Semantic embedding-guided graph self-attention network for plant stem-leaf separation from 3D point clouds

文献类型： 外文期刊

第一作者： Yang, Anhao

作者： Yang, Anhao;Yang, Juntao;Wu, Haiyang;Li, Zhenhai;Bai, Bo;Li, Guowei

作者机构：

关键词： 3D point cloud; Stem-leaf separation; Graph self-attention network; Semantic embedding-guided strategy

期刊名称：COMPUTERS AND ELECTRONICS IN AGRICULTURE （ 影响因子：8.9； 五年影响因子：9.3 ）

ISSN： 0168-1699

年卷期： 2025 年 238 卷

页码：

收录情况： SCI

摘要： With the advancement of agricultural modernization, precise plant phenotyping-such as stem-leaf separation-has gained significant importance in the fields of intelligent plant breeding and phenotypic trait extraction. Although deep learning techniques offer superior solutions in the task of complex plant structure segmentation, challenges remain due to insufficient feature representation and low interclass separability. To address this issue, this paper proposes a semantic embedding-guided graph self-attention network for plant stem-leaf separation from 3D point clouds. Specifically, the proposed method is built on an encoder-decoder architecture. The multiscale features are first extracted as the receptive field progressively increases to learn the local geometric representation. Following this, the proposed method constructs a feature enhancement module that integrates graph convolution and self-attention mechanisms. By leveraging graph convolution and the self-attention mechanism, both local and global sets of information are aggregated across multiple scales, capturing intricate geometric and topological relationships to ensure highly descriptive and distinguishing feature representations. Afterwards, we employ a hierarchical decoding structure that combines upsampling and feature fusion to progressively reconstruct high-resolution point cloud feature representations. Finally, the integration of semantic-aware discriminative loss with cross-entropy loss is designed to increase intraclass compactness, interclass separability, and regularization, thereby further strengthening class distinction and segmentation quality. To validate the effectiveness and reliability of the proposed method, experiments were conducted on publicly available Plant-3D and Pheno4D datasets. The results demonstrate that the proposed method achieves superior performance from both quantitative and qualitative perspectives in terms of stem-leaf separation, demonstrating a trend towards outperforming existing methods on the tested datasets, with improvements of 3.97% in precision, 4.35% in recall, 4.3% in the F1 score, 5.23% in the IoU and 7.64% in the mIoU. Additionally, t-SNE visualization and qualitative comparisons further confirm the model's superiority in feature clustering and structural boundary recognition. Our code is publicly available at https://github.com/Ahaoyang1/3D-SemSegRandlAnet.

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