基于三维点云多视角几何图编码器的黄瓜幼苗分割网络。

IF 7.6 1区 农林科学 Q1 AGRONOMY Plant Phenomics Pub Date : 2024-10-16 eCollection Date: 2024-01-01 DOI:10.34133/plantphenomics.0254
Yonglong Zhang, Yaling Xie, Jialuo Zhou, Xiangying Xu, Minmin Miao
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引用次数: 0

摘要

植物表型分析在观察和理解植物的生长发育过程中起着举足轻重的作用。在表型分析中,基于三维点云的植物器官分割近年来受到越来越多的关注。然而,仅利用欧几里得空间的几何关系特征仍然无法准确地分割和测量植物。为此,我们挖掘了更多的几何特征,并提出了一种基于多视图几何图编码器的分割网络,称为 SN-MGGE。首先,我们构建了一个点云采集平台来获取黄瓜幼苗点云数据集,并使用 CloudCompare 软件对点云数据进行标注。然后设计 GGE 模块,通过欧几里得空间和双曲空间上的图编码器生成点特征,包括几何关系和几何形状结构。最后,通过下采样操作和多层感知器获得语义分割结果。在黄瓜幼苗数据集上进行的大量实验清楚地表明,我们提出的 SN-MGGE 网络优于几种主流分割网络(如 PointNet++、AGConv 和 PointMLP),mIoU 和 OA 值分别达到 94.90% 和 97.43%。在分割结果的基础上,通过 K-means 聚类方法提取了 4 个表型参数(即株高、叶长、叶宽和叶面积);这些参数与地面实况非常接近,R 2 值分别达到 0.98、0.96、0.97 和 0.97。此外,一项消融研究和一项泛化实验也表明,SN-MGGE 网络具有鲁棒性和广泛性。
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Cucumber Seedling Segmentation Network Based on a Multiview Geometric Graph Encoder from 3D Point Clouds.

Plant phenotyping plays a pivotal role in observing and comprehending the growth and development of plants. In phenotyping, plant organ segmentation based on 3D point clouds has garnered increasing attention in recent years. However, using only the geometric relationship features of Euclidean space still cannot accurately segment and measure plants. To this end, we mine more geometric features and propose a segmentation network based on a multiview geometric graph encoder, called SN-MGGE. First, we construct a point cloud acquisition platform to obtain the cucumber seedling point cloud dataset, and employ CloudCompare software to annotate the point cloud data. The GGE module is then designed to generate the point features, including the geometric relationships and geometric shape structure, via a graph encoder over the Euclidean and hyperbolic spaces. Finally, the semantic segmentation results are obtained via a downsampling operation and multilayer perceptron. Extensive experiments on a cucumber seedling dataset clearly show that our proposed SN-MGGE network outperforms several mainstream segmentation networks (e.g., PointNet++, AGConv, and PointMLP), achieving mIoU and OA values of 94.90% and 97.43%, respectively. On the basis of the segmentation results, 4 phenotypic parameters (i.e., plant height, leaf length, leaf width, and leaf area) are extracted through the K-means clustering method; these parameters are very close to the ground truth, and the R 2 values reach 0.98, 0.96, 0.97, and 0.97, respectively. Furthermore, an ablation study and a generalization experiment also show that the SN-MGGE network is robust and extensive.

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来源期刊
Plant Phenomics
Plant Phenomics Multiple-
CiteScore
8.60
自引率
9.20%
发文量
26
审稿时长
14 weeks
期刊介绍: Plant Phenomics is an Open Access journal published in affiliation with the State Key Laboratory of Crop Genetics & Germplasm Enhancement, Nanjing Agricultural University (NAU) and published by the American Association for the Advancement of Science (AAAS). Like all partners participating in the Science Partner Journal program, Plant Phenomics is editorially independent from the Science family of journals. The mission of Plant Phenomics is to publish novel research that will advance all aspects of plant phenotyping from the cell to the plant population levels using innovative combinations of sensor systems and data analytics. Plant Phenomics aims also to connect phenomics to other science domains, such as genomics, genetics, physiology, molecular biology, bioinformatics, statistics, mathematics, and computer sciences. Plant Phenomics should thus contribute to advance plant sciences and agriculture/forestry/horticulture by addressing key scientific challenges in the area of plant phenomics. The scope of the journal covers the latest technologies in plant phenotyping for data acquisition, data management, data interpretation, modeling, and their practical applications for crop cultivation, plant breeding, forestry, horticulture, ecology, and other plant-related domains.
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