A stomata imaging and segmentation pipeline incorporating generative AI to reduce dependency on manual groundtruthing.

Abstract

Stomata regulate gas and water exchange in plants and are crucial for plant productivity and survival, making their trait analysis essential for advancing plant biology research. While current machine learning methods enable automated stomatal trait extraction, existing approaches face significant limitations that require extensive manual labeling for training and additional human annotation when applied to new species. This study presents an automated system for extracting stomatal traits from Pisum sativum (pea) leaves that addresses these challenges through generative artificial intelligence. Our pipeline integrates imaging, detection, segmentation, and synthetic data generation processes. A nail polish impression technique was employed to prepare leaf microscopic images, followed by the application of deep learning networks to identify and segment stomata in these images. By including generative AI-produced synthetic data, our system achieves high segmentation accuracy across species, reducing manual relabeling requirements. This approach enables seamless cross-species model adaptation for many cases, alleviating the annotation bottleneck that often limits machine learning applications in plant biology. Our results demonstrate the pipeline's effectiveness for automated stomatal trait extraction and highlight generative AI's transformative potential in advancing stomatal detection methodologies, offering a scalable solution for broad-scale comparative stomatal analysis.

Plant phenotyping relevance

気孔形質を画像取得・検出・セグメンテーションで抽出する手法と、生成AIによる合成データを用いた種間適応を中心に開発しているため、植物フェノタイピング手法に該当する。

Code and data availability

The supplied blocks describe pea stomatal imaging, synthetic data generation, and YOLO11 segmentation, and reference publicly available datasets (chickpea reference dataset [23], barley/Arabidopsis datasets [24]), but no authors' public repository URL, code deposit, or dataset link appears in the supplied text. The DOI

No evidence-backed public reproduction asset is currently recorded.