Estimating Radicle Length of Germinating Elm Seeds via Deep Learning.

Abstract

spp.), ecologically and economically significant, pose unique challenges due to their curved seedling morphology. Traditional manual measurement methods are time-consuming, prone to human error, and often lack consistency. Moreover, automated approaches remain limited and often fail to accurately process seedlings with nonlinear or curved morphologies. In this study, we introduce GLEN, a deep learning-based model for detecting germinating elm seeds and accurately estimating their lengths of germinating structures. It leverages a dual-path architecture that combines pixel-level spatial features with instance-level semantic information, enabling robust measurement of curved radicles. To support training, we construct GermElmData, a curated dataset of annotated elm seedling images, and introduce a novel synthetic data generation pipeline that produces high-fidelity, morphologically diverse germination images. This reduces the dependence on extensive manual annotations and improves model generalization. Experimental results demonstrate that GLEN achieves an estimation error on the order of millimeters, outperforming existing models. Beyond quantifying germinating elm seeds, the architectural design and data augmentation strategies in GLEN offer a scalable framework for morphological quantification in both plant phenotyping and broader biomedical imaging domains.

Plant phenotyping relevance

発芽エルム種子の曲がった幼根長を深層学習で推定する手法を開発し、注釈付き画像データセットと合成データ生成パイプラインも構築しているため、植物表現型取得が中心である。

Code and data availability

The paper introduces GermElmData (796 real + 655 synthetic annotated elm seedling images) and the GLEN model, but no block contains any availability, deposit, or public URL for the dataset, synthetic pipeline, code, or trained checkpoints. All URLs present (ImageJ, ISAT_with_segment_anything, Dreamina/jimeng, AdelaiDet

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