Optical monitoring of the plant growth status using polarimetry

News

HomeHome / News / Optical monitoring of the plant growth status using polarimetry

Jun 25, 2023

Optical monitoring of the plant growth status using polarimetry

Scientific Reports volume 12, Article number: 21841 (2022) Cite this article 1386 Accesses 1 Altmetric Metrics details Polarimetry is a powerful characterization technique that uses a wealth of

Scientific Reports volume 12, Article number: 21841 (2022) Cite this article

1386 Accesses

1 Altmetric

Metrics details

Polarimetry is a powerful characterization technique that uses a wealth of information from electromagnetic waves, including polarization. Using the rich information provided by polarimetry, it is being actively studied in biomedical fields such as cancer and tumor diagnosis. Despite its importance and potential in agriculture, polarimetry for living plants has not been well studied. A Stokes polarimetric imaging system was built to determine the correlation between the polarization states of the light passing through the leaf and the growth states of lettuce. The Stokes parameter s3 associated with circular polarization increased over time and was strongly correlated with the growth of lettuce seedlings. In the statistical analysis, the distribution of s3 followed the generalized extreme value (GEV) probability density function. Salt stress retarded plant growth, and the concentration of treated sodium chloride (NaCl) showed a negative correlation with the location parameter μ of GEV. The clear correlation reported here will open the possibility of polarization measurements on living plants, enabling real-time monitoring of plant health.

The polarization state of light refracted, reflected, or diffracted by a material depends on the properties of the material. Optical polarimetry is used to analyze the physical or chemical properties of materials by measuring and interpreting changes in the polarization state of light1. Ellipsometry is one of the well-known techniques used to characterize materials by measuring polarization states. The versatile power of polarization measurements is not limited to objects such as organic or inorganic compounds but can also be used in organisms (e.g., organs, tissues, and cells). Biomedical diagnostics using polarimetry is one of the most actively studied techniques in this field2,3,4,5. The huge benefit of polarimetric biomedical diagnostics is the noncontact and real-time measurement. For example, the Mueller polarimetric optical fiber is used for the diagnosis of cancers4.

For the same reason, diagnosis of living plants is a promising technique for agriculture. Meanwhile, there are few studies on polarization techniques for living plants6,7,8,9,10,11,12,13. Polarimetry uses not only the amplitude of an electromagnetic wave at a given wavelength of light but also the phase information1. Thus, it can provide more information about the plant than conventional hyperspectral analysis, which only uses the amplitude of the electromagnetic field14,15,16,17,18,19,20,21,22. Many parameters can be obtained from polarimetry, including the polarization azimuth angle and ellipticity. However, most previous research has been limited to using only depolarization information for monitoring the state of plants6,8,12,13. Thus, an exact correlation between the state of plants and the polarization state of light has not yet been revealed and remains an open issue.

Currently, some plants grow in controlled environments (e.g., greenhouses and plant factories) where light, temperature, and humidity are artificially regulated to allow for year-round harvesting. Meanwhile, plants can grow differently even in controlled environments, so monitoring techniques need to be developed. To determine the correlation between the polarization states and plant growth, a Stokes polarimetric imaging system was built. Using the Stokes imaging system, the polarization states of light passing through lettuce leaves were recorded and analyzed. Lettuce (Lactuca sativa L.) was chosen as the subject because it is one of the most popular leafy vegetables grown and consumed worldwide23. In this paper, a strong correlation between plant growth and polarization state was shown, proving the feasibility of polarization measurements for plant monitoring.

In addition, lettuce seedlings treated with different concentrations of sodium chloride (NaCl) were prepared, and the correlation between the salt level and the polarization states was studied. The growth of the plant affects the yield, and it can be retarded under stress conditions related to light quality, nutrition composition, water level, and salt concentration24,25,26,27. Among them, a moderate level of salt could enhance crop quality, while a high level of salt stress inhibits plant growth and results in mortality28,29,30,31,32. By using the proposed method, monitoring the growth of plants could be possible, including the level of salt stress.

Figure 1a shows a schematic of the plant monitoring system using Stokes polarimetry. The probe beam was incident on the CCD camera through a fixed polarizer with a transmission axis angle of 135°, a lettuce leaf, a rotating QWP (\(\delta =\pi /2\)), and a polarizer with \(\alpha =0^\circ\). First, the intensity of the light passed through the lettuce leaf was recorded in a probe area of 25 mm diameter (first row of Fig. 1b). Each image is composed of the transmitted intensity in each pixel at the given time t while the QWP was rotating at a constant angular speed of 20 rad/s. The Fourier coefficients A, B, C and D were obtained by fitting the intensity at each pixel with Fourier expansion33 (second row in Fig. 1b). The Stokes parameters were calculated using the relationship between the Fourier coefficients and the Stokes parameters (third row in Fig. 1b). The degree of polarization (DOP = \(\sqrt{{S}_{1}^{2}+{S}_{2}^{2}+{S}_{3}^{2}}/{S}_{0}\)) was kept in a reasonable range (~ 70%) during the whole measurement process. Pixels with abnormal DOP (DOP ≤ 0% or DOP ≥ 100%) were excluded during the calculations. Most of the abnormal DOP data were shown due to the low light intensity of the beam edge. All sets of Stokes parameters were normalized to S0 = 1.

(a) Plant monitoring system using Stokes polarimetry. (b) Examples of processes to obtain the Stokes image. Each row corresponds to intensity, Fourier coefficients, and Stokes images in order of the first row to the third row.

The Poincare sphere is a useful graphical tool to visualize the polarization states in the Cartesian coordinate system (S1S2S3-axes). Each polarization state (polarization ellipse) is uniquely given by the azimuth angles ψ and ellipticity χ and represented as a point on the Poincare sphere1. Figure 2a shows the probability density of polarization states, which was measured from the reference seedlings 10 days after planting. After passing through the lettuce leaf, most of the polarization states were placed near the equator (S3 = 0) and spread at azimuthal angles from 270° to 360°. The polarization states of the seedlings treated with NaCl also showed a similar distribution, making it difficult to distinguish the difference. Therefore, the Stokes parameter was statistically analyzed instead of the polarization state, as shown in Fig. 2b. The probability distribution of linear polarization components s1 and s2 was a pseudoexponential distribution and a superposition of two extreme distributions, respectively. Meanwhile, the circular polarization component s3 had two extreme distributions near 0, and it became single when applying the absolute to it (|s3|) and was easy to analyze.

(a) Probability density plotted on the Poincaré sphere surface corresponding to polarization states of light after passing through the reference lettuce seedling leaves after 10 days of planting and (b) that of Stokes parameters obtained from the reference seedlings. (c) Box chart of |s3| and (d) means of GEV parameters as a function of treatment days for seedlings with different NaCl concentrations.

The distribution of |s3| was changed by the NaCl concentration and duration of NaCl treatment, as shown in Fig. 2c. The difference between the distributions was tested by the Wilcoxon signed-rank test at a p-value < 0.001. Because the data did not meet a normal distribution, we used the Wilcoxon signed rank test, a nonparametric statistical hypothesis test. The median and median absolute deviation (MAD) of seedlings are summarized in Table 1. At the beginning of the measurement (day 0), the distribution of the reference and 400 mM seedlings was not significantly different from each of them, and the 100 mM and 200 mM seedlings were also statistically identical. After NaCl treatment, the |s3| distributions of the seedlings at the same duration of treatment became significantly different up to 4 groups for the durations of 2, 4, 8, and 10 days. All the distributions (five seedlings) were significantly different from each other at 6 days of treatment. The median and MAD tended to increase over time. Although the median of |s3| did not match the concentration of NaCl, it is meaningful that salt-stressed lettuce could be distinguished by measuring the Stokes parameters. If there are seedlings with a significantly lower median of |s3|, they can be considered salt-stressed. For example, the median of control seedlings was always higher than that of other seedlings, but the median of seedlings treated with 400 mM NaCl was smaller than that of other seedlings.

To find a clearer correlation between the plant state and the polarization state, we tried to fit the data with the generalized extreme value (GEV) distribution. The GEV distribution is widely used to model economic or natural events such as financial risk and rainfall34. The probability density function of the GEV is given by,

where s = (x-μ)/σ, μ is the location parameter, σ is the scale parameter, and ξ is the shape parameter34. The probability density distribution of |s3| was well matched with the GEV, as shown in Fig. 2b. The average GEV parameters of the five replicants are shown in Fig. 2d, where the error bar is the standard deviation. There seems to be a positive linear correlation between GEV parameters and the duration of treatment, while the correlation was weaker at higher NaCl concentrations. The asterisk symbols ***, **, and * in the legend of Fig. 2d represent the significance of ξ at p-value < 0.05, 0.01, 0.001 obtained by analysis of variance (ANOVA). The shape parameter ξ of the reference, 100 mM, and 200 mM seedlings was significant at p-value < 0.001 (***) depending on the duration of treatment. The significance levels of seedlings treated with 300 mM and 400 mM NaCl were 0.01 (**) and 0.05 (*), respectively. Therefore, a high concentration of NaCl interrupted the change in the circular polarization component (s3) over time. A similar trend was also shown in the summary statistics of seedlings treated with 400 mM NaCl (Fig. 2c).

After obtaining the GEV parameters, a statistical correlation analysis was performed. The Spearman correlation between the concentration of NaCl and the location parameters showed the greatest correlation (Fig. S3). Figure 3 shows the results of linear regression on the median of |s3| for each duration of treatment as a function of the concentration of NaCl, where the error bar is the MAD. Before the start of the treatment (0 days), the location parameters were not correlated with the concentration of NaCl. Very after salt stress (2 days), a negative linear correlation was clearly shown, and the negative slope became greater with durations until 10 days. Therefore, the salt stress level of lettuce grown during the same period could be determined by measuring the Stokes parameter. This result could be applied to lettuces grown for different durations of time because the growth stages of the seedlings could be determined by summary statistics (Fig. 2c).

Median (symbols) and linear regression (lines) with 95% confidence range (shades) of location parameter μ for five replicants with different durations of treatments as a function of the concentration of NaCl.

The polarization of light can be changed by optical characteristics such as birefringence, diattenuation, and depolarization. Figure 4 shows the polarization response of lettuce obtained by Mueller matrix decomposition. The values are the average of three measurements, and the uncertainty is the standard deviation. The diattenuation was large at the midrib, while those in the vein and the lamia were small. Meanwhile, the retardation, especially linear retardation, was high in all parts of the leaf. Most of the light passed through the lamia during the experiments. In the lamia region, the retardation is much larger than the diattenuation. Therefore, the change in circular polarization components after passing through the lettuce seedlings is probably related to the optical retardation of the lettuce. Optical retardation in plants has been reported in many works in the literature9,10,11. For example, linear retardation can be shown by the aligned cellulose in the plant, and glucose or layer structures could produce circular retardation35,36,37,38,39,40. As the plant grows, the physical properties (growth parameters) of the leaves increase, e.g., length, weight, and density. Linear retardance is simply given by the product of birefringence and thickness. The birefringence is known to depend on the density so that it would be increased while the plant is growing. Therefore, the linear retardation will naturally increase as well-grown plants have thicker leaves.

Polarization components obtained from a Mueller matrix of a lettuce leaf, where CD and LD are the circular and linear diattenuations, CR and LR are circular and linear retardances and R is the total retardance.

Salt stress is known to hinder the growth of plants, including lettuce. The growth of lettuce seedlings retards with a high concentration of NaCl (Figs. S1, S2). The circular polarization components tended to increase with the growth of the seedling over time (Fig. 2c). The degeneration of growth results in the inhibition of the formation of lettuce cells, including cellulose and other retardation-related parts. Thus, the level of salt stress was shown as the change in the circular polarization component (s3) of the light passed through the lettuce.

The optical response of lettuce was studied, and circular polarization (s3) was found to be sensitive to the growth of the seedlings. The polarization of light passed through the leaves of the lettuce seedlings was measured by the Stokes imaging system. While the seedlings were growing, the median and the variance of the circular polarization components of the Stokes parameters (s3) were increased. The growth of the seedlings was significantly retarded by the high concentration of NaCl. As a result, the circular polarization did not change significantly as the salt stress level increased. The probability density of s3 was well matched with the GEV probability density function, and the GEV parameters then increased over the growth time. Among the GEV parameters, the location parameter (μ) showed the greatest Spearman correlation with the concentration of NaCl. The change in s3 seems to be related to the retardation components of the plants, such as linear retardation from cellulose or circular retardation of glucose.

These results will be helpful for developing a remote sensing system for monitoring the growth (or health) of plants. Compared with the hyperspectral technique, the polarimetry data are much more compact to analyze because a single wavelength is used as in this paper. In addition, Stokes polarimetry requires only the polarization state analyzer, so it has a straightforward configuration compared to Mueller polarimetry. The great statistical significance between s3 and the growth time or the concentration of NaCl is shown. Therefore, the classification of the level of growth and salt stress will be possible with the assistance of machine learning.

The configuration and process of Stokes polarimeters using a rotating retarder with a fixed polarizer have been well established by various studies41,42,43. In the configuration shown in Fig. 1a, the intensity of arbitrarily polarized light incident on a detector (or pixel) can be rewritten as,

where \(\omega\) is the rotational velocity and \({\varphi }_{0}\) is the initial phase. Using Fourier expansion of the light intensity, the Stokes parameters are given by41,

The recorded light intensity of each pixel was fitted by Fourier expansion, and then Fourier coefficients and Stokes parameters were obtained.

The Mueller matrix of the lettuce was measured by a commercial Mueller matrix polarimeter AxoScan (AxoMetrics).

Stokes images were image processed and fitted by using MATLAB (MathWorks). Then, the processed data were statistically analyzed by using RStudio Desktop (RStudio).

Lettuce (Lactuca sativa L.) cultivar ‘Cheong Chi Ma’ (Asia Seed) seeds were sown in 50-cell plug trays (54.4 cm × 28.2 cm × 5.4 cm) filled with soil, and the trays were kept inside a lightbox (65 cm × 35 cm × 50 cm). The seedlings were grown under a fluorescent lamp (TLD 32 W/865RS, Philips) where the photosynthetic photon flux density (PPFD) was 210 ± 10 μmol/m2 s−1 and the photoperiod was 16 h light and 8 h dark at 24 °C and 18 °C, respectively. The relative humidity was kept at 50% during the experiments. The seedlings were subirrigated with tap water for 20 min each morning for 12 days and then treated with NaCl solution for 10 days. The treatment solutions were prepared by dissolving NaCl at concentrations of 100, 200, 300, and 400 mM in tap water. Seedlings treated with only water (0 mM) were used as controls.

The authors confirm that no plant or seed specimens were collected in this study. The seeds were purchased from Asia Seed in South Korea, and all plant materials were grown in the author's laboratory. This study complies with relevant institutional, national, and international guidelines and laws, and no genotyping data were analyzed or generated during the study.

Data underlying the results presented in this paper are not publicly available at this time but may be obtained from the authors upon reasonable request. To request the data, please contact the corresponding author.

Bass, M. Handbook of Optics, Volume I: Geometrical and Physical Optics, Polarized Light, Components and Instruments (McGraw-Hill, 2009).

Google Scholar

Badieyan, S., Ameri, A., Rafii-Tabar, H., Razzaghi, M. R. & Sasanpour, P. Mueller Matrix Polarimetric Imaging of Prostate Tissue. In 2018 IEEE 38th International Conference on Electronics and Nanotechnology, ELNANO 2018—Proceedings 375–378 (IEEE, 2018). https://doi.org/10.1109/ELNANO.2018.8477512.

Ghosh, N. Tissue polarimetry: Concepts, challenges, applications, and outlook. J. Biomed. Opt. 16, 110801 (2011).

Article ADS Google Scholar

He, C. et al. Polarisation optics for biomedical and clinical applications: A review. Light Sci. Appl. 10, 194 (2021).

Article ADS CAS Google Scholar

Qi, J. & Elson, D. S. Mueller polarimetric imaging for surgical and diagnostic applications: A review. J. Biophoton. 10, 950–982 (2017).

Article Google Scholar

Van Eeckhout, A. et al. Depolarizing metrics for plant samples imaging. PLoS ONE 14, e0213909 (2019).

Article Google Scholar

Van Eeckhout, A. et al. Polarimetric imaging microscopy for advanced inspection of vegetal tissues. Sci. Rep. 11, 3913 (2021).

Article ADS Google Scholar

Maxwell, D. J., Partridge, J. C., Roberts, N. W., Boonham, N. & Foster, G. D. The effects of plant virus infection on polarization reflection from leaves. PLoS ONE 11, e0152836 (2016).

Article Google Scholar

Patty, C. H. L. et al. Circular spectropolarimetric sensing of vegetation in the field: Possibilities for the remote detection of extraterrestrial life. Astrobiology 19, 1221–1229 (2019).

Article ADS Google Scholar

Patty, C. H. L. et al. Circular spectropolarimetric sensing of chiral photosystems in decaying leaves. J. Quant. Spectrosc. Radiat. Transf. 189, 303–311 (2017).

Article ADS CAS Google Scholar

Patty, C. H. L. et al. Imaging linear and circular polarization features in leaves with complete Mueller matrix polarimetry. Biochim. Biophys. Acta Gen. Subj. 1862, 1350–1363 (2018).

Article CAS Google Scholar

Lin, L.-H., Lo, Y.-L., Liao, C.-C. & Lin, J.-X. Optical detection of glucose concentration in samples with scattering particles. Appl. Opt. 54, 10425 (2015).

Article ADS CAS Google Scholar

Sarkar, M., Gupta, N. & Assaad, M. Monitoring of fruit freshness using phase information in polarization reflectance spectroscopy. Appl. Opt. 58, 6396 (2019).

Article ADS CAS Google Scholar

Lowe, A., Harrison, N. & French, A. P. Hyperspectral image analysis techniques for the detection and classification of the early onset of plant disease and stress. Plant Methods 13, 1–12 (2017).

Article ADS Google Scholar

Behmann, J., Steinrücken, J. & Plümer, L. Detection of early plant stress responses in hyperspectral images. ISPRS J. Photogramm. Remote Sens. 93, 98–111 (2014).

Article ADS Google Scholar

Rumpf, T. et al. Early detection and classification of plant diseases with Support Vector Machines based on hyperspectral reflectance. Comput. Electron. Agric. 74, 91–99 (2010).

Article Google Scholar

Yoosefzadeh-Najafabadi, M., Earl, H. J., Tulpan, D., Sulik, J. & Eskandari, M. Application of machine learning algorithms in plant breeding: Predicting yield from hyperspectral reflectance in soybean. Front. Plant Sci. 11, 1–14 (2021).

Article Google Scholar

He, K. S., Rocchini, D., Neteler, M. & Nagendra, H. Benefits of hyperspectral remote sensing for tracking plant invasions. Divers. Distrib. 17, 381–392 (2011).

Article Google Scholar

Mishra, P. et al. Close range hyperspectral imaging of plants: A review. Biosyst. Eng. 164, 49–67 (2017).

Article Google Scholar

Thomas, S. et al. Benefits of hyperspectral imaging for plant disease detection and plant protection: A technical perspective. J. Plant Dis. Prot. 125, 5–20 (2018).

Article Google Scholar

Bock, C. H., Poole, G. H., Parker, P. E. & Gottwald, T. R. Plant disease severity estimated visually, by digital photography and image analysis, and by hyperspectral imaging. CRC. Crit. Rev. Plant Sci. 29, 59–107 (2010).

Article Google Scholar

Wu, H. et al. Monitoring plant health with near-infrared fluorescent H2O2 nanosensors. Nano Lett. 20, 2432–2442 (2020).

Article ADS CAS Google Scholar

Food and Agriculture Organization of the United Nations. In Crop and Livestock Statistics. https://www.fao.org/faostat (2020).

Camejo, D. et al. Artificial light impacts the physical and nutritional quality of lettuce plants. Hortic. Environ. Biotechnol. 61, 69–82 (2020).

Article CAS Google Scholar

Al-Maskri, A., Al-Kharusi, L., Al-Miqbali, H. & Khan, M. M. Effects of salinity stress on growth of lettuce (Lactuca sativa) under closed-recycle nutrient film technique. Int. J. Agric. Biol. 12, 377–380 (2010).

CAS Google Scholar

Sofo, A. et al. Different agronomic and fertilization systems affect polyphenolic profile, antioxidant capacity and mineral composition of lettuce. Sci. Hortic. (Amsterd.) 204, 106–115 (2016).

Article CAS Google Scholar

Fu, W., Li, P. & Wu, Y. Effects of different light intensities on chlorophyll fluorescence characteristics and yield in lettuce. Sci. Hortic. (Amsterd.) 135, 45–51 (2012).

Article CAS Google Scholar

Shin, Y. K. et al. Response to salt stress in lettuce: Changes in chlorophyll fluorescence parameters, phytochemical contents, and antioxidant activities. Agronomy 10, 1627 (2020).

Article CAS Google Scholar

Xu, C. & Mou, B. Evaluation of lettuce genotypes for salinity tolerance. HortScience 50, 1441–1446 (2015).

Article Google Scholar

Qin, L. et al. Effect of salt stress on growth and physiology in amaranth and lettuce: Implications for bioregenerative life support system. Adv. Sp. Res. 51, 476–482 (2013).

Article ADS CAS Google Scholar

Acosta-Motos, J. et al. Plant responses to salt stress: Adaptive mechanisms. Agronomy 7, 18 (2017).

Article Google Scholar

Rouphael, Y., Petropoulos, S. A., Cardarelli, M. & Colla, G. Salinity as eustressor for enhancing quality of vegetables. Sci. Hortic. (Amsterd.) 234, 361–369 (2018).

Article CAS Google Scholar

Schaefer, B., Collett, E., Smyth, R., Barrett, D. & Fraher, B. Measuring the Stokes polarization parameters. Am. J. Phys. 75, 163–168 (2007).

Article ADS Google Scholar

de Haan, L. & Ferreira, A. Extreme Value Theory: An Introduction. Analysis (Springer, 2006). https://doi.org/10.1007/0-387-34471-3.

Sun, P., Ma, Y., Liu, W., Yang, Q. & Jia, Q. Mueller matrix decomposition for determination of optical rotation of glucose molecules in turbid media. J. Biomed. Opt. 19, 046015 (2014).

Article ADS Google Scholar

Bezruchenko, V. S., Murauski, A. A. & Muravsky, A. A. Determination of the dispersion of the principal refractive indices for birefringent polypropylene films. J. Appl. Spectrosc. 81, 476–482 (2014).

Article ADS CAS Google Scholar

Avnir, D. Critical review of chirality indicators of extraterrestrial life. New Astron. Rev. 92, 101596 (2021).

Article Google Scholar

Mendoza-Galván, A. et al. Transmission Mueller-matrix characterization of transparent ramie films. J. Vac. Sci. Technol. B 38, 014008 (2020).

Article Google Scholar

Lagerwall, J. P. F. et al. Cellulose nanocrystal-based materials: From liquid crystal self-assembly and glass formation to multifunctional thin films. NPG Asia Mater. 6, 1–12 (2014).

Article Google Scholar

Uetani, K., Koga, H. & Nogi, M. Estimation of the intrinsic birefringence of cellulose using bacterial cellulose nanofiber films. ACS Macro Lett. 8, 250–254 (2019).

Article CAS Google Scholar

Berry, H. G., Gabrielse, G. & Livingston, A. E. Measurement of the Stokes parameters of light. Appl. Opt. 16, 3200 (1977).

Article ADS CAS Google Scholar

Flueraru, C., Latoui, S., Besse, J. & Legendre, P. Error analysis of a rotating quarter-wave plate stokes’ polarimeter. IEEE Trans. Instrum. Meas. 57, 731–735 (2008).

Article ADS Google Scholar

Lizana, Á., Campos, J., Van Eeckhout, A. & Márquez, A. Influence of temporal averaging in the performance of a rotating retarder imaging Stokes polarimeter. Opt. Express 28, 10981 (2020).

Article ADS Google Scholar

Download references

Ministry of Trade, Industry, and Energy (20011031); National Research Foundation of Korea (2019H1A2A1074764, 2019R1A2B5B01069580, 2019R1A6A1A09031717); Jeonbuk National University (BK21 Four Program).

Division of Electronics Engineering, Future Semiconductor Convergence Technology Research Center, Jeonbuk National University, Jeonju, 54896, Korea

Jongyoon Kim, Yunsu Nam & Ji-Hoon Lee

Department of Horticulture, College of Agriculture & Life Sciences, Jeonbuk National University, Jeonju, 54896, Korea

Yu Kyeong Shin & Jun Gu Lee

Core Research Institute of Intelligent Robots, Jeonbuk National University, Jeonju, 54896, Korea

Yu Kyeong Shin & Jun Gu Lee

Institute of Agricultural Science & Technology, Jeonbuk National University, Jeonju, 54896, Korea

Jun Gu Lee

You can also search for this author in PubMed Google Scholar

You can also search for this author in PubMed Google Scholar

You can also search for this author in PubMed Google Scholar

You can also search for this author in PubMed Google Scholar

You can also search for this author in PubMed Google Scholar

J.K. developed the Stokes polarimetric imaging system, analysis the results, wrote the manuscript, and created the figures. Y.K.S. prepared and treated the lettuce seedlings with help of J.G.L. Y.S.N. captured the Stokes polarimetric images. J.-H.L. suggested the system and wrote the manuscripts. All authors contributed to the manuscript.

Correspondence to Ji-Hoon Lee.

The authors declare no competing interests.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

Reprints and Permissions

Kim, J., Shin, Y.K., Nam, Y. et al. Optical monitoring of the plant growth status using polarimetry. Sci Rep 12, 21841 (2022). https://doi.org/10.1038/s41598-022-26023-2

Download citation

Received: 07 September 2022

Accepted: 08 December 2022

Published: 17 December 2022

DOI: https://doi.org/10.1038/s41598-022-26023-2

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

By submitting a comment you agree to abide by our Terms and Community Guidelines. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.