Datasets

Stomata hub hosts published datasets to support the creation of deep learning architectures for the analysis of stomata. We hope this will continue to grow as researchers from other institutions reach out to provide their own datasets and form collaborations. The existing datasets hosted here come with annotations ranging from simple points through to detailed segmentation masks. Each is aimed at providing valuable training and testing data for machine learning and computer vision applications, including classification, object localisation, and segmentation. If you have a dataset you would like to share or have any questions about existing datasets please do get in touch here.

All available datasets have been shared with the permission of the authors. Please ensure you include original citations in your work. Those that are currently unavailable are either still waiting permission from the original authors, or have had the request for permission to share rejected.

Please note: All hosted datasets have been pre-processed using Contrast Limited Adaptive Histogram Equalisation (CLAHE) to remove colour biases and improve the contrast, and a conversion of annotation files to a generic xml format, again supporting more generic applications. More details on the format can be found here. If you require the annotations converting to a different format, please get in touch and we can arrange to convert these.

Poplar (Gibbs et al,. 2021)

Plant(s)
Poplar
Annotation
Bounding Box
Annotated
Stomata, Pore
Quantity
113
Size
217
Download
Available here (Total downloads: 8)
Gibbs J. A., McAusland L., Zazueta C. R., Murchie E. H., Burgess A.J. (2021). A deep learning method for fully automatic stomatal morphometry and maximal conductance estimation. Frontiers in Plant Science, Technical Advances in Plant Science.
View here

Wheat (Gibbs et al,. 2021)

Plant(s)
Wheat (Triticum aestivum)
Annotation
Bounding Box
Annotated
Stomata, Pore
Quantity
349
Size
913
Download
Available here (Total downloads: 6)
Gibbs J. A., McAusland L., Zazueta C. R., Murchie E. H., Burgess A.J. (2021). A deep learning method for fully automatic stomatal morphometry and maximal conductance estimation. Frontiers in Plant Science, Technical Advances in Plant Science.
View here

LabelStoma

Plant(s)
Soybean, common bean, barley
Annotation
Bounding Box
Annotated
Stomata
Quantity
1800
Size
1
Download
Available here (Total downloads: 7)
Casado-García, A., del-Canto, A., Sanz-Saez, A., Pérez-López, U., Bilbao-Kareaga, A., Fritschi, F.B., Miranda-Apodaca, J., Muñoz-Rueda, A., Sillero-Martínez, A., Yoldi-Achalandabaso, A. and Lacuesta, M., 2020. LabelStoma: A tool for stomata detection based on the YOLO algorithm. Computers and Electronics in Agriculture, 178, p.105751
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Oil Palm

Plant(s)
Oil Palm
Annotation
Bounding Box
Annotated
Stomata
Quantity
321
Size
48
Download
Available here (Total downloads: 6)
Kwong, Q.B., Wong, Y.C., Lee, P.L., Sahaini, M.S., Kon, Y.T., Kulaveerasingam, H. and Appleton, D.R., 2021. Automated stomata detection in oil palm with convolutional neural network. Scientific reports, 11(1), p.15210.
View here

StomataScorer (ProScope)

Plant(s)
Maize
Annotation
Bounding Box
Annotated
Stomata
Quantity
2000
Size
278
Download
Unavailable
Liang, X., Xu, X., Wang, Z., He, L., Zhang, K., Liang, B., Ye, J., Shi, J., Wu, X., Dai, M. and Yang, W., 2022. StomataScorer: a portable and high‐throughput leaf stomata trait scorer combined with deep learning and an improved CV model. Plant biotechnology journal, 20(3), pp.577-591.
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StomataScorer (TipScope)

Plant(s)
Maize
Annotation
Bounding Box
Annotated
Stomata
Quantity
500
Size
298
Download
Unavailable
Liang, X., Xu, X., Wang, Z., He, L., Zhang, K., Liang, B., Ye, J., Shi, J., Wu, X., Dai, M. and Yang, W., 2022. StomataScorer: a portable and high‐throughput leaf stomata trait scorer combined with deep learning and an improved CV model. Plant biotechnology journal, 20(3), pp.577-591.
View here

StomataTracker

Plant(s)
Wheat
Annotation
Bounding Box
Annotated
Stomata
Quantity
2161
Size
376
Download
Unavailable
Sun, Z., Wang, X., Song, Y., Li, Q., Song, J., Cai, J., Zhou, Q., Zhong, Y., Jin, S. and Jiang, D., 2023. StomataTracker: Revealing circadian rhythms of wheat stomata with in-situ video and deep learning. Computers and Electronics in Agriculture, 212, p.108120.
View here

StoManager1

Plant(s)
Hardwood, Poplar
Annotation
Bounding Box
Annotated
Stomata, Pore
Quantity
10715
Size
6590
Download
Available here (Total downloads: 4)
Wang, J., Renninger, H.J., Ma, Q. and Jin, S., 2023. StoManager1: An Enhanced, Automated, and High-throughput Tool to Measure Leaf Stomata and Guard Cell Metrics Using Empirical and Theoretical Algorithms.
View here

TodaYosuke2021

Plant(s)
Wheat
Annotation
Bounding Box
Annotated
Stomata
Quantity
728
Size
406
Download
Unavailable
Toda, Y., Tameshige, T., Tomiyama, M., Kinoshita, T. and Shimizu, K.K., 2021. An affordable image-analysis platform to accelerate stomatal phenotyping During microscopic observation. Frontiers in plant science, 12, p.715309.
View here

AonoAH2021

Plant(s)
Maize
Annotation
Classification
Annotated
Stomata
Quantity
2000
Size
458
Download
Unavailable
Aono, A.H., Nagai, J.S., Dickel, G.D.S., Marinho, R.C., de Oliveira, P.E., Papa, J.P. and Faria, F.A., 2021. A stomata classification and detection system in microscope images of maize cultivars. PloS one, 16(10), p.e0258679.
View here

Sultana2021Soybean

Plant(s)
Soybean
Annotation
Bounding Box
Annotated
Stomata
Quantity
183
Size
928
Download
Available here (Total downloads: 3)
Sultana, S.N., Park, H., Choi, S.H., Jo, H., Song, J.T., Lee, J.D. and Kang, Y.J., 2021. Optimizing the Experimental Method for Stomata-Profiling Automation of Soybean Leaves Based on Deep Learning. Plants, 10(12), p.2714.
View here

Cowling2021

Plant(s)
African Rice
Annotation
Bounding Box
Annotated
Stomata
Quantity
258
Size
272
Download
Available here (Total downloads: 1)
Cowling, S.B., Soltani, H., Mayes, S. and Murchie, E.H., 2021. Stomata Detector: High-throughput automation of stomata counting in a population of African rice (Oryza glaberrima) using transfer learning. bioRxiv, pp.2021-12.
View here