用图神经网络分析玉米植株点云，识别叶、茎、土等部件。
Graph-Based Deep Learning for Component Segmentation of Maize Plants
- 将点云转为图结构，结合PCA与GNN提取特征
- 在玉米点云上实现80%以上平均交并比
- 适合农业自动化中植物部件分割任务
在精准农业中，识别单株作物组件是研究作物生产的重要任务。传统方法如2D成像、3D重建和卷积神经网络（CNN）在处理3D数据及识别个体植物组件时存在局限。为此，本文提出一种基于图神经网络（GNN）的新型深度学习架构，用于处理激光雷达（LiDAR）生成的3D点云（PC）数据。该方法将每个点视为节点，通过K近邻（KNN）层建立边，构建图结构；再使用Edge-Conv层增强点特征；最后通过图注意力网络（GAT）对可见表型组件（如叶、茎、土壤）进行分类。实验表明，该方法显著提升了分割精度，在平均交并比（IoU）上超过80%，优于现有基于点云的模型。
原文摘要 · Abstract (English)
In precision agriculture, one of the most important tasks when exploring crop production is identifying individual plant components. There are several attempts to accomplish this task by the use of traditional 2D imaging, 3D reconstructions, and Convolutional Neural Networks (CNN). However, they have several drawbacks when processing 3D data and identifying individual plant components. Therefore, in this work, we propose a novel Deep Learning architecture to detect components of individual plants on Light Detection and Ranging (LiDAR) 3D Point Cloud (PC) data sets. This architecture is based on the concept of Graph Neural Networks (GNN), and feature enhancing with Principal Component Analysis (PCA). For this, each point is taken as a vertex and by the use of a K-Nearest Neighbors (KNN) layer, the edges are established, thus representing the 3D PC data set. Subsequently, Edge-Conv layers are used to further increase the features of each point. Finally, Graph Attention Networks (GAT) are applied to classify visible phenotypic components of the plant, such as the leaf, stem, and soil. This study demonstrates that our graph-based deep learning approach enhances segmentation accuracy for identifying individual plant components, achieving percentages above 80% in the IoU average, thus outperforming other existing models based on point clouds.
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