ArticleScientific reports2019
Genetic Diversity in Stomatal Density among Soybeans Elucidated Using High-throughput Technique Based on an Algorithm for Object Detection.
Article in Scientific reports, 2019. 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.
- Candidate genes with antagonistic roles in stomatal development are associated with population-wide variation in apple.Horticulture research · 2026Article
- Photosynthesis-informed breeding to enhance Soybean (Glycine max) yield potential: a framework integrating high-throughput phenomics, genomic prediction and G×E×M.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2026Review
- SCAN: an automated phenotyping tool for real-time capture of leaf stomatal traits in canola.Journal of experimental botany · 2025Article
- Advanced phenotyping features utilizing deep learning techniques for automated analysis of stomatal guard cell orientation.Scientific reports · 2025Article
- Stomata morphology measurement with interactive machine learning: accuracy, speed, and biological relevance?Plant methods · 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
- A continental-scale analysis reveals the latitudinal gradient of stomatal density across amphistomatous species: evolutionary history vs. present-day environment.Annals of botany · 2024Article
- Plant microphenotype: from innovative imaging to computational analysis.Plant biotechnology journal · 2024Review
- Performance and morphology of several soybean varieties and responses to pests and diseases in South Sulawesi.Heliyon · 2024Article
- Water deficit differentially modulates leaf photosynthesis and transpiration of fungus-tolerantFrontiers in plant science · 2024Article
- Rotating Stomata Measurement Based on Anchor-Free Object Detection and Stomata Conductance Calculation.Plant phenomics (Washington, D.C.) · 2023Article
- Differential regulation of flower transpiration during abiotic stress in annual plants.The New phytologist · 2022Article
- StomataScorer: a portable and high-throughput leaf stomata trait scorer combined with deep learning and an improved CV model.Plant biotechnology journal · 2022Article
- Optimizing the Experimental Method for Stomata-Profiling Automation of Soybean Leaves Based on Deep Learning.Plants (Basel, Switzerland) · 2021Article
- Machine learning-enabled phenotyping for GWAS and TWAS of WUE traits in 869 field-grown sorghum accessions.Plant physiology · 2021Article
- Optical topometry and machine learning to rapidly phenotype stomatal patterning traits for maize QTL mapping.Plant physiology · 2021Article
- Classical phenotyping and deep learning concur on genetic control of stomatal density and area in sorghum.Plant physiology · 2021Article
- A generalised approach for high-throughput instance segmentation of stomata in microscope images.Plant methods · 2021Article
- An Affordable Image-Analysis Platform to Accelerate Stomatal Phenotyping During Microscopic Observation.Frontiers in plant science · 2021Article
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
The stomatal density (SD) can be a promising target to improve the leaf photosynthesis in soybeans (Glycine max (L.) Merr). In a conventional SD evaluation, the counting process of the stomata during a manual operation can be time-consuming. We aimed to develop a high-throughput technique for evaluating the SD and elucidating the variation in the SD among various soybean accessions. The central leaflet of the first trifoliolate was sampled, and microscopic images of the leaflet replica were obtained among 90 soybean accessions. The Single Shot MultiBox Detector, an algorithm for an object detection based on deep learning, was introduced to develop an automatic detector of the stomata in the image. The developed detector successfully recognized the stomata in the microscopic image with high-throughput. Using this technique, the value of R
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