Photo’s of stomata

Microscopy of Nature

Vossen R. (xxxx)


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.


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
  • 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 –

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)


International Journal of Scientific and Research Publications 9(10): 375 – ISSN 2250-3153 –

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,




(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 –

Abstract : 


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.


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.


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.


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.

Measure stomatal aperture by MSER on smart mobile phone

A fast method to measure stomatal aperture by MSER on smart mobile phone

Liu S., Tang J., Petrie P., Whitty M. (2016)

Scarlett Liu, Julie Tang, Paul Petrie, Mark Whitty,

In: Imaging and applied optics congress 2016: 3–5 – OSA Technical Digest (online) (Optica Publishing Group, 2016), paper AIW2B.2.


A fast image processing method is proposed for detecting stomata and measuring stomatal aperture size in individual images. The accuracy of aperture measurements is 97%. A prototype mobile application is developed to assist field measurements.

© 2016 Optical Society of America

Identification of Plant Stomata

Identification of Plant Stomata Based on YOLO v5 Deep Learning Model

Ren F., Zhang Y., Liu X., Zhang Y., Liu Y., Zhang F. (2021)

Fangtao RenYawei ZhangXi Liu, Yingqi Zhang,  Ying LiuFan Zhang,


CSAI 2021: 2021 5th International Conference on Computer Science and Artificial Intelligence December 2021: 78–83 –


Stomata is an important structure in all terrestrial plants and is very vital in controlling plant photosynthesis and transpiration flow. Precise detection of plant stomata is the basis for studying stomata characteristics. Traditional detection methods are mostly manual operations, which is a tedious and inefficient process. Manually extracting features requires high image quality. Choosing appropriate features depends on certain prior knowledge, especially for the object with large morphological changes such as plant stomata. With the widespread use of deep learning technology, efficient solutions to this task have become possible. This article combines the characteristics of the corn leaf stomatal data sets to improve the latest object detection model YOLO v5)You Only Look Once(. By introducing the attention mechanism, that is, adding the SE module to the backbone network, the precision and recall of stoma detection are improved. At the same time, The loss function has been improved from to for avoiding some problems that may occur when selecting the best prediction box. Experimental results show that the precision and recall rates of the improved model on the corn leaf stomata data sets have reached 94.8% and 98.7% respectively, lay the foundation for the measurement of stomatal parameters. In addition, this paper also can help agriculturists and botanists to build their own data sets for stomatal research by explaining the methods of acquiring, pre-processing, and annotating data sets.

Stomata opening is a crucial part of the plant development process. There are well-measured metrics that are an excellent proxy of the phenomenon. One of such measures is stomatal conductivity which can be measured with a Sigrow stomata camera.

Using Sigrow Stomata Camera to monitor plant health – Discover the importance of monitoring plant stomatal conductance and read about the insights we got from the stomata camera

Digital Latest News/Quantum (2022)


Greenhouses are an essential part of the modern food supply chain, and their efficiency is a matter of growing concern for growers. Automating indoor climate parameters adjustment is a way to increase the effectiveness of greenhouses and reduce costs. Sounds plain and easy in theory. In practice, however, it gets complicated fast.

Wageningen University & Research (WUR) hosts the Autonomous Greenhouse Challenge, a competition for professional growers, programmers, data scientists, and whoever is passionate about the idea of a challenge. Our data scientists, of course, also participated in it.

Stomata is basically a pore in the plant’s “skin,” which allows the plant to exchange gasses with an atmosphere. Photosynthesis, a process of converting sunlight energy and gasses into sugars, would not be possible without such exchange. Green inhabitants of our planet do not have these pores open all the time. However: whenever conditions are suitable for photosynthesis, the stomata of a plant is open; otherwise, they prefer not to give out water into the atmosphere, and photosynthesis ceases.

Maximizing photosynthesis must be the core objective if growers want plants to grow healthy and produce a good quality product. Monitoring stomatal opening is complicated in its purest form since pores are incredibly tiny. Stomatal conductance, however, is the best indicator of pores opening.

Stomatal conductance is the exchange rate of carbon dioxide and water vapor of a particular surface. Obviously, the higher the rate, the more actively photosynthesis is occurring. The catch is two-fold here: firstly, such a rate is never precisely zero, and the maximum possible value is usually far from theoretical; secondly, it tells nothing about which part of the environment the plant is uncomfortable with, whether it is too cold or too hot, low carbon dioxide level or high humidity. This is where a grower’s knowledge might be of great help.

Image processing is a key component of many autonomous systems. Whether it is a self-driving car or a greenhouse, a great deal of information can be gained from images. Organizers of the Autonomous Greenhouse Challenge knew it and took great care of cameras and sensors setup for participating teams.

RGB and Sigrow cameras data overview