ArticlePloS one2021
A stomata classification and detection system in microscope images of maize cultivars.
Article in PloS one, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 15 papers.
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15 citing papers in PubMed, 31 citations in OpenAlex.
- Genetic markers of stomatal cluster development in Begoniaceae revealed through trait analysis assisted by interactive deep-learning.Plant physiology · 2026Article
- SCAN: an automated phenotyping tool for real-time capture of leaf stomatal traits in canola.Journal of experimental botany · 2025Article
- StomaYOLO: A Lightweight Maize Phenotypic Stomatal Cell Detector Based on Multi-Task Training.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
- 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
- Correction: A stomata classification and detection system in microscope images of maize cultivars.PloS one · 2024Article
- Article
- No cell is an island: characterising the leaf epidermis using epidermalmorph, a new R package.The New phytologist · 2023Article
- StomataScorer: a portable and high-throughput leaf stomata trait scorer combined with deep learning and an improved CV model.Plant biotechnology journal · 2022Article
- Optical topometry and machine learning to rapidly phenotype stomatal patterning traits for maize QTL mapping.Plant physiology · 2021Article
- Automated stomata detection in oil palm with convolutional neural network.Scientific reports · 2021Article
- A Deep Learning-Based Method for Automatic Assessment of Stomatal Index in Wheat Microscopic Images of Leaf Epidermis.Frontiers in plant science · 2021Article
- A large-scale optical microscopy image dataset of potato tuber for deep learning based plant cell assessment.Scientific data · 2020Article
- CuticleTrace: A toolkit for capturing cell outlines from leaf cuticle with implications for paleoecology and paleoclimatology.Applications in plant sciencesArticle
Corrections and comments
- Erratum issued
Authors and funding
7 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Plant stomata are essential structures (pores) that control the exchange of gases between plant leaves and the atmosphere, and also they influence plant adaptation to climate through photosynthesis and transpiration stream. Many works in literature aim for a better understanding of these structures and their role in the evolution process and the behavior of plants. Although stomata studies in dicots species have advanced considerably in the past years, even there is not much knowledge about the stomata of cereal grasses. Due to the high morphological variation of stomata traits intra- and inter-species, detecting and classifying stomata automatically becomes challenging. For this reason, in this work, we propose a new system for automatic stomata classification and detection in microscope images for maize cultivars based on transfer learning strategy of different deep convolution neural netwoks (DCNN). Our performed experiments show that our system achieves an approximated accuracy of 97.1% in identifying stomata regions using classifiers based on deep learning features, which figures out as a nearly perfect classification system. As the stomata are responsible for several plant functionalities, this work represents an important advance for maize research, providing an accurate system in replacing the current manual task of categorizing these pores on microscope images. Furthermore, this system can also be a reference for studies using images from different cereal grasses.
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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.