LASH-SegNet: A Lightweight Deep Learning Network for Multi-Trait Segmentation of Early-Stage Soybean Plants

Abstract

Accurate segmentation of multiple phenotypic traits in early-stage soybean plants is essential for automated phenotyping and early-stage breeding analysis. However, the morphological diversity and heterogeneous visual characteristics of key traits, including hypocotyls, flowers, pubescence, and leaves, make unified segmentation challenging under complex backgrounds. To address this problem, this study proposes LASH-SegNet, a lightweight deep learning network for multi-trait segmentation of early-stage soybean plants. The network integrates dynamic snake convolution to model elongated and non-rigid structures and incorporates a SegNeXt-Attention module to enhance multi-scale feature representation and boundary awareness. In addition, the WIoUv3 loss function is adopted to improve localization accuracy and boundary alignment, particularly for slender targets. Experimental results show that LASH-SegNet achieves a precision of 88.82%, recall of 89.78%, and an F1-score of 89.30%, with an mAP50 of 91.24%, while maintaining a compact model size of 5.9 M parameters and 11.3 MB. These results demonstrate that LASH-SegNet provides an accurate and efficient solution for high-throughput multi-trait early-stage soybean plant phenotyping.

Plant phenotyping relevance

大豆幼苗の複数形質を自動抽出する画像セグメンテーション手法を開発しており、植物フェノタイピング手法が研究の中心である。

Code and data availability

The paper's soybean multi-trait image dataset (1500 annotated images) and trained LASH-SegNet model are not publicly available; the Data Availability Statement says they are available only on request from the corresponding author.

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