ArticlePlant phenomics (Washington, D.C.)2023
Rotating Stomata Measurement Based on Anchor-Free Object Detection and Stomata Conductance Calculation.
Article in Plant phenomics (Washington, D.C.), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
5 citing papers in PubMed.
- Intermolecular-Interaction-Driven Adaptive Remodeling: A Network Perspective on Plant Abiotic Stress Responses.Plants (Basel, Switzerland) · 2026Review
- Automated stomatal traits measurement in melon (Cucumis melo L.) based on vision transformers with dynamically composable multi-head attention.Plant methods · 2026Article
- Advanced phenotyping features utilizing deep learning techniques for automated analysis of stomatal guard cell orientation.Scientific reports · 2025Article
- Stoma Detection in Soybean Leaves and Rust Resistance Analysis.Plants (Basel, Switzerland) · 2025Article
- Application of deep learning for the analysis of stomata: a review of current methods and future directions.Journal of experimental botany · 2024Review
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Stomata play an essential role in regulating water and carbon dioxide levels in plant leaves, which is important for photosynthesis. Previous deep learning-based plant stomata detection methods are based on horizontal detection. The detection anchor boxes of deep learning model are horizontal, while the angle of stomata is randomized, so it is not possible to calculate stomata traits directly from the detection anchor boxes. Additional processing of image (e.g., rotating image) is required before detecting stomata and calculating stomata traits. This paper proposes a novel approach, named DeepRSD (deep learning-based rotating stomata detection), for detecting rotating stomata and calculating stomata basic traits at the same time. Simultaneously, the stomata conductance loss function is introduced in the DeepRSD model training, which improves the efficiency of stomata detection and conductance calculation. The experimental results demonstrate that the DeepRSD model reaches 94.3% recognition accuracy for stomata of maize leaf. The proposed method can help researchers conduct large-scale studies on stomata morphology, structure, and stomata conductance models.
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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.