OCT Image Inspections of Indicator Plant Leaves Under

Environmental Stresses

Hayate Goto

and Tatsuo Shiina

Graduate School of Science and Engineering, Chiba University,

1-33 Yayoi-cho, Inage-ku, Chiba-shi, Chiba, 263-8522, Japan

Keywords: Optical Coherence Tomography, Indicator Plant, Environmental Assessment, Water Stress, Ozone Stress.

Abstract: In recent years, environmental pollution has intensified, raising concerns about the health impacts on humans

and plants. In this study, we evaluate the indicator plants that can indicate environmental conditions by Optical

Coherence Tomography (OCT) which can make tomographic images quantitatively and non-invasively

observation. Trifolium repens, commonly known as white clover, which is prevalent in Japan, serves as an

indicator plant for ozone, suggesting that OCT measurements of Trifolium repens enabled the estimation of

ozone concentration. However, to evaluate whether the changes observed inside leaves are specific to ozone,

it is necessary to also differentiate other environmental stresses. In this study, we compared the OCT

measurement results of Trifolium repens grown under ozone stress and water stress. The analysis focused on

variations in tissue thickness, interference light intensity, and texture of the OCT images, while also

considering the stability of the analytical parameters. Differences were observed in the trends of changes in

palisade tissue thickness and interference light intensity under ozone stress and water stress. Although

variations under those stresses were observed in the results of texture analysis, these were not as significant

as those in thickness and intensity. This result indicates that plants induce specific changes within their leaves

due to different stresses, confirming the potential of OCT measurements for environmental assessments.

1 INTRODUCTION

In recent years, the progression of environmental pollution

has become a problem in urban and industrial areas. In

particular, ozone, produced when gases emitted from

vehicles react with sunlight, can become concentrated

enough high to harm plants and animals in the present

automobile-advanced society. To estimate environmental

conditions, there is a method that involves observing

indicator plants that are sensitive to specific or multiple

environmental stress factors(Kitao et al., 2009; Oishi, 2018).

By measuring indicator plants, it is possible to infer the

stress conditions to which the plants are exposed, enabling

a comprehensive evaluation of the surrounding

environment. This approach provides critical data that can

be utilized for the effective control of both the quality and

quantity of crop production. The observation of indicator

plants typically relies on non-quantitative methods, such as

visual inspections or microscopic examinations, which

require pre-treatment that damages the plants. Although

these methods provide insights into environmental

conditions, the development of remote sensing techniques

https://orcid.org/0000-0001-5387-9109

https://orcid.org/0000-0001-9292-4523

capable of quantitatively assessing plant responses without

altering their internal states is essential. In this study, we

used OCT (Optical Coherence Tomography), which can

quantitatively measure and visualize the internal structure

of a sample in a non-invasive, non-contact, and non-

destructive manner(Fercher et al., 1996).

OCT has been studied and developed in the fields of

ophthalmology (Tewarie et al., 2012), dentistry(Colston et al.,

1998; Schneider et al., 2017), and dermatology (Liu et al.,

2020). Research has been especially active in the field of

ophthalmology, where the technology is useful for the

diagnosis of glaucoma through visualization of the retina. It

has also been studied in the field of agriculture and has been

used to diagnose fruit diseases (M. Li et al., 2021; Sharifi et

al., 2023), monitor seed germination (X. Li et al., 2022), and

monitor crops during storage (Srivastava et al., 2018). In

addition to measurements in the laboratory, research has also

been done to carry the devices to plant growth sites to measure

live plants without damaging them by cutting (Lee et al.,

2019). We have developed TD-OCT(Time-Domain OCT)

plant measurement system (Goto, Lagrosas, & Shiina, 2024;

Goto, Lagrosas, Galvez, et al., 2024; Goto & Shiina, 2023).

Goto, H. and Shiina, T.

OCT Image Inspections of Indicator Plant Leaves Under Environmental Stresses.

DOI: 10.5220/0013151100003902

Paper published under CC license (CC BY-NC-ND 4.0)

In Proceedings of the 13th International Conference on Photonics, Optics and Laser Technology (PHOTOPTICS 2025), pages 28-34

ISBN: 978-989-758-736-8; ISSN: 2184-4364

The objective of this study is to evaluate the ozone

status of the surrounding environment to which

Trifolium repens, an ozone indicator plant, is exposed

using feature extraction by OCT measurements.

When plant leaves are exposed to ozone gas, ozone

enters the leaf through the stomata, producing

reactive oxygen species that destroy the palisade

tissue (the regularly arranged tissue located inside the

leaf close to the adaxial epidermis, which is the front

side of the leaf)(Pell et al., 1997). In the case of water

stress, stomatal occlusion occurs to inhibit

transpiration from the stomata. As a result, the

amount of 𝐶𝑂



taken in through the stomata is

reduced and photosynthesis is suppressed(Osakabe et

al., 2014). Thus, when leaves are exposed to different

stresses, different responses are triggered within the

leaf. Therefore, in OCT measurements of stress-

injured leaves, OCT images will reflect different

characteristics for each stress. In this study, we

analyse OCT images of leaves exposed to ozone

stress and water stress, and discuss the feature

extraction that appeared in those leaves. This study

will verify that OCT measurements can provide a

comprehensive observation of the surrounding

environmental conditions to which leaves are

exposed.

2 METHOD

2.1 Optical Coherence Tomography

The OCT (Optical Coherence Tomography) system

developed in our laboratory for plant measurement is

shown in Fig. 1. The system is based on TD-OCT

with a Michelson interferometer configuration, and

OCT measures the relative distance between

interference points by identifying the backscattered

position of light within the sample. The light from the

SLD light source (central wavelength: 1310 nm,

wavelength width: 53 nm) is split into two paths

(reference path and sample path) by a fiber coupler.

In the reference path, the optical path length changes

at a constant speed due to a rotation mechanism and

returns to the fiber coupler. In the sample path, the

light backscattered at each layer of the sample returns

to the fiber coupler with an optical path length

corresponding to the backscattered position. The light

returning from both paths interferes, and the intensity

of the interference light is detected by an oscilloscope.

Since low-coherence light is used, the interference

light intensity is obtained only when the optical path

lengths of the two lights match within the coherence

length. The reflecting position of the light within the

sample is determined by the time difference between

interference points, and the scanning speed of the

optical path length of the reference arm(A-scan).

Additionally, by moving the probe that irradiates the

light parallel to the surface of the sample during

measurement, two-dimensional information

including internal information about the sample can

be obtained (B-scan). A two-dimensional

tomographic image can be created by mapping the

intensity information with colors.

Figure 1: OCT configuration.

Figure 2: Peak detection on OCT image.

Figure 3: Averaged A-line.

SLD

picoscope

Fiber coupler

Sample optical path

Reference optical path

Trigger Signal

Stage Control

OCT Image Inspections of Indicator Plant Leaves Under Environmental Stresses

(a) Before exposure to water stress

(b) After exposure to water stress

Figure 4: OCT images of leaves under water stress.

The axial resolution of the OCT is determined by

the coherence length, which is determined by the

central wavelength and wavelength width. Although

the resolution increases as the central wavelength

becomes shorter, the absorption of chlorophyll

becomes stronger, resulting in a shallower depth.

Therefore, a wavelength of 1310 nm was selected.

The axial resolution calculated from the central

wavelength and wavelength width is 14.2 μm. The

output of the SLD is 15 μW, and the acquisition speed

of the A-line is 25 Hz. During measurement, the OCT

light is incident on the abaxial side of the leaf, and

each A-line is averaged from 16 measurements to

suppress noise. The B-scan image is created by

acquiring 400 A-lines at intervals of 10 μm.

2.2 Signal Analysis

The acquired A-line signals were performed

background light subtraction, intensity correction due

to focal distance displacement, moving average,

normalization, and logarithmic transformation. The

intensity correction due to focal distance was

performed to compensate for intensity attenuation

caused by deviations from the focal distance. This

correction applies the inverse of the focal intensity

distribution that shows the interference light intensity

changes due to the displacement from the focal

position to the A-line.

Based on the analysis of the thickness, intensity,

and texture of the palisade tissue from the B-scan

image, comparisons of measurement results for each

leaf were conducted. Initially, to obtain the location

of the palisade tissue within the B-scan images, peak

detection was performed for each A-line. The results

of the peak detection are indicated by the red lines in

Fig. 2. In the OCT image of leaves in Fig. 2, the

vertical axis represents the depth direction of the leaf

and the horizontal axis indicates the lateral position.

The peak detection identified the first peak (the

interface between the adaxial epidermis and the

palisade tissue) and the second peak (the interface

between the palisade tissue and the spongy tissue).

The distance between the first and second peaks

(representing the thickness of the palisade tissue) was

calculated for each A-line, and the average thickness

across the entire B-scan image was estimated. To

acquire the intensity in the palisade tissue, all A-lines

within the B-scan image were averaged to create a

single A-line (Fig. 3). The minimum intensity

obtained at the location of the palisade tissue from the

peak detection was recorded as the intensity of the

palisade tissue. Texture analysis was performed using

the Gray Level Co-occurrence Matrix (GLCM). In

GLCM, the number of pairs of intensity differences

between adjacent pixels is counted to create a matrix,

thereby extracting local variations within the image.

From the constructed matrix, Contrast, Correlation,

Energy, and Homogeneity were calculated. Contrast

increases as the number of pixel pairs with large

intensity differences in the image. Correlation

becomes larger when pixel pairs have values closer to

the matrix mean. Energy increases with a higher

frequency of identical intensity pairs, while

Homogeneity grows as the number of pixel pairs with

similar intensities increases.

PHOTOPTICS 2025 - 13th International Conference on Photonics, Optics and Laser Technology

(a) Before exposure to ozone gas

(b) After exposure to ozone gas

Figure 5: OCT images of leaves under ozone stress.

2.3 Leaf Samples

The sample used in this study was the five leaves of

Trifolium repens (White Clover), an indicator plant

for ozone gas. The leaves were grown in an incubator

maintained at a constant temperature of 25°C, with 12

hours of light exposure during the day.

In the water stress experiment, an automatic

watering system was used. OCT measurements were

taken for 11 days after stopping watering until the

Trifolium repens wilted. Once the plants had wilted

for 14 days, watering was resumed, and the leaf

recovery process was observed by OCT.

For the ozone stress experiment, an ozone gas

generator was placed in the incubator, and the plants

were grown in an environment with an ozone

concentration of approximately 0.2 ppm during the

measurement period. This concentration can have

significant influences on both humans and plants.

Measurements were taken several times over 10 days

after the ozone generator was introduced to monitor

changes over time.

3 RESULT

3.1 Stress Influences on Leaves

The measurement results before and after applying

water stress to the Trifolium repens leaves are shown

in Fig. 4. In contrast, the results before and after

ozone stress are shown in Fig. 5. In these images, the

horizontal axis represents the lateral position of the

OCT probe, and the vertical axis shows the depth of

the sample. The measurements were taken from the

adaxial side of the leaves, representing the results

when light was illuminated from the top of the images.

In Fig. 4(a), from top to bottom, the adaxial

epidermis, palisade layer, and spongy layer are visible.

The adaxial epidermis appears as a region of stronger

signal intensity around a depth of 0.2 mm in Fig. 4(a).

Below the epidermis, there is a region where the

signal disappears and then reappears, indicating the

presence of the palisade tissue, which is situated

above the spongy layer.

After applying water stress, an increase in signal

density is observed in the spongy layer. In Fig. 4(b),

on the left side, the originally visible palisade tissue

layer appears to have thinned, causing the signals

from the adaxial epidermis and the spongy layer to

merge. It seems to be little change in the main vein.

In contrast, after applying ozone stress, the signal

density decreases in Fig.5(b), and the palisade layer

becomes more distinct, showing changes opposite to

those caused by water stress. Additionally, the main

vein becomes less defined. Since OCT images make

it difficult to perform quantitative assessments or

objectively evaluate small changes, the variations in

the images were quantified by analyzing the intensity,

and thickness of the palisade tissue, and texture using

GLCM (Gray Level Co-occurrence Matrix).

3.2 Intensity Change in Palisade Tissue

Figure 6 illustrates the changes in the intensity of the

palisade tissue (red circles in Fig. 3) under the

stresses. The horizontal axis of Fig. 6 represents the

number of days since each type of stress was applied.

The vertical axis represents the absolute intensity

OCT Image Inspections of Indicator Plant Leaves Under Environmental Stresses

value in the palisade tissue, where a higher value

indicates less reflection from the palisade tissue. The

blue dots represent the results of the water stress

experiment, where the leaves wilted from day 11, and

watering was resumed on day 14. The orange dots

show the results of the ozone stress experiment(up to

10 days).

In the case of water stress, the values decreased

during the period from when watering was stopped

until the leaves wilted. Even after watering was

resumed and the leaves recovered, the intensity of the

palisade tissue remained unchanged, maintaining a

nearly constant value. In contrast, the ozone stress

experiment showed a gradual increase, although the

change was not as significant as in the water stress

experiment.

Under water stress, the cell walls likely hardened

as a response to minimize the effects of the stress,

which resulted in stronger reflections within the cells.

Even after watering resumed, it is possible that the

hardened cell walls did not fully recover during the

measurement period. On the other hand, under ozone

stress, ozone penetrated and damaged the cell walls,

disrupting the organized structure of the palisade

tissue, which likely reduced the amount of light

reflected in the probe. The two types of stress caused

different changes within the leaves, allowing us to

evaluate these differences using OCT.

3.3 Thickness Change in Palisade

Tissue

Figure 7 illustrates the changes in the thickness of the

palisade tissue (between the two red lines in Fig. 2)

under the stresses. The vertical axis of Fig. 7

represents the average thickness of the palisade tissue.

During the water stress period, a decrease in

thickness was observed until wilting(day 11). This

reduction is due to a decrease in internal water content,

reducing cell volume. After watering was resumed,

the thickness approached its original value, but there

were few changes after that. These few changes,

similar to the intensity measurements in the palisade

tissue, indicate that the cell condition did not fully

recover during the measurement period.

In contrast to water stress, the ozone stress

experiment showed a gradual increase in thickness.

This increase will be attributed to water filling the

intercellular spaces by the disruption of the palisade

tissue, resulting in swelling through osmotic pressure.

As with the intensity measurements, the two types of

stress resulted in distinct changes in the palisade

tissue.

Figure 6: Intensity of palisade tissue.

Figure 7: Thickness of palisade layer.

3.4 Texture Change in Palisade Tissue

Figure 8 shows the GLCM measurement results

before and after water stress and ozone stress. The

horizontal axis indicates the number of days, while

the vertical axis shows (a) Contrast, (b) Correlation,

and (c) Homogeneity values, respectively.

During water stress, the Contrast decreased until

wilting occurred(Fig.8(a)). After watering was

resumed, the Contrast temporarily returned to a value

close to its original level but then decreased again,

stabilizing at a certain value. The Contrast value is

higher when there is a greater difference in intensity

between adjacent pixels. In the case of water stress,

the reduction in thickness of the palisade tissue in

Fig.7 led to fewer regions with low signal intensity

within the palisade tissue, resulting in a smaller

intensity difference in Fig.4. The temporary increase

in thickness after resuming watering will have

contributed to the similar trend observed in the

Contrast. In contrast, under ozone stress, the Contrast

initially decreased but returned to its original value

after 10 days. Ozone stress destroys the palisade

tissue, causing light scattering within it to become

random. This randomness contributed to the decrease

in the signal from the palisade tissue. Additionally,

when the leaf is damaged partially by ozone, it will be

0.5

2.5

4.5

6.5

8.5

10.5

0 102030

Intensity of palisade tissue

Day

Water stress

Ozone stress

0.04

0.05

0.06

0.07

0.08

0.09

0.1

0102030

Thickness of palisade layer

Day

Ozone stress

Water stress

PHOTOPTICS 2025 - 13th International Conference on Photonics, Optics and Laser Technology

increased the intensity differences, which causes the

rise in Contrast observed after 10 days.

The Correlation values showed little change for

both water stress and ozone stress(Fig.8(b)). After

resuming watering, there was a temporary significant

decrease in the value compared to other periods, but

it returned to a level similar to the original within a

few days. The Correlation value increases when there

are similar structures in the image. Within the leaf,

similar structures are repeated in the horizontal

direction, causing this value to approach nearly equal

to 1. Immediately after resuming watering, the partial

recovery of only certain areas of the leaf can

introduce some heterogeneity, resulting in a

temporary decrease in the Correlation value.

（a）Contrast

(b) Correlation

Figure 8: The result of GLCM.

Homogeneity increased during water

stress(Fig.8(c)), while it showed little change under

ozone stress. After watering was resumed, the value

remained relatively constant. The Homogeneity value

rises when adjacent pixels have similar intensity pairs.

During water stress, the Homogeneity value changes

due to similar reasons as those affecting Contrast.

The GLCM values exhibited different trends of

change between the two types of stress, similar to the

variations observed in the intensity and thickness of

the palisade tissue; however, these changes were not

as significant. Therefore, to enhance the visibility of

changes in the texture analysis using GLCM related

to the intensity and thickness change, it is necessary

to apply specific image processing techniques such as

preprocessing. Nevertheless, the results indicate that

the internal changes in the leaves of Trifolium repens

exposed to environmental stress can be categorized

according to the type of stress experienced.

4 CONCLUSION

In this study, we apply OCT (Optical Coherence

Tomography) measurement as a method for

evaluating indicator plants to estimate the state of

environmental pollution. To monitor the ozone

pollution, which has been exacerbated by the

development of automobile-based societies, we

conducted OCT measurements on the leaves of

Trifolium repens grown under different conditions.

To quantify changes specific to ozone stress

evaluated in OCT images, we also measured changes

due to water stress for comparison. For quantitative

analysis, we analyzed the interference light intensity,

thickness, and texture (Contrast, Correlation,

Homogeneity) in the palisade tissue.

Although differences between each type of stress

were not clearly visible in the OCT images, changes

in the intensity and thickness of the palisade tissue

were estimated quantitatively. Changes were also

observed in the texture analysis results (Contrast,

Correlation, Homogeneity), but these changes were

not as significant as those in intensity and thickness.

It is possible that more distinct changes could be

deduced by applying image processing techniques to

make the intensity differences within the image

clearer or by changing the direction in which the

GLCM (Gray Level Co-occurrence Matrix) analysis

is performed.

From these results, it can be concluded that

different changes occur in the palisade tissue due to

ozone stress and water stress, and that it is possible to

classify these changes using OCT measurements. By

0.00

5.00

10.00

15.00

20.00

25.00

0 102030

Contrast

Day

Water stress

Ozone stress

0.97

0.98

0.99

1.00

0 102030

Correlation

Day

Water stress

Ozone stress

0.70

0.75

0.80

0.85

0.90

0.95

1.00

0 102030

Homogeneity

Water stress

Ozone stress

OCT Image Inspections of Indicator Plant Leaves Under Environmental Stresses

further advancing detailed research on the influence

of different stresses on leaves, it will become possible

to accurately identify the cause of stress using OCT.

OCT can be taken to the on-site where plants are

grown for measurements, allowing for quick, real-

time, and in vivo estimation of the environmental

conditions on the site. This study demonstrates the

potential to estimate the environmental conditions to

which plants are exposed, which could be beneficial

in agricultural production environments.

ACKNOWLEDGMENTS

This work was supported by JST SPRING, Grant

Number JPMJSP2109

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