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Abstract

Doubled haploid (DH) technology can fast-track crop breeding.Haploid induction yields haploids with only one set of genomes, which are usually sterile.Haploid fertility (HF) is the ability of haploid plants to set seed, and it is a critical bottleneck in DH pipelines.Genetic mechanisms to restore HF hold immense potential in DH crop breeding, yet its phenotyping remains manual, destructive, and inconsistent.While recent advances in imaging and machine learning have improved throughput for general plant traits, no curated image dataset exists for Arabidopsis thaliana that explicitly represents HF.Here, we present AutoSiQ, a dataset and baseline deep learning pipeline for automated HF quantification.AutoSiQ includes high-resolution scanned inflorescences annotated with a seven-class ontology encompassing green siliques, green fertile siliques, mature siliques, fertile siliques, cracked fertile siliques, cracked siliques, and flowers.This multi-class annotation scheme preserves biologically meaningful information beyond binary fertile/non-fertile distinctions, enabling reliable fertility estimation and future phenotyping applications.We release baseline object detection models (YOLOv5), trained using the AutoSiQ dataset, and evaluate their performance across confidence thresholds.Model predictions strongly correlate with manual counts, achieving R² up to 0.94 for total silique number estimation.We further demonstrate AutoSiQ’s utility for automated haploid fertility rate (HFR) estimation and genotype discrimination between two contrasting genotypes (WT and bmf2 mutant).A longitudinal analysis identifies ~60 days after sowing (DAS) as the optimal harvest time for maximizing mature silique counts by balancing between the number of immature buds and silique shattering.By releasing both the dataset and baseline code, AutoSiQ provides a reproducible and extensible foundation for high-throughput fertility phenotyping in haploid Arabidopsis.

Document Type

Article

Publication Date

1-1-2026

Notes/Citation Information

Publisher Copyright: Copyright © 2026 Zhou, Jubery, Ganapathysubramanian, Lübberstedt and Aboobucker.

Digital Object Identifier (DOI)

10.3389/fpls.2026.1767588

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