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StomataHub

StomataHub is a centralised platform for sharing published stomata datasets and research papers, built to accelerate reproducible, data-driven stomatal analysis. A web-based detector and annotation workspace is planned to support interactive, end-to-end workflows.

About stomata

What are stomata? Stomata (singular stoma) refers to the complex consisting of a pair of specialised cells, called guard cells, surrounding a pore on the surface of above-ground plant organs. Through changes in turgor pressure of the guard cells, the pore aperture adjusts to facilitate, or restrict, the exchange of gases between the plant and the atmosphere.

Why are stomata important? Plants respond to signals such as increased light intensity and humidity to increase pore aperture and thus open stomata. Whilst this enables uptake of carbon dioxide required for photosynthesis, water vapour is simultaneously lost. Stomata occupy less than 5% of the leaf epidermal surface, but can account for up to 98% of gas exchange.

Deep learning for the analysis of stomata. Deep learning is particularly effective for image analysis, enabling automated measurement of traits such as stomatal counts or density, area measurements and/or morphology. These methods require annotated datasets for training, and network structure can be adapted to the image analysis task.

Datasets

StomataHub hosts published datasets to support deep learning architectures for stomatal analysis. Annotations range from simple points through to detailed segmentation masks. Each dataset is shared with the permission of the authors, please ensure you include original citations in your work.

Dataset pages include a short summary plus key metadata (e.g., species, imaging modality, and annotation type) to help you choose appropriate training and evaluation data. Where available, we also link directly to the associated paper and recommended citation text.

If you have a dataset you would like to share or have questions about existing datasets, please get in touch here.

Recent papers

Year Title Authors
2025 FieldDino: Rapid In-Field Stomatal Anatomy and Physiology Phenotyping Edward Chaplin; Guy Coleman; Andrew Merchant; William Salter
2025 SCAN: an automated phenotyping tool for real-time capture of leaf stomatal traits in canola Lingtian Yao; Susanne von Caemmerer; Florence R Danila
2025 WSF: A Transformer-Based Framework for Microphenotyping and Genetic Analyzing of Wheat Stomatal Traits Zhou H; Min H; Liang S; Qin B; Sun Q; Pei Z; Pan Q; Wang ...
2025 Advanced phenotyping features utilizing deep learning techniques for automated analysis of stomatal guard cell orientation Thanh Tuan Thai; Sheikh Mansoor; Hoang Thien Van; Van Gia...
2025 A stomata imaging and segmentation pipeline incorporating generative AI to reduce dependency on manual groundtruthing Changye Yang; Huajin Sheng; Kevin T Kolbinson; Hamid Shat...
2025 Stomata morphology measurement with interactive machine learning: accuracy, speed, and biological relevance? Tomke S Wacker; Abraham G Smith; Signe M Jensen; Theresa ...