Accelerated Haustoria Segmentation Enables Rapid Gene Function Analysis in Cereal–Powdery Mildew Pathosystems

Reliable, high-throughput quantification of early fungal infection events is crucial for gene function studies, but it remains labor-intensive. We report an openly available pipeline that automates the detection of β-glucuronidase (GUS)-stained epidermal cells and the intracellular haustoria formed by powdery mildew on barley and wheat leaves. Whole-slide images are captured with a commercial scanner, focus-projected, tiled, and analyzed by deep-learning models trained on expertly annotated datasets. A <i>You Only Look Once</i> (<i>YOLO</i>) network identifies GUS-positive cells, and a companion segmentation model pinpoints haustoria within each cell; automatic focus-layer selection preserves fine structural detail. The workflow runs in minutes per slide on a single workstation and maintains near-perfect agreement with manual counts in both barley and wheat, demonstrating robust cross-species transferability. By delivering single-cell readouts with minimal user input, the pipeline enables rapid functional validation screens and supports large-scale phenotyping of cereal-powdery mildew interactions. [Formula: see text] Copyright © 2025 The Author(s). This is an open access article distributed under the CC BY-NC-ND 4.0 International license.