ArticlePlant methods2023
Rapid non-destructive method to phenotype stomatal traits.
Article in Plant methods, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers.
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Who cites it
27 citing papers in PubMed.
- FieldDino: Rapid In-Field Stomatal Anatomy and Physiology Phenotyping.Plant, cell & environment · 2026Article
- Green revolution breeding favored water conservation, but weakened water use sensitivity to rising vapor pressure deficit in US spring wheat.Plant physiology · 2026Article
- Technological advances in imaging and modelling of leaf structural traits: a review of heat stress in wheat.Journal of experimental botany · 2026Review
- The Small Auxin Upregulated RNAPlants (Basel, Switzerland) · 2026Article
- Plasticity of source-sink dynamics contributes to wheat yield stability.Nature communications · 2026Article
- Intraspecific variation in stomatal architecture, gas exchange, and drought response of a dominant prairie grass sourced from broad climatic gradients.American journal of botany · 2026Article
- Drought adaptation in spring wheat seedlings relies on coordinated deep root architecture and cortical tissue allocation.Frontiers in plant science · 2026Article
- Leaf gas films enhance metabolic responses to submergence inFrontiers in plant science · 2026Article
- Divergent physiological strategies distinguish tolerant and plastic genotypes in elite Australian rice lines under limited irrigation.Frontiers in plant science · 2026Article
- QTLs for heat-induced stomatal anatomy underpin gas exchange variation in field-grown wheat.Frontiers in plant science · 2026Article
- A stomata imaging and segmentation pipeline incorporating generative AI to reduce dependency on manual groundtruthing.Plant methods · 2025Article
- SCAN: an automated phenotyping tool for real-time capture of leaf stomatal traits in canola.Journal of experimental botany · 2025Article
- Smarter stomata: emergent technologies unlocking yield potential in a changing climate.AoB PLANTS · 2025Review
- WSF: A Transformer-Based Framework for Microphenotyping and Genetic Analyzing of Wheat Stomatal Traits.Plants (Basel, Switzerland) · 2025Article
- Stoma Detection in Soybean Leaves and Rust Resistance Analysis.Plants (Basel, Switzerland) · 2025Article
- Stomata morphology measurement with interactive machine learning: accuracy, speed, and biological relevance?Plant methods · 2025Article
- The combined effect of decreased stomatal density and aperture increases water use efficiency in maize.Scientific reports · 2025Article
- Phenotypic adaptation and genomic variation ofFrontiers in plant science · 2025Article
- Effects of ploidy level on leaf morphology, stomata, and anatomical structure of Hibiscus syriacus L.BMC plant biology · 2024Article
- Plant Microbe Interaction-Predicting the Pathogen Internalization Through Stomata Using Computational Neural Network Modeling.Foods (Basel, Switzerland) · 2024Article
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Authors and funding
4 authors.
Funding
Abstract
backgroundStomata are tiny pores on the leaf surface that are central to gas exchange. Stomatal number, size and aperture are key determinants of plant transpiration and photosynthesis, and variation in these traits can affect plant growth and productivity. Current methods to screen for stomatal phenotypes are tedious and not high throughput. This impedes research on stomatal biology and hinders efforts to develop resilient crops with optimised stomatal patterning. We have developed a rapid non-destructive method to phenotype stomatal traits in three crop species: wheat, rice and tomato.
resultsThe method consists of two steps. The first is the non-destructive capture of images of the leaf surface from plants in their growing environment using a handheld microscope; a process that only takes a few seconds compared to minutes for other methods. The second is to analyse stomatal features using a machine learning model that automatically detects, counts and measures stomatal number, size and aperture. The accuracy of the machine learning model in detecting stomata ranged from 88 to 99%, depending on the species, with a high correlation between measures of number, size and aperture using the machine learning models and by measuring them manually. The rapid method was applied to quickly identify contrasting stomatal phenotypes.
conclusionsWe 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 and analysis. It can be readily used to phenotype stomata in large populations in the field and in controlled environments.
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