ArticleFrontiers in plant science2021
A Deep Learning-Based Method for Automatic Assessment of Stomatal Index in Wheat Microscopic Images of Leaf Epidermis.
Article in Frontiers in plant science, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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14 citing papers in PubMed, 35 citations in OpenAlex.
- Stomatalia: a deep learning-based platform for quantitative stomata and pavement cell analysis.Plant methods · 2026Article
- Automated stomatal traits measurement in melon (Cucumis melo L.) based on vision transformers with dynamically composable multi-head attention.Plant methods · 2026Article
- Technological advances in imaging and modelling of leaf structural traits: a review of heat stress in wheat.Journal of experimental botany · 2026Review
- WSF: A Transformer-Based Framework for Microphenotyping and Genetic Analyzing of Wheat Stomatal Traits.Plants (Basel, Switzerland) · 2025Article
- Physiological Efficiency and Adaptability of Greek Indigenous Grapevine Cultivars Under Heat Stress and Elevated COPlants (Basel, Switzerland) · 2025Article
- Elevated COThe New phytologist · 2025Article
- Application of deep learning for the analysis of stomata: a review of current methods and future directions.Journal of experimental botany · 2024Review
- Machine learning-enabled computer vision for plant phenotyping: a primer on AI/ML and a case study on stomatal patterning.Journal of experimental botany · 2024Review
- Plant microphenotype: from innovative imaging to computational analysis.Plant biotechnology journal · 2024Review
- StripeRust-Pocket: A Mobile-Based Deep Learning Application for Efficient Disease Severity Assessment of Wheat Stripe Rust.Plant phenomics (Washington, D.C.) · 2024Article
- Drought-Alleviating Effects of Endophytic Bacteria Isolated from Xerophytic Plants on Capsicum annuum L. Seedlings.Current microbiology · 2023Article
- JustDeepIt: Software tool with graphical and character user interfaces for deep learning-based object detection and segmentation in image analysis.Frontiers in plant science · 2022Article
- Cotton Breeding in Australia: Meeting the Challenges of the 21st Century.Frontiers in plant science · 2022Review
- A Deep Learning Method for Fully Automatic Stomatal Morphometry and Maximal Conductance Estimation.Frontiers in plant science · 2021Article
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Authors and funding
9 authors at 2 institutions in 1 country.
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No grant is acknowledged in the PubMed record.
Abstract
The stomatal index of the leaf is the ratio of the number of stomata to the total number of stomata and epidermal cells. Comparing with the stomatal density, the stomatal index is relatively constant in environmental conditions and the age of the leaf and, therefore, of diagnostic characteristics for a given genotype or species. Traditional assessment methods involve manual counting of the number of stomata and epidermal cells in microphotographs, which is labor-intensive and time-consuming. Although several automatic measurement algorithms of stomatal density have been proposed, no stomatal index pipelines are currently available. The main aim of this research is to develop an automated stomatal index measurement pipeline. The proposed method employed Faster regions with convolutional neural networks (R-CNN) and U-Net and image-processing techniques to count stomata and epidermal cells, and subsequently calculate the stomatal index. To improve the labeling speed, a semi-automatic strategy was employed for epidermal cell annotation in each micrograph. Benchmarking the pipeline on 1,000 microscopic images of leaf epidermis in the wheat dataset (
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