Prediction and monitoring of leaf water content in soybean plants using terahertz time-domain spectroscopy

Computers and Electronics in Agriculture 170 (2020) 105239

Contents lists available at ScienceDirect

Computers and Electronics in Agriculture journal homepage: www.elsevier.com/locate/compag

Prediction and monitoring of leaf water content in soybean plants using terahertz time-domain spectroscopy Bin Lia,b, Xuting Zhaoa,c, Ying Zhanga, Shujuan Zhangc, Bin Luoa,

T

⁎

a

Beijing Research Center for Information Technology in Agriculture, Beijing 100097, China Key Laboratory of Quantitative Remote Sensing in Agriculture, Ministry of Agriculture, 100097,China c College of Engineering, Shanxi Agricultural University, Taigu 030801, China b

A R T I C LE I N FO

A B S T R A C T

Keywords: THz spectrum Phenotyping Precision agriculture Prediction model Leaf water content Drought stress

Leaf water content (LWC) is one of the physiological parameters most commonly used for describing crop growth status and productivity. Thus, rapid and non-destructive methods for the prediction of LWC are important. Here, a rapid and accurate LWC monitoring method using terahertz time-domain spectroscopy (THz-TDS) was tested on soybean. A high-precision mathematical model was developed to predict LWC based on the results of THzTDS. Next, the model was used to study the effect of various levels of water -stress, different growth media, and leaf treatment with exogenous ABA. Reliable results showed that the correlation coefficient and root mean square error of the prediction set were 0.9153 and 0.0526, respectively. LWC gradually decreased over time under different moisture treatments, and even more slowly under conditions of normal water supply than under water stress. The trend followed by LWC under water deficit was dependent on the water-holding capacity of the growth medium. Soil had the best water-holding capacity, followed by the seedling matrix and the vermiculite matrix. Furthermore, the stomata of the adaxial and abaxial leaf surfaces closed due to water deficit, as determined by laser scanning confocal microscopy (LSCM). We found that stomatal opening in leaves significantly decreased under water stress to prevent excessive water loss, consistent with the observed reduction in leaf moisture content. Results indicated a rapid increase in LWC upon ABA treatment followed by a slower decrease. Similarly, changes in stomatal opening were observed using LSCM, consistent with the changes in leaf water content detected macroscopically. This study showed that THz radiation technology combined with modeling methods provides an effective, contact-free, safe, and non-destructive technique to measure water content in soybean leaves. This technique could facilitate further study of plant-water relations.

1. Introduction

and Dong, 1994). The drying technique is generally time and energy consuming, as it requires comparing leaf fresh and dry weights. The resistance and capacitance methods are both simple but require highly sensitive instruments to obtain accurate measurements (Bensalem et al., 2017). In addition, plant tissue temperature significantly affects the dielectric properties of the samples. Although diverse spectroscopy instruments for visible, near, mid, short-wave, thermal infrared, or hyperspectral images, are adequate analytical tools for LWC detection, these methods usually require isolation of the water-sensitive characteristic range within the radiation spectrum (Nie et al., 2017). With the rapid development of information technology, various spectral and image technologies have been applied to the collection and analysis of plant morphological data. Terahertz (THz) is an advanced technology that has received increasing attention in the field of biological sciences (Coutu et al., 2016; Ge et al., 2015). THz radiation

Soybean is an important crop for grain and oil production, providing most of the protein of plant origin in human diet. Additionally, soybean meal is the main source of protein for the animal feed industry. In ture, water is the main environmental factor affecting soybean yield; therefore, the study of plant water content is an important research direction in plant physiology. Water shortage leads to approximately 44% and 29% reduction in soybean yield during flowering and fruiting, respectively (Xie and Dong, 1994). Therefore, the design and development of real-time and non-destructive methods to monitor leaf water content (LWC) in soybean could be of great benefit to researchers and agronomists alike. Currently, LWC is routinely measured by a variety of methods, such as drying, distillation, capacitance, resistance, and spectroscopy (Xie

⁎

Corresponding author. E-mail address: [email protected] (B. Luo).

https://doi.org/10.1016/j.compag.2020.105239 Received 1 September 2019; Received in revised form 15 January 2020; Accepted 18 January 2020 0168-1699/ © 2020 Elsevier B.V. All rights reserved.

Computers and Electronics in Agriculture 170 (2020) 105239

B. Li, et al.

comprises the electromagnetic radiation in the range between the microwave and the infrared spectral regions, from 0.1 to 10 THz of frequency (wavelengths between 30 μm and 3 mm) (Bensalem et al., 2017; Zhang et al., 2017). The strong attenuation of THz radiation in water (Xu et al., 2006) makes this frequency band a sensitive non-contact probe of hydration with enormous potential for non-destructive detection of water content (Hadjiloucas et al., 1999; Breitenstein et al., 2012; Hadjiloucas et al., 2002). THz technology offers high signal-tonoise ratios (Davies et al., 2002), non-destructive testing (Kawase et al., 2003; Hoshina and Sasaki, 2009) and the THz fingerprint spectrum (Shen et al., 2001; Brady, 2014). THz radiation is used in telecommunications (Yao et al., 2009), biochemistry (Han et al., 2017; Brucherseifer, 2000), safety inspection (Kang et al., 2012), non-destructive testing (Qi et al., 2012; Sun et al., 2016), and biology (Siegel, 2014; Zhang, 2002; Ebbinghaus et al., 2007; Castro-Camus et al., 2013; Smye et al., 2001; Falconer and Markelz, 2012). Several studies have investigated the strong absorption in the THz band by liquid water. Gente et al. (2013) proposed a method to determine volumetric LWC based on transmissive THz time-domain spectroscopy (TDS). Their results showed that the water content obtained by this method is consistent with results obtained using the conventional gravimetric method. Similarly, Castro-Camus et al. (2013) monitored leaf water dynamics of Arabidopsis in-vivo by using THz TDS. They studied the changes in LWC during drought stress and the light-dark cycle, and looked at the effect of abscisic acid (ABA) treatment. Their research showed that changes in THz transmittance reflected changes in water content of Arabidopsis leaves. Long et al. (Long, 2017) used time-domain and frequency-domain spectral images of isolated Scindapsus leaves acquired by using THz technology. These images clearly showed the distribution of water within the leaf. Further, results from studies on the relationship between leaf water and THz spectroscopy data showed that the time-domain minimum spectrum gave the best results. However, soybean is a dicotyledonous plant with more complex structure and pubescence on the leaf abaxial surface, for which no reports have been published on the use of THz technology to detect morphological or physiological parameters. Thus, the present study explored methods to make contact-free measurements of water content in soybean leaves by using transmittance THz technology. Three experiments were designed to validate the method. The first experiment was designed to detect changes in water content in leaves of soybean plants subjected to a gradient of soil water availability, the second experiment was designed to detect changes in leaf moisture in soybean plants growing in different substrates, lastly, the third experiment was designed to detect changes in stomatal conductance in soybean leaves before and after ABA treatment.

Fig. 1. Experimental THz time-domain spectroscopy system. (a) Schematic of a typical THz time-domain setup. A laser emits short pulses of light used for the generation and detection of THz pulses. The optical delay unit enables scanning across the THz waveform. (b) TERA K15 terahertz time-domain spectroscopy system used in the experiments reported herein.

thickness. A portable soil moisture speedometer (Spectrum TDR 100, USA) was used to make quick measurements of soil water content. Leaf microscope images were acquired using a laser scanning confocal microscope (LSCM; Leica TCS SP8, Germany). The experimental data were processed with Matlab R2014b (the Math works, Natick, USA) and Unscramber X 10.1 (CAMO AS, Oslo, Norway). ABA and absolute ethyl alcohol were purchased from Beijing Humaiko Biotechnology Co., Ltd.

2. Materials and methods

2.2. Materials

2.1. Experimental instruments and reagents

2.2.1. Soybean cultivation This work was conducted at the experimental station of the Beijing Academy of Agricultural and Forestry Sciences from June to October in 2017. ‘Zhonghuang’ 13 (growth period: 98 days) was cultivated by the Institute of Crop Science of the Chinese Academy of Agricultural Science. The young stems of ‘Zhonghuang’ 13 are purple, plants grow 70 cm tall, the pod length is approximately 20 cm, and 2–3 effective branches develop and grow with elliptic leaves and gray, fuzzy, and purple flowers. To facilitate on-line scanning of soybean leaves, seedlings were cultivated in plastic pots (25 × 25 × 33 cm), each holding approximately 11 kg of soil. Seeds were planted in a sandy loam extracted from the topsoil layer (20 cm) in the field of the experimental station. The maximum water-holding capacity (field capacity) of this soil was about 32.5%. The required fertilizer was calculated from 224 kg of compound fertilizer per ha, which gave 1.3 g of compound fertilizer per pot. The compound fertilizer contained 16% nitrogen, 6% phosphorus, 18% potassium, and 18% sulfur. The soil was sifted and mixed with the

A central component of a THz time-domain spectrometer (TERA K15, Menlo System, Germany) is a femtosecond laser that emits 90 fs pulses at a central wavelength of 780 nm and at a repetition rate of 100 MHz (Fig. 1). The pulse train from the laser is divided into two beams by using a stereoscopic spectroscope. A pump beam excites the emitting antenna to produce photo-generated carriers that radiate THz waves due to their acceleration by a bias voltage. The second beam is the probe beam, which excites free carriers in the probe antenna. The free carriers form a photocurrent due to the action of the electric field of the THz radiation. An optical delay line is used to change the optical path difference between detected and generated light. The temporal waveform of the electric-field intensity of the THz pulse is obtained by sampling. An electronic balance (Cyanine Sea, Shanghai, China) was used to measure the weight of a leaf blade to a precision of 0.1 mg. An SYNTEK electronic digital display Vernier was used to measure leaf blade 2

Computers and Electronics in Agriculture 170 (2020) 105239

B. Li, et al.

compound fertilizer to fill the pots. Full-grain seeds of the same size and free from disease and insect pests were selected and sowed.

where ω is the frequency, c is the speed of light in vacuum, and d is the leaf thickness.

2.2.2. Sample preparation After sowing on June 3rd, three healthy seedlings with uniform growth were retained in each pot and watered moderately before anthesis to ensure healthy growth of the soybean plants. Soil water content (percent field capacity) was used to classify the degree of water stress: (i) normal water supply (80%), (ii) mild water deficit (65%), (iii) moderate water deficit (50%), and (iv) severe water deficit (35%); five replicates per water treatment were established. Thus, 20 potted soybean plants were grown under different levels of water deficit to allow for experimental plants experiencing multiple leaf water-content conditions. When plants entered the flowering stage, soil water content was reduced to the minimum by withholding water on June 28th; henceforth, soil water content was monitored and controlled to the required level by the weighing method and determined with a portable soil moisture speedometer. Water deficit treatments reached the desired levels on July 3rd. All plants were healthy and homogeneous in color, without visible disease or pest incidence; at this point they were transported back to the agricultural THz spectrum and imaging laboratory. A total of 120 samples (each water deficit treatment represented by 30 leaves) were studied. According to the Kennard–Stone algorithm (Liu et al., 2014), the calibration and prediction sets were divided in a ratio of 3:1.

2.3. Model evaluation index In this experiment, the modeling effect was evaluated by the correlation coefficients of calibration (RC) and prediction (RP), and the root mean square error of calibration (RMSEC) and root mean square error of prediction (RMSEP). The coefficients RC and RP reflect the correlation between the value predicted by the model and the real value; RMSEC and RMSEP represent the deviation between the predicted value and the real value for the calibration set and the prediction set, respectively. Generally, the closer RC and RP to 1, and the closer RMSEC and RMSEP to 0, the better the performance of the prediction model. Additionally, as RC and RP approach unity, the reliability of the prediction increases. The smaller the difference between RMSEC and RMSEP, the more stable the model is (Song, 2017). The formulas for calculating RC, RP, RMSEC, and RMSEP are as follows: nc

Rc =

Rp =

i=1

i=1

np

np

∑ (ypi − ymi )2 / ∑ (ypi − ymean )2 i=1

RMSEC =

2.2.3. Data acquisition (1) LWC measurements

RMSEP = Fresh leaves from the same leaf position in each pot were repeatedly mounted to gauge weight and thickness. To decrease measurement error, the average value of three measurements was retained as the datum. Next, the leaf was carefully placed on the THz setup for measurement. During the transmission measurement, three points were randomly selected for repeated measurements carefully avoiding the leaf vein region as much as possible. Once measurements were finished, the leaves were placed in a thermostat at 60 ℃ and oven-dried for 12 h until a constant weight was obtained. In this research, relative LWC was used as the reference value of water content (WC), which was determined through the following formula:

WC =

Wf - Wd × 100\% Wf

The THz time-domain spectrum of the blank reference Eref (t) and leaf samples Esam (t) were obtained from the THz transmission sensor. The THz wave phase of the blank reference Φref (ω) and leaf samples Φsam (ω) were also obtained. The amplitude and phase of the THz timedomain spectra were reconstructed by applying a Fast Fourier Transform (FFT). The absorption coefficient α (ω) and index of refraction n (ω) were directly calculated by using the data-processing method from Dorney et al. (Dorney et al., 2001) and Duvillaret et al. (Duvillaret et al., 1996; Duvillaret et al., 1999):

nc i=1

i=1

∑ (ypi − ymi )2

1 np

∑ (ypi − ymi )2

i=1

(6)

np i=1

(7)

2.5. Model building methods Partial lease squares (PLS) analysis is a widely applied calibration method in the field of spectral analysis for its ability of dealing with the problems concerning with overlapping bands and collinearity of data. Multivariate linear regression (MLR) method is a statistical method used to estimate or predict a function of a set of known variables. In the present study, PLS (Zhao et al., 2017) and MLR (Sandhu and Chen, 1995) were used to establish the prediction models for the THz timedomain spectrum (minimum, maximum, full spectrum), absorption coefficient (0.6–1.8 THz), and index of refraction (0.6–1.8 THz) of soybean leaves and their water content. The model input included time domain extreme value, time-domain full spectrum, absorption coefficient, refractive. The principal factor number of PLS was 2, 12, 11 and 5, respectively. Next, the relationship linking the leaf spectrum to its

(2)

nc

∑ (ypi − ymi )2 / ∑ (ypi − ymean )2

nc

1 nc

An inverted DMI 6000 microscope configured for a Leica TCS SP8 was used to image the epidermis of sample leaves. The layer-scan program was coded in LAS AF software. We scanned along the z stack layer by layer at a scanning rate of 400 Hz. The excitation wavelength was 488 nm; an oil-immersion lens (HC PL APO CS2 63 × 1.4 OIL) served as an objective lens with a 1.0-airy-unit pinhole; the size of the image frame was 1336 × 1336 pixels; the average line scan was four, and the average surface scan was one. Clear two-dimensional planar images were captured by manually adjusting the position along the z axis.

(2) THz spectra

Rc =

(5)

2.4. Settings for laser scanning confocal microscope

(1)

2 4n (ω) In , d Esam (ω)[n (ω) + 1]2 / E ref(ω)

i=1

(4)

where ypi is the predicted water content in the soybean-leaf i, ymi is the measured water content in soybean-leaf i, ymean is the mean water content of soybean leaves in the calibration or prediction set, and nc and np are the number of leaves in the calibration and prediction sets, respectively.

where Wf is the leaf fresh weight obtained as the average value of three weight measurements, and Wd is the leaf dry weight as per the last measurement after leaf drying in the thermostat.

α (ω) =

nc

∑ (ypi − ymi )2 / ∑ (ypi − ymean )2

(3) 3

Computers and Electronics in Agriculture 170 (2020) 105239

B. Li, et al.

Fig. 2. THz spectrum acquisition and raw data. Table 1 Comparison of LWC results in soybean. Model input

Model

Principal factor number

RC

RMSEC (%)

RP

RMSEP (%)

Time domain extreme value

PLS MLR PLS MLR PLS MLR PLS MLR

2 — 12 — 11 — 5 —

0.9213 −0.8081 0.8915 −0.7083 0.8645 −0.6102 0.619 −0.6895

0.0468 0.6575 0.0792 0.5504 0.0771 0.056 0.0448 0.0456

0.9153 −0.7882 0.8785 −0.7581 0.7882 −0.5441 0.2717 −0.3378

0.0526 0.1055 0.0856 0.1298 0.0991 0.5461 0.0516 0.203

Time-domain full spectrum Absorption coefficient Refractive

different moisture contents, in THz band. The comparison of the results of the mathematical model given above showed that the overall accuracy of the model of the time-domain spectrum exceeds that of the frequency-domain. Therefore, we suggest that the input for the model of the frequency-domain spectrum is data calculated by using Eqs. (2) and (3) after FFT of the time-domain spectrum. Leaf thickness d is the influential factor in the formula. On the one hand, soybean leaves can have trichomes, and the thickness of leaf veins and mesophyll is not uniform, whereby accurate measurement the thickness at the measurement point is rather difficult. Additionally, the error introduced by manually measuring leaf thickness also deviate the optical parameter, which affects the accuracy of LWC prediction. As can be seen in Table 1, the PLS model based on the extreme value of the spectrum showed a relatively effective performance regarding prediction precision. The predictive correlation and root mean square

water content can be established according to the analytical model. A prediction model was established based on the chemical measurements of the corrected set samples, and then the model was used to predict the water content of the prediction set samples to verify the reliability of the prediction model. 3. Results and discussion 3.1. Comparison of LWC modeling results Our first goal was to find a relation between the optical properties of the soybean leaf in the THz time or frequency domains and LWC, that should be consistent among different soybean plants of the same variety. Thus, we tried to establish the prediction mathematical model first, and then use it to determine the amount of water in the soybean leaf. Fig. 2 shows the absorption coefficient spectra of leaves under 4

Computers and Electronics in Agriculture 170 (2020) 105239

B. Li, et al.

Fig. 3. Prediction results using the selected PLS model.

under moderate and severe drought stress, soybean water requirements could not be satisfied; thus, after irrigation ceased, the leaves immediately lost water on the second day. Our experimental results showed that, under suitable water supply, soil water content remained high after the water restriction period, whereby LWC remained relatively constant. However, with continuous evaporation of soil water, soil water content decreased significantly upon initiation of severe water restriction treatment, which led to a substantial decrease in LWC. Thus, THz time-domain transmission is a suitable tool for measuring LWC in real time.

error of the prediction were 0.9153 and 0.0526%, respectively. In Fig. 3, the values of water content in the calibration and prediction sets were plotted against the actual values. The prediction formula of the multivariate regression model is:

Y = 1.6063 × X1 + 2.3206 × X2,

(8)

where Y is LWC, X1 is the time-domain maximum, and X2 is the timedomain minimum. This method can be used for accurate, non-destructive determination of LWC at flowering. 3.2. Prediction of soybean LWC with PLS model

(2) LWC during water deficit in different substrate media

To rapidly determine LWC in soybean, we compared the above series of THz spectral data and found that the PLS model based on THzTDS performed the best. Therefore, in the following experiments, we directly acquired the spectra of leaves, and then obtained their water content by applying formula (8) of the PLS model.

Three media (soil, vermiculite substrate, and seedling substrate) were tested for soybean cultivation under water deficit. The vermiculite matrix is a type of secondary metamorphic mineral containing magnesium and aluminosilicate that shows several advantages; specifically, it is a loose soil with high permeability, strong water absorption, and stable temperature (Zhao et al., 2015; Deng and Liu, 2000). The seedling substrate consists of peat produced in the old-growth forest of Changbai Mountain in northeast China; vermiculite and other additives produced in Lingshou County. In terms of fertility and growth environment, the substrate is more conducive to plant growth than the soil. Therefore, seedlings were germinated in the vermiculite matrix, and then transplanted to different substrates; three seedlings were retained in each pot. Each medium was split into three replicates. The irrigation for all plants before flowering was uniform. Irrigation was discontinued at anthesis, at which point a mature leaf was selected in each pot and marked. THz-TDS of the leaf was measured for five consecutive days, the optimal PLS model was used to predict LWC and the curves were plotted (Fig. 5). The average water content of the three leaves was retained as the final data. Thus, we compared the waterretention ability of soybean plants cultivated in various growth media. Fig. 5 compare of the water content in leaves of soybeans plants grown in three substrates with different water holding capacity: cultivated soil, vermiculite substrate and seedling substrate. During water deprivation, the leves of plants grown in vermiculite or seedling substrate showed a higher water loss rate compared to plants grown in cultivated soil. Water content of cultivated soil decreased slightly on the second day after watering cessation and continued decreasing slowly for four days. However, on the fifth day after watering cessation, the leaves wilted noticeably, and leaf water content decreased sharply. The difference in the rate of water loss between the soil and the substrates is clear. Upon water supply cessation, LWC decreased significantly on the second day both in the seedling and in the vermiculite substrates, but less in the soil. Comparatively, the vermiculite substrate lost more water, as its water content dropped by 74% over five days. According to the results of dynamic LWC, the soil showed a higher water holding capacity, whereby plants expressed a certain level of drought resistance

(1) Changes in soybean leaf water content along a water-deficit gradient A second batch of soybean seeds was planted on June 20, 2018. Seeds were germinated in a matrix and then transplanted into different media. The experiment treatments were designed to recreate the following water gradient to study the changes in leaf water content: normal irrigation, mild, moderate, and severe water deficit (80%, 65%, 50%, and 35% of maximum field capacity, respectively). Each treatment was set with three replicates. As explained in the previous section, soil water content was regulated by manual weighing and a portable soil moisture speed-meter as the plants grew up to the flowering stage (July 11, 2018). One leaf from each pot was selected and marked, and then measured for five consecutive days by THz-TDS at three randomly selected points. Water content of soybean leaves was calculated by using the PLS prediction model. The average water content of three repeated measurements was retained as the water content at each given water-deficit treatment. As shown in Fig. 4, water content changed over five successive days of measurement. Overall, LWC gradually decreased over this period for each water stress treatment, which means that the absorption of THz radiation from the water material decreased with increasing water deficit. In addition, leaf water loss in plants growing at high soilmoisture content was relatively slow. However, the rate of leaf water loss increased with increasing stress level. Thus, the higher the degree of stress, the lower the final LWC. For example, after five days of severe water deficit, LWC was reduced to about 68%, while that of normally irrigated plants remained at about 76%. Under normal irrigation and mild drought stress, water content decreased slowly over the first three days, and then decreased remarkably after four days. The reason might be that soybean plants show certain drought resistance. However, 5

Computers and Electronics in Agriculture 170 (2020) 105239

B. Li, et al.

Fig. 4. Variation in soybean LWC with time at increasing levels of water deficit.

water deficit treatment. The mean stomatal opening on the adaxial leaf surface on the first day was 3.09 μm, and then decreased to 2.28 μm by the fifth day. Compared with the stomatal opening of the adaxial leaf epidermis, the change in the abaxial leaf epidermis was more significant (3.29 and 2.27 μm, respectively). In addition, epidermal cells were turgid under normal water supply and non-turgid under water deficit. Moreover, the gap between epidermal cells sizes increased during water deficit. These results showed that stomata and epidermal cells were sensitive to water deficit. Stomatal closure is the leaf primary response to changes in the environment that allows plants to better adapt to a water shortage (Wen et al., 2014). Thus, during a water deficit, plants control stomatal aperture to prevent excessive water loss (Wang et al., 2017; Cornic, 2000). Stomatal opening is adjusted according to the sufficiency or shortage of plant tissue water; this is consistent with the above macroscopic experimental results. At the early stage of water stress, plants experienced mild drought and managed to maintain water balance by intensifying water absorption and controlling water evaporation, which continued through the duration of water restriction. When the strategies of increasing water absorption and reducing water loss are insufficient to maintain the internal plant water balance, water content, water potential and turgor pressure in the leaf tissues will obviously decrease, and plant injury will occur. At this point, in order to improve water distribution within the plant body, plants take further measures to adjust leaf stomatal opening. Therefore, stomatal opening is also a method to estimate LWC.

over a relatively long period. In contrast, water loss from soybean plants grown in substrates was too rapid showing that the water holding capacity of these materials was weak. In comparison, the vermiculite matrix showed the lowest water holding capacity of all three media tested; therefore, LWC based on THz methods was consistent with water holding capacity of vermiculite or the seedling matrix. Overall, our results verified the potential of THz methods for accurate measurement of LWC. (3) Microscopic changes in soybean leaves under water stress In order to explore the relationship between macroscopic observation by THz prediction and microscopic observation by LSCM, the following experiment was carried out. From the macroscopic viewpoint, the above experiment detected changes in soybean LWC over five successive days. In the following work, optical microscopy images were collected to investigate the microscopic-scale decrease in LWC. When plant water absorption proceeds at a lower rate than transpirational loss, a tissue water deficit develops (Li et al., 2011). In this study, LSCM was used to observe the adaxial and abaxial epidermis of soybean leaves. Soybean leaves under normal irrigation (80% of maximum, field water-holding capacity) were selected as samples for these experiments. A Leica Microsystems LAS AF-TCS MP5 system was used to measure the size of the stomata and mark it in the images. Because of the difference in samples (resulting from differences in sample processing), the number of stomata varied across samples for a given magnification. Thus, we selected the same number of stomata in each image at random and compared them to calculate the size of the stomata. The images shown in Fig. 6 are 1336 × 1336 pixels and clearly showed the leaf stomatal aperture. As shown in Fig. 6, the stomatal leaf aperture changed significantly between normal irrigation and water deficit treatments. Table 2 lists the mean stomatal opening for a given leaf on the first and fifth day of

3.3. Water content of soybean leaves after spraying with exogenous ABA (1) Changes in water content of soybean leaves before and after ABA treatment ABA is a plant hormone that plays an important role in plant growth, development, stress resistance, and gene expression. It can

Fig. 5. LWC in soybean plants grown in different media as a function of time. 6

Computers and Electronics in Agriculture 170 (2020) 105239

B. Li, et al.

Fig. 6. Optical-microscope images of guard cells in soybean leaves: (a) guard cells on the adaxial leaf surface and (b) guard cells on the abaxial leaf surface. The sample was taken from a soybean canopy under 80% irrigation treatment (both experiments were carried out at 11 a.m.). The left panel shows a sample taken on the first day of drought stress; the right panel shows a sample taken on the fifth day of drought stress. Three randomly selected stomata from the adaxial and the abaxial leaf surfaces were marked with red ellipses; these were used to calculate the mean stomatal opening for the leaf. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

(a) Stomatal guard cells on the adaxial leaf surface

(b) Stomatal fuard cells on the abaxial leaf surface because of an ABA-induced stomatal closure. The variation in stomata opening directly affected leaf water status in soybean plants (Pang, 2008; Dzomeku et al., 2016; Mencuccini et al., 2000; Xu and Zhou, 2008). Consistently, spraying with an ABA solution promoted the outflow of potassium and chloride ions (Liu et al., 2013), which resulted in stomata closing, thereby reducing the transpiration rate, and increasing leaf water content. After a short period, the effect of ABA abated and the stomata reopened, whereby the transpiration rate to increase and consequently, LWC decreased. Li et al. (2003) studied wheat leaves and showed that, to hold stomata conductance at a minimum, ABA should be continuously supplied, otherwise the stomata will reopen. This was consistent with the results reported herein. Thus, our experiment showed that THz radiation provided an extremely sensitive probe to monitor water content in soybean leaves.

improve the adaptability of plants to drought, high temperature, chilling, and other environmental stress conditions. To investigate how exogenous ABA affects soybean leaves, three soybean plants were tested under normal watering conditions (80% of field-capacity irrigation treatment). ABA solution (100 μmol/L, prepared in ethanol) was sprayed on both sides of soybean leaves from plants grown in potted soil. We ensured that ABA droplets were fine and that the spray was uniform. A mature leaf was selected from each pot and characterized based on five consecutive THz time-domain measurements before spraying with the ABA solution. After spraying, the liquid was allowed to evaporate from the leaf surface until the surface was moisture free. Next, the leaf was continuously measured by THz-TDS until it wilted. Then, the PLS water-content prediction model was used to calculate water content, where each datum consisted of the average of the three repeated THz-TDS measurements. Fig. 7 shows the water content as a function of time before and after ABA treatment. Water content of the soybean leaf was about 84% before spraying; 20 min after ABA treatment, leaf water content increased and reached a maximum at 90.1% 36 min after ABA treatment, likely

(2) Characteristics of epidermal cells in soybean leaves before and after ABA treatment As detailed above, the THz technique revealed a change in LWC that

Table 2 Average stomatal opening for the adaxial and abaxial leaf surface. Aperture size (adaxial; μm) First day Fifth day

3.76 2.44

2.81 2.35

2.71 2.04

Mean (μm)

Aperture size (abaxial; μm)

3.09 2.28

3.56 2.75

7

3.71 2.5

Mean (μm) 3.71 1.48

3.28 2.75

2.20 1.86

3.29 2.27

Computers and Electronics in Agriculture 170 (2020) 105239

B. Li, et al.

Fig. 7. Effect of ABA application on LWC OF soybean leaves.

(a) Stomatal guard cells on the adaxial surface of a soybean leaf

(b) Stomatal guard cells of the abaxial surface of a soybean leaf Fig. 8. Comparison of stomatal opening before (left) and after (right) ABA treatment.

3.09 and 2.33 μm (3.29 and 2.03 μm), respectively. Thus, spraying the leaf with an ABA solution caused stomata opening to closed significantly. The results of macroscopic experiments showed that leaf water content increased rapidly upon ABA spraying. Consistently, microscopic experiments demonstrated that the application of an ABA solution led to stomatal closure, thereby rapidly reducing water evaporation and increasing LWC. This result was consistent with predictions based on the results of the macroscopic experiments detailed above. In addition, the morphology of leaf epidermal cells changed

resulted from spraying the leaf with exogenous ABA. To understand the microscopic mechanism underlying the change in LWC, LSCM was used to study epidermal cells before and after ABA treatment. As shown in Fig. 8, stomata on both the adaxial and abaxial epidermis of a soybean leaf were all open before spraying the leaf with an ABA solution. However, the stomata were all semi-closed or completely closed soon after ABA treatment. Table 3 summarizes the comparison between the corresponding stomatal openings. Mean stomatal opening for the adaxial (abaxial) leaf surfaces before and after spraying ABA was 8

Computers and Electronics in Agriculture 170 (2020) 105239

B. Li, et al.

Table 3 Average stomatal opening before and after ABA treatment. Aperture size (adaxial; μm) Before After

3.9 2.87

4.03 2

3.84 2.11

Mean (μm)

Aperture size (abaxial; μm)

3.09 2.33

3.79 2.03

4.27 2.24

Mean (μm) 3.05 1.81

3.18 1.9

2.18 2.19

3.29 2.03

interests or personal relationships that could have appeared to influence the work reported in this paper.

markedly before and after ABA treatment. Before ABA treatment, the morphology of the epidermal cells was more irregular than after ABA treatment, and the delimitation of the cells was more obvious.

Acknowledgements 4. Conclusions This study was supported by the Beijing Municipal Natural Science Foundation (6182012), The Science and Technology Innovation Special Construction Funded Program of the Beijing Academy of Agriculture and Forestry Sciences (KJCX20180119), and the “Thirteenth Five-Year” National Key Research and Development Program of China (2016YFD0702002). We would like to thank Editage (www.editage.cn) for English language editing.

As an important physiological trait, LWC has a remarkable impact on plant growth. The water molecule is polar (Yin et al., 2001) and its relaxation time (i.e., the time interval to return to its equilibrium position after being shifted or rotated) ranges from picoseconds to subpicoseconds. Owing to the strong absorption of THz waves by water molecules, THz is an excellent tool for studying plant water status. In addition, the energy of THz photons is very low (Santavicca et al., 2010; Han et al., 2007); thus, THz radiation will not damage the sample. Therefore, compared with other analytical techniques, THz spectroscopy has unique advantages with regard to plant water-content detection. Therefore, it can provide valuable information about plant physiological condition, which is vital for irrigation management to help in preventing drought stress in plants. Our findings can be summarized in the following conclusions:

Declaration of Competing Interest The authors declare that they have no conflict of interests. Appendix A. Supplementary material Supplementary data to this article can be found online at https:// doi.org/10.1016/j.compag.2020.105239.

(1) The PLS model with best performance was obtained for quantitative prediction of the water content of soybean leaves. The predictive correlation and the root mean square error of prediction were 0.9153 and 0.0526, respectively. (2) There was an obvious difference in water loss rate under a drought stress gradient. The more severe the drought stress, the greater the water loss. Moreover, withholding irrigation halted leaf tissues of plants grown in the seedling substrate and in the vermiculite substrate, whereby they showed a much higher rate of water loss, compared with plants grown in soil. (3) Stomatal opening changed remarkably when water supply changed. Especially under water deficit, stomata closed to prevent excessive water loss. (4) When soybean plants were sprayed with ABA, leaf water content increased rapidly, after ten minutes, it began to decrease and continued to decrease gradually until it reached a stable state. Moreover, upon spraying with ABA, stomata closed, which explained why leaf water content increased significantly.

References Xie, P., Dong, Z., 1994. Effects of drought on growth and yield of Soybean at different growth stages. J. Shenyang Agric. Univ. 25, 13–16. Bensalem, M., Sommier, A., Mindeguia, J.C., Batsale, J.C., Pradere, C., 2017. Terahertz measurement of the water content distribution in wood materials. J. Infrared Millimeter Terahertz Waves. 2/3, 1–15. Nie, Pengcheng, Fanggang, Qu., Lin, Lei, Dong, Tao, He, Yong, et al., 2017. Detection of water content in rapeseed leaves using terahertz spectroscopy. Sensors 17, 2830–2842. Coutu, R.A., Medvedev, I.R., Petkie, D.T., 2016. Improved sensitivity MEMS cantilever sensor for terahertz photoacoustic spectroscopy. Sensors 16, 251. Ge, H., Jiang, Y., Lian, F., Zhang, Y., Xia, S., 2015. Characterization of wheat varieties using terahertz time-domain spectroscopy. Sensors 15, 12560–12572. Zhang, W., Wang, S., Zhan, P., Han, Y., 2017. Detection method of mahogany based on terahertz time domain spectroscopy. Acta Optica Sinica. 37 (02), 349–355. Xu, J., Plaxco, K.W., Allen, S.J., 2006. Absorption spectra of liquid water and aqueous buffers between 0.3 and 3.72 THz. J. Chem. Phys. 124 (3), 10373. Hadjiloucas, S., Karatzas, L.S., Bowen, J.W., 1999. Measurements of leaf water content using terahertz radiation. Microw. Theory Tech. IEEE Trans. 47, 142–149. Breitenstein, B., Scheller, M., Shakfa, M.K., Kinder, T., Koch, M., Selmar, D., et al., 2012. Introducing terahertz technology into plant biology, a novel method to monitor changes in leaf water status. J. Appl. Botany Food Qual. 84 (2), 158–161. Hadjiloucas, S., Bowen, J., Galvão, R., 2002. Analysis of spectroscopic measurements of leaf water content at terahertz frequencies using linear transforms. J. Opt. Soc. Am. 19, 2495–2509. Davies, A.G., Linfield, E.H., Johnston, M.B., 2002. The development of terahertz sources and their applications. Phys. Med. Biol. 47, 3679–3689. Kawase, Kodo, Ogawa, Yuichi, Watanabe, Yuuki, Inoue, Hiroyuki, 2003. Non-destructive terahertz imaging of illicit drugs using spectral fingerprints. Opt. Express. 11, 2549–2554. Hoshina, Hiromichi, Sasaki, Yoshiaki, Hayashi, Aya, Otani, Chiko, Kawase, Kodo, 2009. Noninvasive mail inspection system with terahertz radiation. Appl. Spectrosc. 63, 81–86. Shen, J., Sun, J., Li, N., Liang, L., Xu, X., Liu, H., et al. Investigation on THz fingerprint spectrum of illicit drugs. Infrared and Millimeter Waves and 13th International Conference on Terahertz Electronics, The Joint 30th International Conference on (2001, Vol. 1, pp. 321–322). IEEE. Brady, P.K., 2014. System and method for identifying materials using a THz spectral fingerprint in a media with high water content: WO, WO 2014099822 A4 [P]. Yao, J., Chi, N., Yang, P., et al., 2009. Research and prospect of terahertz communication technology. China Laser. 36 (9), 2213–2233. Han, X., Cui, H., Jin, Zhang, Yang, Y., Ma, Y., Dai, G., et al., 2017. Terahertz time-domain spectroscopy for accurate and rapid extraction of optical parameters of thin slices. Infrared Laser Eng. 46 (5) pp. 525003–0525003 (7). Brucherseifer, M., et al., 2000. Label-free probing of the binding state of DNA by timedomain terahertz sensing. Appl. Phys. Lett. 77, 4049–4051.

Due to the complex structure and high cost of THz generation and detection, THz technology is still at the stage of research and development. Although the development of THz technology is still only incipient, its unique characteristics have attracted significant interest from many fields of science and engineering. We expect that, as with other bands in the electromagnetic spectrum, THz technology will have a profound impact on science and technology. CRediT authorship contribution statement Bin Li: Conceptualization, Investigation, Writing - original draft. Xuting Zhao: Formal analysis, Investigation. Ying Zhang: Methodology, Resources. Shujuan Zhang: Software. Bin Luo: Resources, Data curation, Writing - review & editing. Declaration of Competing Interest The authors declare that they have no known competing financial 9

Computers and Electronics in Agriculture 170 (2020) 105239

B. Li, et al.

Syst. Secur. 1 (1), 93–132. Zhao, H., Li, T., Zhao, Z., Liu, C., et al., 2015. Effect of vermiculite and nitrogen, phosphorus and potassium Fertilizer on the growth of Cucumber seedlings. J. Agric. Shanghai. 31 (05), 13–20. Deng, Y., Liu, Z., 2000. Study on growing medium and growth law for containerized seedling stocks grown in greenhouse. Scientia Silvae sinicae. 36 (5), 33–39. Li, W., Li, X., Zhang, S., Shan, L., 2011. The root system hydraulic conductivity and water use efficiency of alfalfa and sorghum under water deficit. Acta Ecologica Sinica. 31 (5), 1323–1333. Wen, Z., Wang, L., Wang, X., Li, L., Cui, J., 2014. Effects of O3 and drought stress on stomatal characteristics of Acer truncatum leaves. J. Ecol. 33 (03), 560–566. Wang, F., Yang, F., Bo, X., Zhou, P., 2017. Study on morphological changes of leaves and stomata of tropical cherry under drought stress. Chinese J. Trop. Crops 38 (08), 1441–1445. Cornic, G., 2000. Drought stress inhibits photosynthesis by decreasing stomatal aperturenot by affecting atp synthesis. Trends Plant Sci. 5 (5), 187–188. Pang, Y., 2008. Effects of Water stress on growth, physiological and Ecological characteristics and nutrient migration of Soybean. Chinese Acad. Agric. Sci. Dzomeku, B., Sarkordie-Addo, J., Darkey, S., Bam, R., Wünsche, J., Staver, C., 2016. Responses of leaf stomatal parameters to induced water stress and its relationship with stomatal conductance in false horn plantain. Int. J. Plant Soil Sci. 12 (2), 1–14. Mencuccini, M., Mambelli, S., Comstock, J., 2000. Stomatal responsiveness to leaf water status in common bean (phaseolus vulgaris l.) is a function of time of day. Plant Cell Environ. 23 (10), 1109–1118. Xu, Z., Zhou, G., 2008. Responses of leaf stomatal density to water status and its relationship with photosynthesis in a grass. J. Exp. Bot. 59 (12), 3317–3325. Liu, H., Shi, C., Zhang, H., Wang, Z., Chai, S., 2013. Effects of potassium on yield, photosynthate distribution, enzymes' activity and aba content in storage roots of sweet potato (ipomoea batatas lam.). Aust. J. Crop Sci. 7 (6), 735–743. Li, Z., Wang, G., Zeng, Z., 2003. ABA study on Plant drought stress. Agric. Res. Arid Areas. 21 (2), 99–104. Yin, P., Wang, Z., Bai, Z., Li, C.D., Xin, X., 2001. A theoretical study on reactivity of singlet phosphinidene by its reacting with polar water molecule. Chem. Phys. 264 (1), 1–8. Santavicca, D.F., Reulet, B., Karasik, B.S., Pereverzev, S.V., Olaya, D., Gershenson, M.E., et al., 2010. Energy resolution of terahertz single-photon-sensitive bolometric detectors. Appl. Phys. Lett. 96 (8), 405. Han, W., Yomoto, M., Kasai, S., 2007. Detection of terahertz photon by GaAs-based single electron transistor at low temperatures GaAs. J. Semicond. 28 (4), 500–506.

Kang, S., Wang, J., Liu, K., Zhang, X., Xie, C., Zhan, T., 2012. Spectral characteristics of terahertz wave. Acta Optica Sinica. 32 (06), 73–77. Qi, S., Zhang, Z., Zhao, K., Han, D., 2012. Non-destructive determination of walnut quality by terahertz time-domain spectroscopy. Spectroscopy Spect. Anal. 32 (12), 3390–3393. Sun, Y., Shi, T., Liu, J., Hong, Z., 2016. Terahertz supermaterial EIT resonance unlabeled biosensor. J. Opt. 36 (03), 269–274. Siegel, P.H., 2014. Terahertz technology in biology and medicine. MTT-S Int. Microwave Sympos. Digest. 1575–1578. Zhang, X., 2002. Terahertz wave imaging, horizons and hurdles. Phys. Med. Biol. 47 (21), 3667. Ebbinghaus, S., Kim, S.J., Heyden, M., Yu, X., Heugen, U., Gruebele, M., et al., 2007. An extended dynamical hydration shell around proteins. Proc. Natl. Acad. Sci. USA 104 (52), 20749–20752. Castro-Camus, E., Palomar, M., Covarrubias, A.A., 2013. Leaf water dynamics of arabidopsis thaliana monitored in-vivo using terahertz time-domain spectroscopy. Sci. Rep. 3 (10), 2910. Smye, S.W., Chamberlain, J.M., Fitzgerald, A.J., Berry, E., 2001. The interaction between terahertz radiation and biological tissue. Phys. Med. Biol. 46 (9), R101. Falconer, R.J., Markelz, A.G., 2012. Terahertz spectroscopic analysis of peptides and proteins. J. Infrared Millimeter Terahertz Waves 33 (10), 973–988. Gente, R., Born, N., Voß, N., Sannemann, W., Léon, J., Koch, M., et al., 2013. Determination of leaf water content from terahertz time-domain spectroscopic data. J. Infrared Millimeter Terahertz Waves. 34 (3–4), 316–323. Long, Y., Zhao, C., Li, B., 2017. A preliminary study on the moisture detection of plant leaves based on terahertz technology. Spectroscopy and Spectroscopic Analysis, 37 (10), 3027–3031. Liu, X., Mao, D., Wang, Z., 2014. Near-infrared spectroscopy for rapid identification of the species of seal. Spectroscopy Spectral Anal. 34 (5), 1259–1263. Dorney, T.D., Baraniuk, R.G., Mittleman, D.M., 2001. J. Opt. Soc. Am. A 18 (7), 1562. Duvillaret, L., Garet, F., Coutaz, J.L., 1996. J. Sel. Top. Quant. Electron. 2 (3), 739–746. Duvillaret, L., Garet, F., Coutaz, J.L., 1999. Appl. Opt. 38 (2), 409. Song, K., Zhang, C., Peng, J., Ye, L., Liu, F. He Yong, 2017. Rapid determination of caffeine content in coffee beans based on laser induced breakdown spectroscopy. Spectroscopy Spectral Anal, 3707, 2199–2204. Zhao, X., Zhang, S., Liu, J., Sun, H., 2017. Study on identification of nectarine varieties by hyperspectral technique combined with CARS-ELM. Modern Food Sci. Technol. 33 (10), 281–287. Sandhu, R., Chen, F., 1995. The multilevel relational (MLR) data model. ACM Trans. Inf.

10