Graph-Based Deep Learning for Component Segmentation of Maize Plants

• URL: http://arxiv.org/abs/2507.00182v2
• Date: Wed, 02 Jul 2025 17:15:08 GMT
• Title: Graph-Based Deep Learning for Component Segmentation of Maize Plants
• Authors: J. I. Ruiz-Martinez, A. Mendez-Vazquez, E. Rodriguez-Tello,
• Abstract summary: We propose a novel Deep Learning architecture to detect components of individual plants on LiDAR 3D Point Cloud data sets.<n>This architecture is based on the concept of Graph Neural Networks (GNN) and feature enhancing with Principal Component Analysis (PCA)<n>Our graph-based deep learning approach enhances segmentation accuracy for identifying individual plant components, achieving percentages above 80% in the IoU average.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

Related papers

• Rapeseed population point cloud completion network (RP-PCN) with dynamic graph convolution for 3D reconstruction of crop canopy occlusion architecture [4.377318975816766]
  We propose a point cloud completion model for 3D reconstruction of rapeseed populations from seeding to silique stages.<n>A complete point cloud generation framework was developed with the virtual-real integration (VRI) simulation method.<n>The effectiveness of point cloud completion was validated by predicting yield using architectural indicators from complete point clouds of rapeseed population.
  arXiv  Detail & Related papers  (2025-06-23T05:02:31Z)
• Point-GR: Graph Residual Point Cloud Network for 3D Object Classification and Segmentation [2.4999074238880485]
  This paper presents Point-GR, a novel deep learning architecture designed explicitly to transform unordered raw point clouds into higher dimensions.<n>It introduces residual-based learning within the network to mitigate the point permutation issues in point cloud data.<n>It achieves a state-of-the-art scene segmentation mean IoU of 73.47% on the S3DIS benchmark dataset, showcasing its effectiveness.
  arXiv  Detail & Related papers  (2024-12-04T06:12:19Z)
• PLANesT-3D: A new annotated dataset for segmentation of 3D plant point clouds [0.892439883048931]
  We introduce PLANesT-3D; a new annotated dataset of 3D color point clouds of plants. PLANesT-3D is composed of 34 point cloud models representing 34 real plants from three different plant species.
  arXiv  Detail & Related papers  (2024-07-30T19:27:37Z)
• Boosting Cross-Domain Point Classification via Distilling Relational Priors from 2D Transformers [59.0181939916084]
  Traditional 3D networks mainly focus on local geometric details and ignore the topological structure between local geometries. We propose a novel Priors Distillation (RPD) method to extract priors from the well-trained transformers on massive images. Experiments on the PointDA-10 and the Sim-to-Real datasets verify that the proposed method consistently achieves the state-of-the-art performance of UDA for point cloud classification.
  arXiv  Detail & Related papers  (2024-07-26T06:29:09Z)
• Eff-3DPSeg: 3D organ-level plant shoot segmentation using annotation-efficient point clouds [1.5882586857953638]
  We propose a novel weakly supervised framework, Eff-3DPSeg, for 3D plant shoot segmentation. High-resolution point clouds of soybean were reconstructed using a low-cost photogrammetry system. A weakly-supervised deep learning method was proposed for plant organ segmentation.
  arXiv  Detail & Related papers  (2022-12-20T14:09:37Z)
• GraphCSPN: Geometry-Aware Depth Completion via Dynamic GCNs [49.55919802779889]
  We propose a Graph Convolution based Spatial Propagation Network (GraphCSPN) as a general approach for depth completion. In this work, we leverage convolution neural networks as well as graph neural networks in a complementary way for geometric representation learning. Our method achieves the state-of-the-art performance, especially when compared in the case of using only a few propagation steps.
  arXiv  Detail & Related papers  (2022-10-19T17:56:03Z)
• Dense Graph Convolutional Neural Networks on 3D Meshes for 3D Object Segmentation and Classification [0.0]
  We present new designs of graph convolutional neural networks (GCNs) on 3D meshes for 3D object classification and segmentation. We use the faces of the mesh as basic processing units and represent a 3D mesh as a graph where each node corresponds to a face.
  arXiv  Detail & Related papers  (2021-06-30T02:17:16Z)
• PIG-Net: Inception based Deep Learning Architecture for 3D Point Cloud Segmentation [0.9137554315375922]
  We propose a inception based deep network architecture called PIG-Net, that effectively characterizes the local and global geometric details of the point clouds. We perform an exhaustive experimental analysis of the PIG-Net architecture on two state-of-the-art datasets.
  arXiv  Detail & Related papers  (2021-01-28T13:27:55Z)
• TSGCNet: Discriminative Geometric Feature Learning with Two-Stream GraphConvolutional Network for 3D Dental Model Segmentation [141.2690520327948]
  We propose a two-stream graph convolutional network (TSGCNet) to learn multi-view information from different geometric attributes. We evaluate our proposed TSGCNet on a real-patient dataset of dental models acquired by 3D intraoral scanners.
  arXiv  Detail & Related papers  (2020-12-26T08:02:56Z)
• Learning Geometry-Disentangled Representation for Complementary Understanding of 3D Object Point Cloud [50.56461318879761]
  We propose Geometry-Disentangled Attention Network (GDANet) for 3D image processing. GDANet disentangles point clouds into contour and flat part of 3D objects, respectively denoted by sharp and gentle variation components. Experiments on 3D object classification and segmentation benchmarks demonstrate that GDANet achieves the state-of-the-arts with fewer parameters.
  arXiv  Detail & Related papers  (2020-12-20T13:35:00Z)
• PC-RGNN: Point Cloud Completion and Graph Neural Network for 3D Object Detection [57.49788100647103]
  LiDAR-based 3D object detection is an important task for autonomous driving. Current approaches suffer from sparse and partial point clouds of distant and occluded objects. In this paper, we propose a novel two-stage approach, namely PC-RGNN, dealing with such challenges by two specific solutions.
  arXiv  Detail & Related papers  (2020-12-18T18:06:43Z)
• Campus3D: A Photogrammetry Point Cloud Benchmark for Hierarchical Understanding of Outdoor Scene [76.4183572058063]
  We present a richly-annotated 3D point cloud dataset for multiple outdoor scene understanding tasks. The dataset has been point-wisely annotated with both hierarchical and instance-based labels. We formulate a hierarchical learning problem for 3D point cloud segmentation and propose a measurement evaluating consistency across various hierarchies.
  arXiv  Detail & Related papers  (2020-08-11T19:10:32Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.