StomataSeg: Semi-Supervised Instance Segmentation for Sorghum Stomatal Components

2026-01-31

AI Summary

This study addresses the challenge of high-throughput phenotyping in sorghum, hindered by the minute size of stomatal structures and the high cost of manual annotation. To overcome these limitations, the authors propose a semi-supervised instance segmentation framework tailored to stomatal components—specifically pores, guard cells, and their composite regions. The approach innovatively integrates high-resolution image tiling with a pseudo-label augmentation strategy, effectively mitigating the nested small-object problem under scarce annotation conditions. Experimental evaluations based on Mask R-CNN and related models demonstrate significant improvements over baseline methods, achieving a semantic segmentation mIoU of 70.35% and an instance segmentation AP of 46.10%.

Technology Category

• Computer Vision: Segmentation
• Machine Learning: Semi-Supervised Learning
• Reasoning under Uncertainty: Stochastic Optimization

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets

Abstract

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 $\mu$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.

Problem

• stomata segmentation
• semi-supervised learning
• sorghum phenotyping
• small object detection
• annotation bottleneck

Innovation

• semi-supervised learning
• instance segmentation
• stomatal phenotyping
• patch-based preprocessing
• pseudo-labelling