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Related Concept Videos

Regulation of Transpiration by Stomata

Regulation of Transpiration by Stomata

During photosynthesis, plants acquire the necessary carbon dioxide and release the produced oxygen back into the atmosphere. Openings in the epidermis of plant leaves is the site of this exchange of gasses. A single opening is called a stoma—derived from the Greek word for “mouth.” Stomata open and close in response to a variety of environmental cues.

Light Acquisition

Light Acquisition

In order to produce glucose, plants need to capture sufficient light energy. Many modern plants have evolved leaves specialized for light acquisition. Leaves can be only millimeters in width or tens of meters wide, depending on the environment. Due to competition for sunlight, evolution has driven the evolution of increasingly larger leaves and taller plants, to avoid shading by their neighbors with contaminant elaboration of root architecture and mechanisms to transport water and nutrients.

C4 Pathway and CAM

C4 Pathway and CAM

Most plants use the C3 pathway for carbon fixation. However, some plants, such as sugar cane, corn, and cacti that grow in hot conditions, use alternative pathways to fix carbon and conserve energy loss due to photorespiration. Photorespiration is the process that occurs when the oxygen concentration is high. Under such conditions, the rubisco enzyme in the Calvin cycle binds O2 instead of CO2, which halts photosynthesis and consumes energy.
C4 Pathway
The C4 pathway is used by plants such as...

Adaptations that Reduce Water Loss

Adaptations that Reduce Water Loss

Though evaporation from plant leaves drives transpiration, it also results in loss of water. Because water is critical for photosynthetic reactions and other cellular processes, evolutionary pressures on plants in different environments have driven the acquisition of adaptations that reduce water loss.

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Related Experiment Video

Updated: Jul 9, 2026

Direct Observation and Automated Measurement of Stomatal Responses to Pseudomonas syringae pv. tomato DC3000 in Arabidopsis thaliana

Direct Observation and Automated Measurement of Stomatal Responses to Pseudomonas syringae pv. tomato DC3000 in Arabidopsis thaliana

Published on: February 9, 2024

Stomatalia: a deep learning-based platform for quantitative stomata and pavement cell analysis.

Dan Jeric Arcega Rustia¹, Marcello Gazale², Micha Gracianna Devi²

¹Greenhouse Horticulture, Wageningen Plant Research, Wageningen University & Research, Droevendalsesteeg 1, 6708PB, Wageningen, The Netherlands. dan.rustia@wur.nl.

|July 7, 2026

Summary

Stomatalia, a new deep learning platform, automates the analysis of leaf epidermal cells, enabling high-throughput phenotyping for plant research. This tool quantifies stomatal and pavement cell traits, aiding in developing climate-resilient crops.

Keywords:

Deep learning Digital phenotyping Pavement cells Stomata

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Identification of the Genes Involved in Stomatal Development via Epidermal Phenotype Scoring

Identification of the Genes Involved in Stomatal Development via Epidermal Phenotype Scoring

Published on: January 20, 2023

Related Experiment Videos

Last Updated: Jul 9, 2026

Direct Observation and Automated Measurement of Stomatal Responses to Pseudomonas syringae pv. tomato DC3000 in Arabidopsis thaliana

Direct Observation and Automated Measurement of Stomatal Responses to Pseudomonas syringae pv. tomato DC3000 in Arabidopsis thaliana

Published on: February 9, 2024

Identification of the Genes Involved in Stomatal Development via Epidermal Phenotype Scoring

Identification of the Genes Involved in Stomatal Development via Epidermal Phenotype Scoring

Published on: January 20, 2023

Area of Science:

Plant biology and crop science
Computational biology and bioinformatics
Agricultural technology

Background:

Stomata and pavement cells are crucial for plant gas exchange, water loss, and leaf growth.
Current methods for quantifying epidermal traits are slow and not easily scalable for large-scale phenotyping.
Limitations hinder the integration of epidermal morphology into crop improvement for climate resilience.

Purpose of the Study:

To develop Stomatalia, a deep learning platform for automated and standardized quantification of stomatal and pavement cell traits.
To enable high-throughput phenotyping of leaf epidermal morphology.
To support crop improvement pipelines for climate-resilient varieties.

Main Methods:

Developed a rapid, minimally-destructive leaf-printing protocol.
Utilized an instance-segmentation deep learning algorithm within a user-friendly web interface.
Trained the algorithm on epidermal images of potato and tomato genotypes under varying conditions.

Main Results:

Stomatalia achieved high performance (F1-scores 0.86-0.94) in dicot species for stomatal and pavement cell segmentation.
Demonstrated generality in stomatal detection across several species, with variable performance in monocots.
Showcased faster processing times and closer agreement with manual counts compared to another public application.

Conclusions:

Stomatalia is a robust, user-friendly platform for automated, high-throughput analysis of leaf epidermal images.
The platform facilitates rapid, reproducible phenotyping of stomatal and pavement cells.
Its current strengths lie in dicot leaf-print analysis, with potential for expansion to other species through further model retraining.