ArticleEcology and evolution2020
From leaf to label: A robust automated workflow for stomata detection.
Article in Ecology and evolution, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed, 40 citations in OpenAlex.
- FieldDino: Rapid In-Field Stomatal Anatomy and Physiology Phenotyping.Plant, cell & environment · 2026Article
- WSF: A Transformer-Based Framework for Microphenotyping and Genetic Analyzing of Wheat Stomatal Traits.Plants (Basel, Switzerland) · 2025Article
- 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
- Application of deep learning for the analysis of stomata: a review of current methods and future directions.Journal of experimental botany · 2024Review
- Leaf functional trait evolution and its putative climatic drivers in African Coffea species.Annals of botany · 2024Article
- Plant microphenotype: from innovative imaging to computational analysis.Plant biotechnology journal · 2024Review
- Rapid non-destructive method to phenotype stomatal traits.Plant methods · 2023Article
- Envisaging a global infrastructure to exploit the potential of digitised collections.Biodiversity data journal · 2023Article
- Rotating Stomata Measurement Based on Anchor-Free Object Detection and Stomata Conductance Calculation.Plant phenomics (Washington, D.C.) · 2023Article
- Article
- StomataScorer: a portable and high-throughput leaf stomata trait scorer combined with deep learning and an improved CV model.Plant biotechnology journal · 2022Article
- HairNet: a deep learning model to score leaf hairiness, a key phenotype for cotton fibre yield, value and insect resistance.Plant methods · 2022Article
- A Deep Learning Method for Fully Automatic Stomatal Morphometry and Maximal Conductance Estimation.Frontiers in plant science · 2021Article
- Stomatal Arrangement Pattern: A New Direction to Explore Plant Adaptation and Evolution.Frontiers in plant science · 2021Article
- From leaf to label: A robust automated workflow for stomata detection.Ecology and evolution · 2020Article
Corrections and comments
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Authors and funding
3 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Plant leaf stomata are the gatekeepers of the atmosphere-plant interface and are essential building blocks of land surface models as they control transpiration and photosynthesis. Although more stomatal trait data are needed to significantly reduce the error in these model predictions, recording these traits is time-consuming, and no standardized protocol is currently available. Some attempts were made to automate stomatal detection from photomicrographs; however, these approaches have the disadvantage of using classic image processing or targeting a narrow taxonomic entity which makes these technologies less robust and generalizable to other plant species. We propose an easy-to-use and adaptable workflow from leaf to label. A methodology for automatic stomata detection was developed using deep neural networks according to the state of the art and its applicability demonstrated across the phylogeny of the angiosperms.We used a patch-based approach for training/tuning three different deep learning architectures. For training, we used 431 micrographs taken from leaf prints made according to the nail polish method from herbarium specimens of 19 species. The best-performing architecture was tested on 595 images of 16 additional species spread across the angiosperm phylogeny.The nail polish method was successfully applied in 78% of the species sampled here. The VGG19 architecture slightly outperformed the basic shallow and deep architectures, with a confidence threshold equal to 0.7 resulting in an optimal trade-off between precision and recall. Applying this threshold, the VGG19 architecture obtained an average
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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.