Graph Self-Attention Network with Semantic Embedding for Stem-Leaf Separation from 3D Point Clouds

Abstract. In the context of agricultural modernization, precise 3D organ segmentation has become indispensable for automated extraction of phenotypic traits. In particular, the precise delineation of stem and leaf structures from 3D point clouds is critical for monitoring plant growth and supporting high-throughput breeding programs. However, the intricate structure of crops and the blurred boundaries between stems and leaves present significant challenges, leading to the poor segmentation performance. To tackle these problems, we propose a Semantic Embedding-Guided Graph Self-Attention Network for stem-leaf separation in 3D point clouds, to tackle weak feature representation and low inter-class separability in complex plant structures. During the encoding stage, a multi-scale feature extraction module captures fine-grained local geometries, while a feature fusion module integrating graph convolution and self-attention facilitates deep fusion of local and global semantic information. In the decoding stage, hierarchical upsampling combined with multi-level feature fusion reconstructs high-resolution representations to achieve fine-grained segmentation. Furthermore, we introduce a joint loss function that integrates inter-class discriminative loss with cross-entropy, aiming to optimize intra-class uniformity and reinforce class boundary delineation. Validation experiments on the Plant-3D dataset demonstrate that our methodology attains superior performance, with mean precision, recall, and IoU achieving 96.47%, 96.39%, and 93.50%, respectively. The proposed approach demonstrates high robustness and generalizability across diverse plant species and growth stages, providing an effective solution for high-throughput plant phenotyping.