 °C between WW canopy and the air temperature under greenhouse conditions. Significantly linear relationships between the artificial dry and wet reference temperatures and SWS and WW canopy temperatures, respectively, proved that for computing the CWSI of sesame, the wet and dry references can be good surrogates. Detecting water stress in sesame using CWSI/Ig indices extracted from the thermal images validated the effectiveness of the developed thermal imaging system. However, CWSI/Ig calculated by the information obtained from NWSB had the higher R2 for estimating the leaf CWSI/RWC. The results obtained under greenhouse conditions showed that if the air temperature is used as dry reference, the Tdry in CWSI/Ig should be considered as the air temperature plus 3 °C. A strong and linear relationship between the actual and measured temperatures of the artificial references was achieved. A 1.5 °C difference between the predicted and measured canopy temperatures was observed. This was due to overestimation of the measured temperature by the thermal camera. The overestimation was probably due to the effects of environmental factors such as: solar radiation, air temperature, and background temperature that could be corrected by using the developed transfer function. Moreover, transfer function was proved as a good solution to compensate all of those environmental effects to detect the actual canopy temperature rather than the relative ones. This study revealed that the non-contact thermal imaging is able to detect water stress and estimate leaf RWC in sesame plant effectively.
but their relationships are not as strong as with gs. Based on the results obtained in this research, the thermography has capability to act as a rapid and accurate method for remote sensing of plant water status in sesame. However, the camera should be on board of a UAV, aircraft or satellite; otherwise, it will be time expensive because of the high number of images required to create the mosaic of all the crop surface. In addition, there are possibilities to use the infrared image sensors under greenhouse conditions, as an interesting option to continuously monitor canopy temperature by selecting pixels of interest for processing. 3.3. Predicting the canopy temperature using the transfer function To quantify the actual canopy temperature, a transfer function to include the environmental influences from the air temperature, relative humidity, and solar radiance was considered. The relationship between the actual temperature of the artificial wet and dry references measured by thermometer (called actual temperature), and the temperature of the same surfaces recorded by the thermal camera (called measured temperature) was plotted in Fig. 13. The results indicated a strong and linear relationship between the actual and measured temperature with a coefficient of determination of 0.97. The relationship between the canopy temperatures measured by the thermal camera and predicted by the transfer function is illustrated in Fig. 14. The slope of the fitted line in Fig. 14 is the same as the one obtained for the artificial references as shown in Fig. 13. Both fitted lines have slopes of 0.93 which show the thermal camera under-estimates the temperatures. The time variation of the predicted and measured canopy temperatures of the plants under three different irrigation regimes (WW, MWS and SWS) is shown in Fig. 15. The results revealed that the average canopy temperature measured by the camera ranged from 25.3 to 28.4 °C, whereas by using the transfer function this range altered to 23.9–26.78 °C, with a difference of 1.5 °C. Thus, there is a 1.5 °C difference between the measured and actual canopy temperatures. This overestimation might be due to the influence of the environmental factors such as solar radiation, air temperature, and background temperature on the thermal camera (Jones, 1999; Jones et al., 2002). Therefore, transfer function can be a good solution to compensate all of those environmental effects to detect the actual canopy temperature rather than the relative ones.
Acknowledgements Financial support for this Project (no. 95835271) was provided by the Iran National Science Foundation (INSF) and Isfahan University of Technology, which is gratefully acknowledged. References Alves, I., Pereira, L.S., 2000. Non-water-stressed baselines for irrigation scheduling with infrared thermometers: a new approach. Irrig. Sci. 19, 101–106. Ballester, C., Jiménez-Bello, M., Castel, J., Intrigliolo, D., 2013. Usefulness of thermography for plant water stress detection in citrus and persimmon trees. Agric. Forest Meteorol. 168, 120–129. Bedigian, D., 2010. Introduction. history of the cultivation and use of sesame. In: Bedigian, D. (Ed.), SPlants-Industrial Profiles. Sesame, the Genus Sesamum. CRC Press, pp. 1–32. Berni, J., Zarco-Tejada, P., Sepulcre-Cantó, G., Fereres, E., Villalobos, F., 2009a. Mapping canopy conductance and CWSI in olive orchards using high resolution thermal
4. Conclusions To the best of our knowledge, this is the first report using thermography to identify the systematic nature of the quantitative 232
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Plant temperature-based indices using infrared thermography for detecting water status in sesame under greenhouse conditions
T
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Azar Khorsandia, Abbas Hemmata, , Seyed Ahmad Mireeia, Rasoul Amirfattahib, Parviz Ehsanzadehc a
Biosystems Engineering Department, College of Agriculture, Isfahan University of Technology, Isfahan, 84156-83111, Iran Department of Electrical and Computer Engineering, Isfahan University of Technology, Isfahan, 84156-83111, Iran c Department of Agronomy and Plant Breeding, College of Agriculture, Isfahan University of Technology, Isfahan, PO Box 84156-83111, Iran b
A R T I C LE I N FO
A B S T R A C T
Keywords: Stomatal conductance (gs) Crop water stress index (CWSI) Stomatal conductance index (Ig) Relative water content (RWC) Water stress Drought
There have been studies on the effect of water stresses on leaf stomatal conductance (gs); however, the scientific reports on using non-contact techniques such as thermography for sesame (Sesamum indicum L.) are rare. The objectives of this study were hence to detect water status in sesame (genotype, “Naz-Takshakhe”) under greenhouse conditions using Crop Water Stress (CWSI) and stomatal conductance (Ig) Indices. One hundred and fifty pots were randomly assigned to three equal groups which were irrigated at soil water potential of −0.1 MPa (well-watered, WW), −1.0 MPa (moderate-water stressed, MWS), and −1.5 MPa (severe-water stressed, SWS). Four formulations of CWSI and two of Ig using canopy temperature (TC) from the WW treatment or temperature from a wet reference for the upper threshold and TC from the SWS treatment, temperature from a dry reference or air temperature plus 3° as the lower threshold were compared. Moreover, an additional CWSI and Ig formulations were also obtained by non-water stress baseline (NWSB) information using meteorological data. Furthermore, the relative water content (RWC) and gs were measured on the youngest and uppermost fully developed leaves of each pot. TC of MWS and SWS plants was higher than WW plants by 1.9 and 2.6 °C, respectively. A significant and linear relationship (P < 0.001) between CWSI/Ig and gs/RWC was found. Therefore, both physiological traits of gs and RWC can be estimated by temperature-based indices of CWSI/Ig. The results also showed the developed system enables us to estimate actual time variations in canopy temperatures. This study validates the effectiveness of using CWSI/Ig for non-destructive detection of water stress and estimation of relative water content in sesame.
1. Introduction Agricultural productivity is limited worldwide by various biotic and abiotic stresses (Kumar, 2013). Drought is of particular importance since it is the main abiotic stress factor which causes the highest yield losses (Manavalan and Nguyen, 2012). From the agricultural point of view, crop water stress occurs when the amount of the water provided through rainfall and irrigation is not sufficient to meet the needs of plant evapotranspiration. Like other crop stresses, water stress influences on a large number of physiological, biochemical, and molecular reactions of plants (Lisar et al., 2012; Manavalan and Nguyen, 2012). Precision irrigation can help to improve water use efficiency and to increase the crop productivity. Several methods for monitoring crop water stress have been introduced which can be classified as: soil-based and plant-based
⁎
measurements (Alves and Pereira, 2000; Cohen et al., 2005; Cohen et al., 2012). Among these methods, soil moisture sensors, pressure chambers and leaf diffusion porometers have been widely used for measuring soil moisture, individual leaf/stem water potential and leaf stomatal conductance, respectively (Ballester et al., 2013; Idso et al., 1977; Moller et al., 2006). However, these techniques are unsuitable for automatic monitoring of crop water stress since they are destructive, labor-intensive, and time-consuming. Therefore, the applications of these methods for spatial and temporal monitoring of crop water stress in large acreage production systems are not feasible (Ballester et al., 2013). From plant physiology, if a plant is experiencing water stress, the stomata tends to close, leads to a reduction in transpiration and rising the leaf temperature (Ballester et al., 2013; Jones, 1999, 2004; Leinonen and Jones, 2004). Therefore, the increase in canopy
Corresponding author. E-mail addresses: [email protected] (A. Khorsandi), [email protected] (A. Hemmat), [email protected] (S.A. Mireei), [email protected] (R. Amirfattahi), [email protected] (P. Ehsanzadeh). https://doi.org/10.1016/j.agwat.2018.04.012 Received 9 July 2017; Received in revised form 31 March 2018; Accepted 8 April 2018 0378-3774/ © 2018 Elsevier B.V. All rights reserved.
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Nomenclature ASW a b CWSI DV DW ΔT1 ΔT2 ΔT3 EC FOV FW SWS gs MWS Ig LWIR MAD NWSB P pH
RDI RH RWC ρb TC Tdry Twet Tair TWWC TW θFC θPWP
Available soil water [cm3] Crop specific intercept for NWSB [°C] Crop specific slope for NWSB [°C kPa−1] Crop water stress index [−] Digital value [−] Dry weight [g] Temperature difference of WW canopy and air [°C] Temperature difference of plant canopy and air [°C]Temperature difference of plant canopy and air [°C] Temperature difference of assumed upper limit canopy (air temperature plus 3 °C) and air [°C] Electrical conductivity [dS m−1] Field of view [Degree] Fresh weight [g] Severe-water stressed [−] Stomatal conductance [mmol m−2 s−1] Moderate-water stressed [−] Stomatal conductance index [−] Long wave infrared [μm] Maximum allowable depletion [cm3] Non-water stressed baseline [−] Fraction of ASW that can be depleted from the root zone [%] Potential of hydrogen [−]
UAV Virrig VPD Vpot WSB WW X X1 X2 Y Y1 Y2
Regulated deficit irrigation [−] Relative humidity [%] Relative water content [%] Bulk density the of the soil [g cm−3] Plant canopy temperature [°C] Upper bound for canopy temperature [°C] Lower bound for canopy temperature [°C] Air temperature [°C] Well-watered canopy temperature [°C] Turgid weight [g] Gravimetric soil–water content at field capacity [%] Gravimetric soil–water content at permanent wilting point [%] Unmanned aerial vehicles [−] Volume of irrigation [cm3] Vapor pressure deficit [kPa] Volume of the pot [cm3] Water stress baseline [−] Well-watered [−] Canopy temperature measured by thermal camera [°C] Temperatures of wet reference in thermal images [°C] Temperatures of dry reference in thermal images [°C] Predicted canopy temperature [°C] Measured surface temperatures of wet reference [°C] Measured surface temperatures of dry reference [°C]
aloft is a satellite, which is space borne. Nevertheless, for satellite data, atmospheric correction may be needed to obtain accurate surface temperature estimates (Ramírez-Cuesta et al., 2017). Several indices have been presented for quantifying and monitoring the crop water stress in which TC (crop canopy temperature) is the main factor for evaluating the crop water status. The first indicator, which was developed for the arid climate of Arizona (where has a similar climate to the arid regions of central Iran) was known as Crop Water Stress Index (CWSI) (Jones, 1999). For calculating CWSI, TC must be normalized with well-watered and non-transpiring crop canopy temperatures as lower and upper leaf temperature bounds, respectively (DeJonge et al., 2015). S. indicum is an ancient warm season oilseed crop which is said to be partially resistant to some environmental constraints (Bedigian, 2010; Mortazavian and Kohpayegani, 2010). Sesame oil contains an unique antioxidants that cannot be found in other edible oils and make the sesame oil the high quality one. In addition to the oil, crop seed is used as a source of proteins, vitamins and minerals for humans as well as in animal feed (Boureima et al., 2012). Thus, the seed owes its great economic potential to the pharmaceutical and cosmetic industries, yet greater economic interest lies in its oil content, which is used in the production of high-quality edible oil. Sesame is usually cultivated in semi-arid region and like many other crops, it is sensitive to drought during its vegetation stage, therefore its production potential can be affected widely by water stress (Boureima et al., 2012). Although there have been studies on the effects of water stresses on leaf stomatal conductance (Yousefzadeh Najafabadi and Ehsanzadeh, 2017), there are few scientific reports on using non-contact instruments such as thermometers for sesame (Hall et al., 1979). To the best of our
temperature can be a good water stress indicator that can be measured by means of infrared thermometers or thermal cameras (Moller et al., 2006). These methods offer non-contact and non-destructive monitoring of crop water stress (Jones, 2004; Leinonen and Jones, 2004). Infrared thermometers are more limited in use since they provide a single point average temperature value of all objects within the sensor's field of view such as shaded and unshaded parts of plant canopy and/or soil surface. The accuracy of this sensor is even worse when the plant is immature because soil covers a majority of the surface (Maes and Steppe, 2012). However, thermal imaging is a potential tool for estimating plant temperature, which can be used as an indicator of stomatal closure and water deficit stress. Recently, the emergence of thermal cameras, particularly, when combined with the automated analysis of images, makes the use of thermal images much easier. The accuracy of this method is higher than that obtained using infrared thermometer because in this imaging method, by segmentation of canopy thermal images, the influence of soil background can be minimized (Maes and Steppe, 2012). However, thermal cameras are capable of measuring relative temperature rather than actual temperature. To quantify actual surface temperature, the thermal camera has to be calibrated at environmental conditions (Mangus et al., 2016). There are three broad categories of remote sensing platforms: ground based, airborne, and satellite. The platform used in this research is laboratory-instruments ground-based which is used almost exclusively for research. However, for monitoring water status in field crops, other remote sensing platforms should be used to cover large surfaces of crops in very short times by mounting thermal cameras on board drones, aircrafts or satellites. Low altitude aircraft/drone is good for acquiring high spatial resolution data. The most stable platform Table 1 Some physical and chemical properties of the experimental soil. Soil texture
Sand (%)
Clay (%)
Silt (%)
θFC (%)
θPWP (%)
ρb (g cm−3)
pH
EC (dS m−1)
Sandy clay loam
55.3
26.4
18.3
18
9
1.3
7.7
2.45
θFC is the percentage of the gravimetric soil–water content at field capacity, θPWP is the percentage of the gravimetric soil–water content at permanent wilting point, and ρb is the bulk density. 223
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well watered (WW), (b) −1.0 MPa as moderate-water stressed (MWS), and (c) −1.5 MPa as severe-water stressed (SWS) (Yousefzadeh Najafabadi and Ehsanzadeh, 2017). Each treatment had 50 replications (pots). Sesame (Sesamum indicum L.) seeds ('Naz-Takshakhe' genotype) were sown on the 13th of July 2016 into 17-cm diameter tapered conical pots. The pots with a volume (Vpot) of 1940 cm3 were filled with equal amounts of gravel (for drainage) and soil (2.5 kg of air-dried soil) with a sandy clay loam texture as potting material. Physico-chemical properties of the soil are given in Table 1. The average air temperature (Tair) and relative humidity (RH) in the greenhouse were 24 °C and 32% for daytime and 18 °C and 10% for night-time, respectively, and average solar radiation during experiments was 115 W m−2. The day length was 15 h. After complete emergence, thinning was carried out to six seedlings per pot. Up to 6 weeks after sowing, all pots were irrigated equally, when 20% of available soil water (ASW) was depleted. The experimental duration was from the 25th of August till the 8th of September. Weight method was used to determine the amount of water irrigation. By means of this method, the pots had to be irrigated approximately, every other day. The volume of the irrigation (Virrig) was calculated based on the maximum allowable depletion percentage (p) of the ASW, where 20, 73 and 86% depletions were considered as WW, MWS and SWS treatments, respectively. The soil-water potential based on depletion of ASW was determined by a soil moisture release curve (Fig. 1) which is the relationship between soil water content and water potential (Schuhmann et al., 2011). The ASW and Virrig were determined using Eqs. (1) and (2), respectively (Kirkham, 2014; Kramer and Boyer, 1995).
Fig. 1. Soil moisture release curve.
ASW = (θFc − θPWP ) × ρb × VPot
(1)
Virrig = ASW × P
(2)
where θFC and θPWP are the percentage of gravimetric soil water content at field capacity and permanent wilting point, respectively and ρb is soil bulk density (g cm−3). Cumulative irrigation water applied to a single pot of each irrigation treatments during the experiments (from the 24th of August till the 8th of September) was shown in Fig. 2. 2.2. Thermal imaging and environmental monitoring An imaging system, including imaging frame, thermal infrared and visible cameras was developed and deployed in the greenhouse for taking both thermal and visible images (Fig. 3). At the top of the frame, there was a place for mounting both cameras next to each other in such a position that the distance between the centers of camera lenses was 16 cm. Thermal images were obtained with a thermal infrared camera (SAT-G90, Guangzhou Sat Infrared Technology CO., LTD., China) with a 24° × 20° FOV lens. It was equipped with an uncooled microbolometer sensor with a 240 × 320 fixed pixel array and sensitive to long-wave infrared (LWIR) radiation of 8–14 μm. For using uncooled microbolometer thermal camera, the effects of Focal Plane Array (FPA) temperature, warm-up time, camera settings, and the environment in which the measurements are performed, should be considered (Kusnierek and Korsaeth, 2014). However, the results of other studies showed that the accuracy of uncooled microbolometer thermal camera for detecting canopy temperature is sufficient (Mangus et al., 2016; Meron et al., 2010). The camera also had an accuracy of ± 2 °C, a temperature resolution of 0.08 °C and provided an 8-bit (256 discrete-pixel intensity or digital value, DV) thermal images. These temperature resolution and image DV have been shown to be enough for this kind of studies (Maes et al., 2016; Mangus et al., 2016). The thermal camera was mounted on the frame at a 2 m height nadir to the ground throughout the study; thereby providing a spatial resolution of 2.5 mm pixel−1. At the same time, visible images were obtained by a digital camera (Canon, PowerShot A3100IS 12.1 MP). Pot images were taken at 25th, 27th, 29th of August and 3rd and 8th of September; 1, 3, 5, 11 and 15 days after
Fig. 2. Cumulative irrigation water applied to each pot of three treatments from the 24th of August till the 8th of September.
knowledge, thermography has not been applied for sesame plants; however, it can be recommended as long as there is a significant relationship between stomatal conductance and thermal indices which can be derived from thermal images. In this regard, the main objective of the study was to detect water status in sesame under greenhouse conditions using plant temperature-based indices. The other objectives were to compare gs/RWC measurements with indices extracted from thermal images and to estimate the actual canopy temperature by means of a transfer function. 2. Materials and methods 2.1. Greenhouse experiments This research was conducted on July 2016 in an experimental greenhouse of Isfahan University of Technology, central Iran (32°38′N, 51°39′E, and 1620 m altitude), as a pot experiment. The experiment was a completely randomized design with three irrigation treatments. The treatments were: (a) irrigation at soil water potential of −0.1 MPa as 224
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Fig. 3. Individual component of the imaging system including visible and thermal infrared cameras, artificial reference surfaces, solar meter, digital thermometer, digital sensor for air temperature and relative humidity and PC for data collection and processing.
Fig. 4. Images obtained by (a) digital visible camera and (b) thermal infrared camera of three pots under severe-water stressed (SWS, right bottom), well-watered (WW, left bottom), moderate-water stressed (MWS, left top), and artificial dry and wet references (right top).
1333R Datalogging) with an accuracy of ± 0.1 W m−2. For normalizing the camera temperatures with ambient conditions, artificial wet and dry references were used within the view of the camera. Reference temperatures were measured continuously by a digital thermometer (Gwinstek, GDM-451) with an accuracy of ± 0.1 °C (Fig. 3). The vapor
water stress treatments were applied. In addition to the thermal and visible images, ambient air temperature and relative humidity were measured by digital sensors (Thermo-Hygrometer, TFA) with accuracies of ± 0.1 °C and ± 2%, respectively. Solar radiation was also measured by a solar meter (TES 225
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Tair
Table 2 Lower and upper temperature limits for calculating thermal indices. Indices
Lower temperature limit Twet/ΔT1
Upper temperature limit Tdry/ ΔT3
CWSI1 CWSI2 CWSI3 CWSI4 CWSI5
Wet reference temperature Wet reference temperature Wet reference temperature WW canopy temperature temperature difference of the WW canopy and air Wet reference temperature Wet reference temperature temperature difference of the WW canopy and air
Dry reference temperature air temperature plus 3 °C SWS canopy temperature air temperature plus 3 °C 3 °C
Ig1 Ig2 Ig3
⎛ ⎞ RH ⎞ ⎛ ⎞ × ⎜0.6108 × 10⎝7.5 × 237.3 + Tair ⎠ ⎞⎟ VPD = ⎛1 − ⎛ ⎝ 100 ⎠ ⎠ ⎝ ⎝ ⎠
(3)
where VPD was calculated in kPa. Then, the VPD versus the canopy minus air temperature differences for well-irrigated plants (WW) was plotted in order to determine a non-water stressed baseline (NWSB). For solar radiation greater than 100 W m−2, the baseline can be obtained as follows (Mangus et al., 2016):
TWWC − Tair = a − b × VPD
Dry reference temperature air temperature plus 3 °C 3 °C
(4)
where TWWC is the well-watered canopy temperature (°C), and a (°C) and b (°C kPa−1) are the crop specific intercept and slope for NWSB, respectively. The temperatures of the canopy and artificial references were derived from the thermal infrared images via a supervised method. In this method, the temperatures were obtained by using the software (SATIR Report) of the thermal camera. At first, both visible and thermal images were compared with each other. Then, the plant canopy and the references were recognized and finally, the average temperatures of these regions were determined by the camera software. To evaluate the plant canopy temperature under three irrigation regimes, time variation of the canopy temperatures for WW, MWS, and SWS plants were obtained. 2.3. Plant measurements Immediately after taking the thermal and visible images, the stomatal conductance of the plant was measured on the three uppermost fully developed leaves per pot (Yousefzadeh Najafabadi and Ehsanzadeh, 2017), by a leaf diffusion porometer (SC-1, Decagon Devices, Inc., USA). The mean value of 3 measurements was assigned to each pot. Consequently, one of the leaves was chosen for the gravimetric measurement of RWC which was calculated as proposed by Smart and Bingham (1974) (Eq. (5)).
RWC (%) = Fig. 5. Stomatal conductance versus RWC.
FW − DW × 100 TW − DW
(5)
where FW is the fresh weight of the leaf (g), DW is the weight of the leaf after drying (g) and TW is the weight of the leaf after placing them in distilled water for 4 h in a dark room (g) (Smart and Bingham, 1974). 2.4. Artificial dry and wet references For normalizing the canopy surface temperature for microclimatic conditions, wet and dry artificial reference surfaces were embedded in the scene of the thermal images. For making these references, a horizontal aluminum frame was used and water absorbent non-woven polyester and viscose mixture cloth was covered around the surface of the frame (Loveys et al., 2006). To create the same light absorbing or reflecting conditions as the leaf surfaces, the color of the artificial reference surfaces were chosen in green (Maes et al., 2016). For the wet reference, the cloth was mounted on the frame in such a way that its edges were constantly absorbing water from a reservoir (Fig. 4). Therefore, these edges served as wicks, soaking up the water to replace evaporation. Both wet and dry reference clothes were selected with the same size and were placed at the same height at the middle of the plant canopies. A value of 0.98 for emissivity of both the canopy and references was used. Other studies are also used an emissivity of 0.98 for canopy surfaces of natural vegetation as well as artificial references (Berni et al., 2009b; Lopez et al., 2012; Meron et al., 2013; RamírezCuesta et al., 2017).
Fig. 6. The WW canopy minus air temperature differences versus vapor pressure deficit (VPD).
pressure deficit (VPD) during the experiment was calculated using the measured Tair (°C), RH (%) and solar radiance data from 11:00 to 14:00 h as follows (Monteith and Unsworth, 2013):
2.5. Crop water stress (CWSI) and stomatal conductance (Ig) indices The following equation was used to calculate CWSI for each pot (Eq. (6)): 226
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Fig. 7. Time variation in canopy temperature of sesame under three irrigation regimes (WW, MWS, and SWS) during 11:00 to 14:00 h on the 25th of August.
Fig. 8. Time variation of (a) SWS canopy, dry reference, and ambient air temperatures and (b) WW canopy, wet reference and ambient air temperatures during 5 imaging days, between 11:00 to 14:00 h, from the 25th of August till the 8th of September.
CWSI =
TC − Twet Tdry − Twet
CWSI5 =
(6)
where TC is the plant canopy temperature. In this study, five different approaches were used to determine CWSI (Table 2). In the first four indices, (CWSI1, CWSI2, CWSI3 and CWSI4), Tdry and Twet were calculated according to the methods given in Table 2. In addition, recent studies suggested use of non-water stress baseline (NWSB) information for calculating the CWSI (Ballester et al., 2013; El-Shikha et al., 2007; Erdem et al., 2010; Grant et al., 2006). Therefore, for calculating CWSI5, Eq. (7) was used:
ΔT1 − ΔT2 ΔT1 − ΔT3
(7)
where ΔT2 is the temperature difference of the canopy and air. ΔT1 and ΔT3 were obtained from Table 2 (Mangus et al., 2016). Three different approaches were also used to determine the stomatal conductance Index (Ig) (Jones, 1999). For calculating Ig1 and Ig2, Eq. (8) was used.
Ig = 227
Tdry − Tc Tdry − Twet
(8)
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80 and 23% under SWS treatments as compared with WW treatment, respectively (Fig. 5). In other studies with the aim of evaluating sesame under drought stress, similar reduction in gs was observed in all sesame genotypes under water stress compared to control (Boureima et al., 2012; Seyyedan Jasbi and Ehsanzadeh, 2014; Yousefzadeh Najafabadi and Ehsanzadeh, 2017). RWC versus gs is plotted in Fig. 5. A significant and linear relationship was found between gs and RWC with a goodness of fit (R2) of 0.80. This figure shows how the three irrigation treatments data are separated well along the fitted line. 3.1. Non-water stress baseline (NWSB) The variation of vapor pressure deficit (VPD) in the period of 11:00 to 14:00 h was in the range of 1.8–3.4 kPa during the experiments. Similar approach as suggested by Mangus et al. (2016) was used to predict NWSB canopy temperature. The plot of the WW canopy minus air temperature differences and VPD is shown in Fig. 6. The fitted line is considered as the NWSB for solar radiation greater than 100 W m−2. A significantly linear and inverse relationship between canopy minus air temperature differences (TC-Tair) and the vapor pressure deficit (VPD) of the air for well watered (WW) plants was observed. The specific intercept (a) and slope (b) for the NWSB line were 3 °C and −2.7 °C kPa−1, respectively (Fig. 6). In addition to obtain NWSB canopy temperature, the time variation of canopy temperatures for three irrigation treatments is provided in Fig. 7. The results indicated that the MWS canopy temperatures were located between WW and SWS; because in WW plants, leaf stomata are open and leaf water evaporates through transpiration which makes the plant cool. On the other hand, SWS plant closes its stomata to retain water, which resulted in reducing transpiration and increasing leaf temperature. Therefore, WW and SWS canopy temperatures, can be used as the low- and high-temperature bounds, respectively. Jones (1999) also reported that the temperature of a non-transpiring and fully-transpiring canopy can be considered respectively, as upper and lower bounds for canopy temperature. The time variation of the SWS canopy, dry reference, and air temperatures during 5 imaging days including 1, 3, 5, 10, and 15 days after the first irrigation treatment (applied at the 25th, 27th, 29th of August, 3rd and 8th of September, respectively) is shown in Fig. 8a. The results show that the variation of the dry reference temperature follows well the variation in SWS canopy temperature. As it shown in Fig. 8a, the high threshold of the SWS canopy temperature was approximately equal to the air temperature plus 3 °C (the maximum difference between SWS canopy temperature and Tair, which was happened on the 29th of August, around 13:20 h). However, Irmak et al. (2000) suggested a 5 °C higher than the air temperature as dry reference temperature which may be due to the differences in the environments where the experiments were conducted, greenhouse versus field. The dry reference temperature versus the SWS canopy temperature is plotted in Fig. 9. The results show a significant and linear relationship (R2 = 0.88) between both temperatures. It indicates that the dry reference can mimic the temperature of the SWS canopy successfully. However, as illustrated in Fig. 8a, the temperature of dry reference was higher than SWS canopy. This observation shows that although the SWS plants were considered as no transpiration plants, its minimum transpiration made the SWS plants cooler than the dry reference surface and led to this over-estimation of dry reference. This over-estimation can also be related to differences in overall characteristics between leaf and artificial reference surface. The time variation of the temperatures for the WW canopy, wet reference, and ambient air during 5 imaging days including 1, 3, 5, 10, and 15 days after application of irrigation treatments is shown in Fig. 8b. The wet reference temperature changes follow the WW canopies. The relationship between the wet reference and the WW canopy temperatures is presented in Fig. 9. Although a linear relationship
Fig. 9. Relationships between the dry reference and SWS canopy temperatures (solid line); the wet reference and WW canopy temperatures (dotted line).The dashed line represents the 1:1 line.
where Tdry and Twet were chosen according to Table 2: The third stomatal conductance index (Ig3) was calculated using Eq. (9):
Ig3 =
ΔT3 − ΔT2 ΔT3 − ΔT1
(9)
where ΔT1, ΔT2 and ΔT3 are the same as the ones given for CWSI5. 2.6. Estimation of actual canopy temperature To obtain actual canopy temperature, the linear line fitting method developed by Mangus et al. (2016) was followed to consider the environmental influences, such as air temperature, relative humidity, and solar radiance on measured temperatures by the camera. The wet and dry references temperatures measured by the thermal images (which is called measured temperature), and by a digital thermometer (which is called the actual temperature), were used to develop the transfer function defined by Eq. (10):
Y2 − Y1 = m(X2 − X1)
(10)
where m is the slope of the transfer function, X1 and X2 respectively, are the temperatures of the wet and dry references in thermal images, Y1 and Y2 respectively are the measured surface temperatures of the wet and dry references using thermometer. Then, the actual canopy temperatures can be predicted by Eq. (11):
Y− Y1 = m(X−X1)
(11)
where X is the canopy temperature measured by the thermal camera (which is called measured canopy temperature) and Y is the predicted canopy temperature. To compare the predicted canopy temperature with the one derived by thermal camera, the time variation curve of both predicted and measured canopy temperatures was obtained. Then, the time variation curves of canopy temperature under three irrigations regimes (WW, MWS and SWS) were found. 3. Results and discussion In this study, gs and RWC were used as conventional plant-based methods, for expressing the plant water status. The results showed that the gs and RWC significantly decreased by 50 and 16% under MWS and 228
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Fig. 10. Relationships between the stomatal conductance (gs) and CWSI calculated using (a) artificial dry reference temperature as Tdry (CWSI1), (b) air temperature plus 3 °C as Tdry (CWSI2), (c) SWS canopy temperature as Tdry and artificial wet reference temperature as Twet (CWSI3), (d) air temperature plus 3° C as Tdry and WW canopy temperature as Twet (CWSI4), and (e) Twet obtained from NWSB and air temperature plus 3 °C as Tdry (CWSI5).
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Fig. 11. Relationships between the gs and Ig calculated by using (a) an artificial dry reference temperature for Tdry (Ig1), (b) air temperature plus 3 °C for Tdry and the artificial wet reference temperature for Twet (Ig2), and (c) Twet obtained from NWSB and air temperature plus 3° C as Tdry (Ig3).
(R2 = 0.53) between both temperatures was found, however, the wet reference was always cooler than the WW canopy. The average difference was approximately 3 °C. This could be due to different characteristics of leaf and artificial reference surface such as their emissivities. Therefore, the artificial wet reference can be used as a lowtemperature bound in plant thermal imaging. The temperature of artificial wet reference, also was used by other researchers (Jones et al., 2002; Meron et al., 2013), as an indicator of the temperature of a freely transpiring leaf with fully opened stomata. Their observations are consistent with the results obtained in this research.
temperatures reached to a higher temperature than the dry reference. The CWSI2 had also a strong correlation (R2 = 0.73) with gs (Fig. 10b). In addition, the higher slope of the fitted line showed that the CWSI2 had more sensitivity to the changes in the stomatal conductance. Fig. 10a–e also illustrates that the data points are located in three distinct clusters by irrigation treatments. The CWSI3 has also a linear and significant correlation (R2 = 0.67) with the gs (Fig. 10c) and was properly able to estimate the crop water status. Both CWSI1 and CWSI3 have similar slopes (Fig. 10a, c). This was due to the fact that the temperatures of dry reference and the SWS canopy were very close to each other (Fig. 8a). Since, SWS canopy temperature was used as Tdry in CWSI3, therefore, this index was equal to 1 for SWS plants. Therefore, the upper limit of the canopy temperature can be replaced by the dry reference. Similar to the CWSI3, a linear and significant relationship with R2 value of 0.68 was found for CWSI4 (Fig. 10d). Since, WW canopy temperature was used as Twet in CWSI4; therefore, this index was equal to 0 for WW plants. Although, using the artificial wet and dry references was a robust technique for determining the CWSI (Fig. 11a and b), the wet and dry references are not practical within a prolonged monitoring study and
3.2. Crop water stress (CWSI) and stomatal conductance (Ig) indices The different crop water stress (CWSI) and stomatal conductance (Ig) indices were examined in order to recommend the optimum index for estimating sesame water status (Figs. 10 and 11). There was a strong and significant correlation (R2 = 0.74) between gs and CWSI1 (Fig. 10a). Since the temperature of dry reference was close to the SWS canopy (Figs. 8a and 9), the calculated CWSI1 ranged from 0 to approximately 1. The calculated CWSI1 in some points led to the values greater than 1, due to in those moments that the SWS canopy 230
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Fig. 12. Linear relationship of RWC with (a) CWSI5 and (b) Ig3.
Fig. 14. Relationship between TC measured by the thermal camera and predicted by the transfer function. The dashed line represents the 1:1 line.
Fig. 13. Relationship between the artificial wet and dry reference temperatures measured by the thermometer (actual temperature) with the same reference temperatures measured by the thermal camera (measured temperature). Both dashed lines show ± 95% confidence limits.
and they were well able to estimate the crop water status in sesame. Berni et al. (2009a) reported that there was a linear relationship between CWSI and gs in olive. In addition, Yu et al. (2015) reported that there was a logarithmic relationship between CWSI and gs in Firmiana plataninfolia. The three Ig indices versus the gs were plotted in Fig. 11. Both Ig1 and Ig2, have significant and linear relationships with the gs with R2 values of 0.67 and 0.73, respectively. Moreover, both had the ability to satisfactorily predict the crop water status. Similar to CWSI1 and CWSI2, Ig2 had a higher R2 value than the Ig1 (Fig. 11a and b). In addition, Ig3 significantly correlated with the gs and had the higher R2 of 0.75. In general, the Ig was directly correlated with the gs which means that by increasing the gs, the Ig would increase as well. Among all indices, the CWSI5 and Ig3, with high correlation with gs (Figs. 10e and 11c), were plotted versus RWC in Fig. 12a and b. The RWC had significant and linear relationships with both CWSI5 and Ig3 (R2 = 0.65); however, the former and latter had inverse and direct correlations with the RWC, respectively. Thus, the physiological trait of RWC can also be estimated by the temperature-based indices of CWSI/Ig
the researchers have been looking for other approaches to calculate the CWSI without relying on these references. For this purpose, the CWSI5 was calculated by using NWSB’s information and the air temperature plus 3 °C. The results showed a significant and linear relationship (R2 = 0.75) between the gs and CWSI5 (Fig. 10e). Some of CWSI5 values were less than 0, since the values of ΔT1 (canopy minus air temperature differences for WW treatment) in Eq. (8) were very close to the values of ΔT2 (canopy minus air temperature differences for WW treatment as measured by the thermal camera and thermometer, respectively). That led to the CWSI5 values to be less than 0 at some points. The comparison of the four first CWSIs with the CWSI5 showed that the CWSI5 calculated by the empirically determined NWSB can present a robust technique for determining the CWSI without relying on artificial reference temperatures. Similar results were reported by other researchers (Taghvaeian et al., 2014a; Taghvaeian et al., 2014b). In general, the relationships of gs with the five CWSI showed that all five indices had significantly strong and inverse relationships with gs, 231
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Fig. 15. Time variation of predicted and measured TC for plants under three different irrigation regimes (WW, MWS, and SWS) from 11:00 till 14:00 h on the 25th of August 2016.
relationships between CWSI/Ig indices and gs/RWC for sesame. The results showed that thermography is a rapid and accurate method for remote sensing of plant water status in sesame. The average value of VPD in this study was 2.5 kPa that represented a difference of approximately 3 °C between WW canopy and the air temperature under greenhouse conditions. Significantly linear relationships between the artificial dry and wet reference temperatures and SWS and WW canopy temperatures, respectively, proved that for computing the CWSI of sesame, the wet and dry references can be good surrogates. Detecting water stress in sesame using CWSI/Ig indices extracted from the thermal images validated the effectiveness of the developed thermal imaging system. However, CWSI/Ig calculated by the information obtained from NWSB had the higher R2 for estimating the leaf CWSI/RWC. The results obtained under greenhouse conditions showed that if the air temperature is used as dry reference, the Tdry in CWSI/Ig should be considered as the air temperature plus 3 °C. A strong and linear relationship between the actual and measured temperatures of the artificial references was achieved. A 1.5 °C difference between the predicted and measured canopy temperatures was observed. This was due to overestimation of the measured temperature by the thermal camera. The overestimation was probably due to the effects of environmental factors such as: solar radiation, air temperature, and background temperature that could be corrected by using the developed transfer function. Moreover, transfer function was proved as a good solution to compensate all of those environmental effects to detect the actual canopy temperature rather than the relative ones. This study revealed that the non-contact thermal imaging is able to detect water stress and estimate leaf RWC in sesame plant effectively.
but their relationships are not as strong as with gs. Based on the results obtained in this research, the thermography has capability to act as a rapid and accurate method for remote sensing of plant water status in sesame. However, the camera should be on board of a UAV, aircraft or satellite; otherwise, it will be time expensive because of the high number of images required to create the mosaic of all the crop surface. In addition, there are possibilities to use the infrared image sensors under greenhouse conditions, as an interesting option to continuously monitor canopy temperature by selecting pixels of interest for processing. 3.3. Predicting the canopy temperature using the transfer function To quantify the actual canopy temperature, a transfer function to include the environmental influences from the air temperature, relative humidity, and solar radiance was considered. The relationship between the actual temperature of the artificial wet and dry references measured by thermometer (called actual temperature), and the temperature of the same surfaces recorded by the thermal camera (called measured temperature) was plotted in Fig. 13. The results indicated a strong and linear relationship between the actual and measured temperature with a coefficient of determination of 0.97. The relationship between the canopy temperatures measured by the thermal camera and predicted by the transfer function is illustrated in Fig. 14. The slope of the fitted line in Fig. 14 is the same as the one obtained for the artificial references as shown in Fig. 13. Both fitted lines have slopes of 0.93 which show the thermal camera under-estimates the temperatures. The time variation of the predicted and measured canopy temperatures of the plants under three different irrigation regimes (WW, MWS and SWS) is shown in Fig. 15. The results revealed that the average canopy temperature measured by the camera ranged from 25.3 to 28.4 °C, whereas by using the transfer function this range altered to 23.9–26.78 °C, with a difference of 1.5 °C. Thus, there is a 1.5 °C difference between the measured and actual canopy temperatures. This overestimation might be due to the influence of the environmental factors such as solar radiation, air temperature, and background temperature on the thermal camera (Jones, 1999; Jones et al., 2002). Therefore, transfer function can be a good solution to compensate all of those environmental effects to detect the actual canopy temperature rather than the relative ones.
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