ArticleFrontiers in plant science2020
Accelerating Automated Stomata Analysis Through Simplified Sample Collection and Imaging Techniques.
Article in Frontiers in plant science, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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Who cites it
22 citing papers in PubMed, 45 citations in OpenAlex.
- UMF-stomata: An unsupervised multi-focus fusion framework for microscopic stomatal phenotyping.Plant phenomics (Washington, D.C.) · 2026Article
- FieldDino: Rapid In-Field Stomatal Anatomy and Physiology Phenotyping.Plant, cell & environment · 2026Article
- Genetic markers of stomatal cluster development in Begoniaceae revealed through trait analysis assisted by interactive deep-learning.Plant physiology · 2026Article
- Automated stomatal traits measurement in melon (Cucumis melo L.) based on vision transformers with dynamically composable multi-head attention.Plant methods · 2026Article
- Mitigation of arsenic and crude oil stress in tomato plants through microbial modulation of physiological and molecular responses.BMC plant biology · 2026Article
- Stomatal Features, Specific Leaf Area and Water Relations in Three Pistachio Cultivars.Plants (Basel, Switzerland) · 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
- Article
- The combined effect of decreased stomatal density and aperture increases water use efficiency in maize.Scientific reports · 2025Article
- Application of deep learning for the analysis of stomata: a review of current methods and future directions.Journal of experimental botany · 2024Review
- Animal family discrimination from hair using ATR-FTIR and machine learning methods for applications in illegal wildlife trafficking.Die Naturwissenschaften · 2024Article
- Plant microphenotype: from innovative imaging to computational analysis.Plant biotechnology journal · 2024Review
- Article
- Article
- Rapid non-destructive method to phenotype stomatal traits.Plant methods · 2023Article
- Rotating Stomata Measurement Based on Anchor-Free Object Detection and Stomata Conductance Calculation.Plant phenomics (Washington, D.C.) · 2023Article
- Stomatal pore width and area measurements inmicroPublication biology · 2023Article
- StomataScorer: a portable and high-throughput leaf stomata trait scorer combined with deep learning and an improved CV model.Plant biotechnology journal · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Digital image processing is commonly used in plant health and growth analysis, aiming to improve research efficiency and repeatability. One focus is analysing the morphology of stomata, with the aim to better understand the regulation of gas exchange, its link to photosynthesis and water use and how they are influenced by climatic conditions. Despite the key role played by these cells, their microscopic analysis is largely manual, requiring intricate sample collection, laborious microscope application and the manual operation of a graphical user interface to identify and measure stomata. This research proposes a simple, end-to-end solution which enables automatic analysis of stomata by introducing key changes to imaging techniques, stomata detection as well as stomatal pore area calculation. An optimal procedure was developed for sample collection and imaging by investigating the suitability of using an automatic microscope slide scanner to image nail polish imprints. The use of the slide scanner allows the rapid collection of high-quality images from entire samples with minimal manual effort. A convolutional neural network was used to automatically detect stomata in the input image, achieving average precision, recall and F-score values of 0.79, 0.85, and 0.82 across four plant species. A novel binary segmentation and stomatal cross section analysis method is developed to estimate the pore boundary and calculate the associated area. The pore estimation algorithm correctly identifies stomata pores 73.72% of the time. Ultimately, this research presents a fast and simplified method of stomatal assay generation requiring minimal human intervention, enhancing the speed of acquiring plant health information.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.