ArticleThe Plant cell2022
LeafNet: a tool for segmenting and quantifying stomata and pavement cells.
Article in The Plant cell, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
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24 citing papers in PubMed, 41 citations in OpenAlex.
- Stomatalia: a deep learning-based platform for quantitative stomata and pavement cell analysis.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
- Climate and hybridization shape stomatal trait evolution in Populus.The New phytologist · 2026Article
- Digitizing microscope slide-based natural history collections: A protocol using slide scanner technology.PloS one · 2026Article
- DFA-YOLO: an enhanced YOLOv11-OBB and knowledge distillation-based maize stomata detection system.Frontiers in plant science · 2026Article
- A user-friendly machine-learning program to quantify stomatal features from fluorescence images.bioRxiv : the preprint server for biology · 2025Article
- Stoma Detection in Soybean Leaves and Rust Resistance Analysis.Plants (Basel, Switzerland) · 2025Article
- Growing connections: PlantConnectome maps molecular networks in Arabidopsis.The Plant cell · 2025Article
- Innovative multi-scale approach to study the phenotypic variation of seedling leaves in four weedy Amaranthus species.Plant biology (Stuttgart, Germany) · 2025Article
- Using the Montgomery-Koyama-Smith equation to calculate the stomatal area per unit lamina area for 12 Magnoliaceae species.Annals of botany · 2024Article
- 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
- Characterization of the cryptic interspecific hybrid Lemna×mediterranea by an integrated approach provides new insights into duckweed diversity.Journal of experimental botany · 2024Article
- Plant microphenotype: from innovative imaging to computational analysis.Plant biotechnology journal · 2024Review
- Significant correlation between leaf vein length per unit area and stomatal density: evidence from Red Tip and Chinese photinias.Frontiers in plant science · 2024Article
- Analysis of stomatal characteristics of maize hybrids and their parental inbred lines during critical reproductive periods.Frontiers in plant science · 2024Article
- Plantorganelle Hunter is an effective deep-learning-based method for plant organelle phenotyping in electron microscopy.Nature plants · 2023Article
- No cell is an island: characterising the leaf epidermis using epidermalmorph, a new R package.The New phytologist · 2023Article
- A simple and efficient method to quantify the cell parameters of the seed coat, embryo and silique wall in rapeseed.Plant methods · 2022Article
- Advanced Image Analysis Methods for Automated Segmentation of Subnuclear Chromatin Domains.Epigenomes · 2022Article
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Authors and funding
10 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Stomata play important roles in gas and water exchange in leaves. The morphological features of stomata and pavement cells are highly plastic and are regulated during development. However, it is very laborious and time-consuming to collect accurate quantitative data from the leaf surface by manual phenotyping. Here, we introduce LeafNet, a tool that automatically localizes stomata, segments pavement cells (to prepare them for quantification), and reports multiple morphological parameters for a variety of leaf epidermal images, especially bright-field microscopy images. LeafNet employs a hierarchical strategy to identify stomata using a deep convolutional network and then segments pavement cells on stomata-masked images using a region merging method. LeafNet achieved promising performance on test images for quantifying different phenotypes of individual stomata and pavement cells compared with six currently available tools, including StomataCounter, Cellpose, PlantSeg, and PaCeQuant. LeafNet shows great flexibility, and we improved its ability to analyze bright-field images from a broad range of species as well as confocal images using transfer learning. Large-scale images of leaves can be efficiently processed in batch mode and interactively inspected with a graphic user interface or a web server (https://leafnet.whu.edu.cn/). The functionalities of LeafNet could easily be extended and will enhance the efficiency and productivity of leaf phenotyping for many plant biologists.
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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.