StomataSeg: Semi-Supervised Instance Segmentation for Sorghum Stomatal Components
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arXiv
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| Auteurs principaux: | , , , , , , , , , , |
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| Format: | Preprint |
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2026
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| _version_ | 1866918317452689408 |
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| author | Huang, Zhongtian Chen, Zhi Huang, Zi Yu, Xin Smith, Daniel Purushothama, Chaitanya Van Oosterom, Erik Wu, Alex Salter, William Li, Yan Chapman, Scott |
| author_facet | Huang, Zhongtian Chen, Zhi Huang, Zi Yu, Xin Smith, Daniel Purushothama, Chaitanya Van Oosterom, Erik Wu, Alex Salter, William Li, Yan Chapman, Scott |
| contents | Sorghum is a globally important cereal grown widely in water-limited and stress-prone regions. Its strong drought tolerance makes it a priority crop for climate-resilient agriculture. Improving water-use efficiency in sorghum requires precise characterisation of stomatal traits, as stomata control of gas exchange, transpiration and photosynthesis have a major influence on crop performance. Automated analysis of sorghum stomata is difficult because the stomata are small (often less than 40 $μ$m in length in grasses such as sorghum) and vary in shape across genotypes and leaf surfaces. Automated segmentation contributes to high-throughput stomatal phenotyping, yet current methods still face challenges related to nested small structures and annotation bottlenecks. In this paper, we propose a semi-supervised instance segmentation framework tailored for analysis of sorghum stomatal components. We collect and annotate a sorghum leaf imagery dataset containing 11,060 human-annotated patches, covering the three stomatal components (pore, guard cell and complex area) across multiple genotypes and leaf surfaces. To improve the detection of tiny structures, we split high-resolution microscopy images into overlapping small patches. We then apply a pseudo-labelling strategy to unannotated images, producing an additional 56,428 pseudo-labelled patches. Benchmarking across semantic and instance segmentation models shows substantial performance gains: for semantic models the top mIoU increases from 65.93% to 70.35%, whereas for instance models the top AP rises from 28.30% to 46.10%. These results demonstrate that combining patch-based preprocessing with semi-supervised learning significantly improves the segmentation of fine stomatal structures. The proposed framework supports scalable extraction of stomatal traits and facilitates broader adoption of AI-driven phenotyping in crop science. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_00703 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | StomataSeg: Semi-Supervised Instance Segmentation for Sorghum Stomatal Components Huang, Zhongtian Chen, Zhi Huang, Zi Yu, Xin Smith, Daniel Purushothama, Chaitanya Van Oosterom, Erik Wu, Alex Salter, William Li, Yan Chapman, Scott Computer Vision and Pattern Recognition Sorghum is a globally important cereal grown widely in water-limited and stress-prone regions. Its strong drought tolerance makes it a priority crop for climate-resilient agriculture. Improving water-use efficiency in sorghum requires precise characterisation of stomatal traits, as stomata control of gas exchange, transpiration and photosynthesis have a major influence on crop performance. Automated analysis of sorghum stomata is difficult because the stomata are small (often less than 40 $μ$m in length in grasses such as sorghum) and vary in shape across genotypes and leaf surfaces. Automated segmentation contributes to high-throughput stomatal phenotyping, yet current methods still face challenges related to nested small structures and annotation bottlenecks. In this paper, we propose a semi-supervised instance segmentation framework tailored for analysis of sorghum stomatal components. We collect and annotate a sorghum leaf imagery dataset containing 11,060 human-annotated patches, covering the three stomatal components (pore, guard cell and complex area) across multiple genotypes and leaf surfaces. To improve the detection of tiny structures, we split high-resolution microscopy images into overlapping small patches. We then apply a pseudo-labelling strategy to unannotated images, producing an additional 56,428 pseudo-labelled patches. Benchmarking across semantic and instance segmentation models shows substantial performance gains: for semantic models the top mIoU increases from 65.93% to 70.35%, whereas for instance models the top AP rises from 28.30% to 46.10%. These results demonstrate that combining patch-based preprocessing with semi-supervised learning significantly improves the segmentation of fine stomatal structures. The proposed framework supports scalable extraction of stomatal traits and facilitates broader adoption of AI-driven phenotyping in crop science. |
| title | StomataSeg: Semi-Supervised Instance Segmentation for Sorghum Stomatal Components |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2602.00703 |