A portable and low-cost stomata phenotyping method that could accurately and dynamically measure the characteristic parameters of living stomata

Results of stomatal pore segmentation by our method for images from the literature by Li et al. (2019). (a) Without reflection removal. (b) Stomata with small opening degree. (c) Blurred stomata.

StomataScorer: a portable and high-throughput leaf stomata trait scorer combined with deep learning and an improved CV model

Liang X., Xu X., Wang Z., He L., Zhang K, Liang B,, Ye J., Shi J., Wu X., Dai M., Yang W. (2022)

Xiuying Liang, Xichen Xu, Zhiwei Wang, Lei He, Kaiqi Zhang, Bo Liang, Junli Ye, Jiawei Shi, Xi Wu, Mingqiu Dai, Wanneng Yang,

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Plant Biotechnology Journal. 20(3): 577–591 – https://doi.org/10.1111/pbi.13741

https://onlinelibrary.wiley.com/doi/full/10.1111/pbi.13741

Abstract

To measure stomatal traits automatically and nondestructively, a new method for detecting stomata and extracting stomatal traits was proposed. Two portable microscopes with different resolutions (TipScope with a 40× lens attached to a smartphone and ProScope HR2 with a 400× lens) are used to acquire images of living stomata in maize leaves. FPN model was used to detect stomata in the TipScope images and measure the stomata number and stomatal density. Faster RCNN model was used to detect opening and closing stomata in the ProScope HR2 images, and the number of opening and closing stomata was measured. An improved CV model was used to segment pores of opening stomata, and a total of 6 pore traits were measured. Compared to manual measurements, the square of the correlation coefficient (R2) of the 6 pore traits was higher than 0.85, and the mean absolute percentage error (MAPE) of these traits was 0.02%–6.34%. The dynamic stomata changes between wild-type B73 and mutant Zmfab1a were explored under drought and re-watering condition. The results showed that Zmfab1a had a higher resilience than B73 on leaf stomata. In addition, the proposed method was tested to measure the leaf stomatal traits of other nine species. In conclusion, a portable and low-cost stomata phenotyping method that could accurately and dynamically measure the characteristic parameters of living stomata was developed. An open-access and user-friendly web portal was also developed which has the potential to be used in the stomata phenotyping of large populations in the future.

Rapid non-destructive imaging of leaf surfaces with automated image analysis

Stomata detection using the machine-learning model in wheat (a), rice (b), tomato (c) and Arabidopsis (d) images using the 400x magnification. The model detects and labels stomata with bounding boxes and gives the confidence number in each box. Scale bar = 0.1 mm.

Rapid non-destructive method to phenotype stomatal traits

Pathoumthong P. , Zhen Zhang Z., Roy S., El Habti A. (2022)

Phetdalaphone Pathoumthong, Zhen Zhang,  Stuart Roy,  Abdeljalil El Habti,

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bioRxiv – https://doi.org/10.1101/2022.06.28.497692

https://www.biorxiv.org/content/10.1101/2022.06.28.497692v1.full

Abstract

Background Stomata are tiny pores located on the leaf surface that are central to gas exchange. Stomatal number, size and aperture are key determinants of plant transpiration and photosynthesis, and any variation in these traits can affect plant growth and productivity. Current methods to screen for stomatal phenotypes are tedious, which impedes research on stomatal physiology and hinders efforts to develop resilient crops with optimised stomatal patterning. We developed a rapid non-destructive method to phenotype stomatal traits in four species: wheat, rice, tomato, and Arabidopsis.

Results The method consists of two steps. The first step is to capture images of a leaf surface directly and non-destructively using a handheld microscope, which only takes a few seconds compared to minutes using other methods. This rapid method also provides higher quality images for automated data analysis. The second step is to analyse stomatal features using a machine-learning model that automatically detects, counts stomata and measures size. The accuracy of the machine-learning model in detecting stomata ranged from 89% to 96%, depending on the species.

Conclusions We developed a method that combines rapid non-destructive imaging of leaf surfaces with automated image analysis. The method provides accurate data on stomatal features while significantly reducing time for data acquisition. It can be readily used to phenotype stomata in large populations in the field and in controlled environments.

An aspirated diffusion porometer suitable for field measurement

An aspirated diffusion porometer

Byrne G. F., Rose C. W., Slatyer R. O. (1970)

G.F.Byrne1, C.W.Rose1, R.O.Slatyer2,

1Division of Land Research, C.S.I.R.O., Canberra, A.C.T. Australia

2Research School of Biological Sciences, Australian National University, Canberra, A.C.T. Australia

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Agricultural Meteorology 7: 39-44 – ISSN 0002-1571 – https://doi.org/10.1016/0002-1571(70)90005-1

https://www.sciencedirect.com/science/article/pii/0002157170900051

Abstract:

An aspirated diffusion porometer suitable for field measurement of leaf diffusive resistance, on leaves of small dimensions if so desired, is described. The forced circulation of air through the instrument cavity eliminates the errors due to thermal convection which have been a problem with instruments using molecular diffusion as the transport mechanism; it also significantly reduces instrument response time. The calibration and measurement procedures are described and illustrated.

Hematoxylin and Safranin for Staining Plant Materials

Delafield’S Hematoxylin and Safranin for Staining Plant Materials

Dean H. L. (2009)

Stain Technology 15(2): –

https://www.tandfonline.com/doi/abs/10.3109/10520294009110650

Abstract

An improved schedule is suggested for staining plant materials in Delafield’s hematoxylin and safranin. Tissues are stained first in Delafield’s hematoxylin. A short bath in acidulated water (1 or 2 drops concentrated HCl to 100 cc.) removes objectionable precipitates, and at the same time serves as a destaining agent. The acid bath must be followed quickly by a thoro wash in tap water, or dilute lithium carbonate solution, to restore the original dark blue color (made reddish in the acid bath) of the hematoxylin and to “set” the stain. Once the hematoxylin solution is satisfactory, none of the reagents ordinarily used will remove it—unless they contain acid. Tissues are counterstained in rapid safranin (5 drops analin in 100 cc. of 1% safranin 0 in 50% ethyl alcohol); this materially lessens the time necessary for staining. The safranin is de-stained in 50% ethyl alcohol (which does not affect the hematoxylin) until sharp differentiation is secured. If destaining is too slow, or differentiation poor, a quick rinse in acidulated 50% alcohol usually sharpens contrast of the stains. This must be followed quickly by a wash in 50% alcohol containing lithium carbonate to neutralize the acid. Dehydrate, and mount as usual. This schedule allows each stain to be individually, and independently, controlled at the will of the operator.

Photo’s of stomata

Microscopy of Nature

Vossen R. (xxxx)

https://microscopyofnature.com/stomata

Introduction

Stomata are small openings that mainly occur on the underside of leaves. They are surrounded by specialised cells and they regulate the gas exchange between the plant and it’s environment, the plant is ‘breathing’ through them, as it were. Stomata are very recognizable by the two kidney- or bean-shaped guard cells that regulate the size of the opening. The guard cells are specialised epidermal cells which contain vacuoles that change their shape when water is absorbed due to a process called turgor, causing the stomata to open. The stomata are opened by stimuli like high humidity and bright light. Depending on the plant family, guard cells are often surrounded by so-called subsidiary cells.

As for the morphology of stomata, some different shapes can be distinguished:

● anomocytic: without subsidiary cells

● paracytic: with lateral subsidiary cells oriented parallel with the guard cells

● tetracytic: with both lateral and polar subsidiary cells

Stomata are fascinating objects to study, in each plant they look a bit different or are positioned differently. To observe stomata we need to peel off the epidermis from the underside of a leaf. If you tear a leaf apart, often a small piece of the epidermis will come off. Especially with thicker leaves this works quite well. Easy to begin with are the leaves of HostaPrunus laurocerasus (Cherry laurel) and Tradescantia.

(Continued)

Active targeting of live leaf structures using NPs coated with molecular recognition molecules: LM6-M-AuNPs strongly adhered to the stomata and remained on the leaf surface after rinsing

Fig. 1 (A) Pathogens on a leaf surface can penetrate open stoma and trichomes, colonizing the
apoplast and spreading to other parts of the plant. (B) NPs can potentially be targeted directly tospecific guard cell wall or trichome-based chemical moieties to efficiently prevent pathogen entry

Protein Coating Composition Targets Nanoparticles to Leaf Stomata and Trichomes

Spielman-Sun E., Avellan A., Bland G. D., Clement E. T., Tappero R. V., Acerbo A. S., Lowry G. V. (2019)

  • Spielman-Sun Eleanor: Carnegie Mellon University, Department of Civil
    and Environmental Engineering
  • Avellan Astrid: Carnegie Mellon University, Department of Civil and
    Environmental Engineering; Center for Environmental Implications of
    Nanotechnology
  • Bland Garret: Carnegie Mellon University, Department of Civil and
    Environmental Engineering
  • Clement Emma; Carnegie Mellon University, Department of Civil and
    Environmental Engineering
  • Tappero Ryan: Brookhaven National Laboratory, National Synchrotron
    Light Source II
  • Acerbo Alvin: University of Chicago
  • Lowry Gregory: Carnegie Mellon University, Civil and Environmental
    Engineering; Center for Environmental Implications of Nanotechnology

Nanoscale NR-COM-09-2019-008100.R1 –

https://pubs.rsc.org/en/content/getauthorversionpdf/c9nr08100c

Different Stomatal Patterns in Some Selected Plants Using Compound Light Microscopy

Comparative Anatomical Studies of Epidermis with Different Stomatal Patterns in Some Selected Plants Using Compound Light Microscopy

Naeem M., Hussain A., Azmi U. R., Maqsood S., Imtiaz U., Ali H., Rehman S. U.,
Kaleemullah, Munir H. M., Ghani U. (2019)

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International Journal of Scientific and Research Publications 9(10): 375 – ISSN 2250-3153 – http://dx.doi.org/10.29322/IJSRP.9.10.2019.p9449

http://www.ijsrp.org/research-paper-1019/ijsrp-p9449.pdf

A composite image is provided to a trainable feature detector in order to determine a number, density, and/or distribution of stomata in the composite image

Portable field imaging of plant stomata

Hall G. (2019)

Hall Giles,

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INARI AGRICULTURE TECHNOLOGY, INC. USA –

https://patentscope.wipo.int/search/en/detail.jsf?docId=WO2021108522

Abstract

(EN) Examples of the disclosure describe systems and methods for identifying, quantifying, and/or characterizing plant stomata. In an example method, a first set of two or more images of a plant leaf representing two or more focal distances is captured via an optical sensor. A reference focal distance is determined based on the first set of images. A second set of two or more images of the plant leaf is captured via the optical sensor, including at least one image captured at a focal distance less than the reference focal distance, and at least one image captured at a focal distance greater than the reference focal distance. A composite image is generated based on the second set of images. The composite image is provided to a trainable feature detector in order to determine a number, density, and/or distribution of stomata in the composite image.

(FR) Des exemples de la divulgation décrivent des systèmes et des procédés permettant d’identifier, de quantifier et/ou de caractériser des stomates de plantes. Dans un procédé donné à titre d’exemple, un premier ensemble d’au moins deux images d’une feuille de plante représentant au moins deux distances focales est capturé par l’intermédiaire d’un capteur optique. Une distance focale de référence est déterminée sur la base du premier ensemble d’images. Un second ensemble d’au moins deux images de la feuille de plante est capturé par l’intermédiaire du capteur optique, comprenant au moins une image capturée à une distance focale inférieure à la distance focale de référence, et au moins une image capturée à une distance focale supérieure à la distance focale de référence. Une image composite est générée sur la base du second ensemble d’images. L’image composite est fournie à un détecteur de caractéristiques apte à l’apprentissage afin de déterminer un nombre, une densité et/ou une distribution de stomates dans l’image composite.

A method suitable for rapid extraction of stomatal information in plant leaves

Zhu J. Y., Xu C. Y., Wu J. (2018)

Zhu JiYou, Xu ChengYang, Wu Ju,

Key Laboratory for Silviculture and Conservation of Ministry of Education, Key Laboratory for Silviculture and Forest Ecosystem of State Forestry Administration, Beijing Forestry University, Beijing 100083, China.

Fast estimation of stomatal density and stomatal area of plant leaves based on eCognition – Journal of Beijing Forestry University 40(5): 37-45 –

https://www.cabdirect.org/cabdirect/abstract/20183262797

Abstract : 

Objective:

Leaf stomatal is a main channel used as exchange matter between plants and environment, which is very sensitive to environmental changes. How to calculate stomatal area and openness data quickly and accurately still lacks mature methods and techniques. This paper aims to explore the quantitative calculation of leaf stomatal density and stomatal area, and provide reference for future research on plant stomatal by this way.

Method:

This study chose the leaf of Fraxinus pennsylvanicaAilanthus altissima and Sophora japonica as objects, analyzing stomatal information by multi-scale segmentation and classification recognition and classifying the leaf stomatal microscopic images via eCognition image processing software. The stomatal imagines were classified and identified based on the spectral characteristics, brightness characteristics and geometric features of the objects.

Results:

The results showed that the best parameters of the stomatal division and the combination of automatic extraction rules were: scale parameters 120-125, shape parameter 0.7, compactness parameter 0.9, brightness value 160-220, red light band >95, shape-density index 1.5-2.2.

Conclusion:

The precision of stomatal density and stomatal area extracted by this method was 99.2% and 94.5%, respectively and the results were satisfactory. So the method is suitable for rapid extraction of stomatal information in plant leaves.