A 3D Surface Reconstruction Pipeline for Plant Phenotyping
Plant phenotyping plays a crucial role in crop science and plant breeding. However, traditional methods often involve time-consuming and manual observations. Therefore, it is essential to develop automated, sensor-driven techniques that can provide objective and rapid information. Various methods re...
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MDPI AG
2024-12-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/16/24/4720 |
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| author | Lina Stausberg Berit Jost Lasse Klingbeil Heiner Kuhlmann |
| author_facet | Lina Stausberg Berit Jost Lasse Klingbeil Heiner Kuhlmann |
| author_sort | Lina Stausberg |
| collection | DOAJ |
| description | Plant phenotyping plays a crucial role in crop science and plant breeding. However, traditional methods often involve time-consuming and manual observations. Therefore, it is essential to develop automated, sensor-driven techniques that can provide objective and rapid information. Various methods rely on camera systems, including RGB, multi-spectral, and hyper-spectral cameras, which offer valuable insights into plant physiology. In recent years, 3D sensing systems such as laser scanners have gained popularity due to their ability to capture structural plant parameters that are difficult to obtain using spectral sensors. Unlike images, point clouds are not structured and require pre-processing steps to extract precise information and handle noise or missing points. One approach is to generate mesh-based surface representations using triangulation. A key challenge in the 3D surface reconstruction of plants is the pre-processing of point clouds, which involves removing non-plant noise from the scene, segmenting point clouds from populations to individual plants, and further dividing individual plants into their respective organs. In this study, we will not focus on the segmentation aspect but rather on the other pre-processing steps, like denoising parameters, which depend on the data type. We present an automated pipeline for converting high-resolution point clouds into surface models of plants. The pipeline incorporates additional pre-processing steps such as outlier removal, denoising, and subsampling to ensure the accuracy and quality of the reconstructed surfaces. Data were collected using three different sensors: a handheld scanner, a terrestrial laser scanner (TLS), and a mobile mapping platform, under varying conditions from controlled laboratory environments to complex field settings. The investigation includes five different plant species, each with distinct characteristics, to demonstrate the potential of the pipeline. In a next step, phenotypic traits such as leaf area, leaf area index (LAI), and leaf angle distribution (LAD) were calculated to further illustrate the pipeline’s potential and effectiveness. The pipeline is based on the Open3D framework and is available open source. |
| format | Article |
| id | doaj-art-eb2dceef7c7442068c103d2bb720617e |
| institution | Kabale University |
| issn | 2072-4292 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj-art-eb2dceef7c7442068c103d2bb720617e2024-12-27T14:51:00ZengMDPI AGRemote Sensing2072-42922024-12-011624472010.3390/rs16244720A 3D Surface Reconstruction Pipeline for Plant PhenotypingLina Stausberg0Berit Jost1Lasse Klingbeil2Heiner Kuhlmann3Institute of Geodesy and Geoinformation, Department of Geodesy, University of Bonn, Nussallee 17, 53115 Bonn, GermanyInstitute of Geodesy and Geoinformation, Department of Geodesy, University of Bonn, Nussallee 17, 53115 Bonn, GermanyInstitute of Geodesy and Geoinformation, Department of Geodesy, University of Bonn, Nussallee 17, 53115 Bonn, GermanyInstitute of Geodesy and Geoinformation, Department of Geodesy, University of Bonn, Nussallee 17, 53115 Bonn, GermanyPlant phenotyping plays a crucial role in crop science and plant breeding. However, traditional methods often involve time-consuming and manual observations. Therefore, it is essential to develop automated, sensor-driven techniques that can provide objective and rapid information. Various methods rely on camera systems, including RGB, multi-spectral, and hyper-spectral cameras, which offer valuable insights into plant physiology. In recent years, 3D sensing systems such as laser scanners have gained popularity due to their ability to capture structural plant parameters that are difficult to obtain using spectral sensors. Unlike images, point clouds are not structured and require pre-processing steps to extract precise information and handle noise or missing points. One approach is to generate mesh-based surface representations using triangulation. A key challenge in the 3D surface reconstruction of plants is the pre-processing of point clouds, which involves removing non-plant noise from the scene, segmenting point clouds from populations to individual plants, and further dividing individual plants into their respective organs. In this study, we will not focus on the segmentation aspect but rather on the other pre-processing steps, like denoising parameters, which depend on the data type. We present an automated pipeline for converting high-resolution point clouds into surface models of plants. The pipeline incorporates additional pre-processing steps such as outlier removal, denoising, and subsampling to ensure the accuracy and quality of the reconstructed surfaces. Data were collected using three different sensors: a handheld scanner, a terrestrial laser scanner (TLS), and a mobile mapping platform, under varying conditions from controlled laboratory environments to complex field settings. The investigation includes five different plant species, each with distinct characteristics, to demonstrate the potential of the pipeline. In a next step, phenotypic traits such as leaf area, leaf area index (LAI), and leaf angle distribution (LAD) were calculated to further illustrate the pipeline’s potential and effectiveness. The pipeline is based on the Open3D framework and is available open source.https://www.mdpi.com/2072-4292/16/24/4720automated pipeline3D point cloudssurface reconstructionlaser scanningcrop reconstructionphenotypic traits |
| spellingShingle | Lina Stausberg Berit Jost Lasse Klingbeil Heiner Kuhlmann A 3D Surface Reconstruction Pipeline for Plant Phenotyping Remote Sensing automated pipeline 3D point clouds surface reconstruction laser scanning crop reconstruction phenotypic traits |
| title | A 3D Surface Reconstruction Pipeline for Plant Phenotyping |
| title_full | A 3D Surface Reconstruction Pipeline for Plant Phenotyping |
| title_fullStr | A 3D Surface Reconstruction Pipeline for Plant Phenotyping |
| title_full_unstemmed | A 3D Surface Reconstruction Pipeline for Plant Phenotyping |
| title_short | A 3D Surface Reconstruction Pipeline for Plant Phenotyping |
| title_sort | 3d surface reconstruction pipeline for plant phenotyping |
| topic | automated pipeline 3D point clouds surface reconstruction laser scanning crop reconstruction phenotypic traits |
| url | https://www.mdpi.com/2072-4292/16/24/4720 |
| work_keys_str_mv | AT linastausberg a3dsurfacereconstructionpipelineforplantphenotyping AT beritjost a3dsurfacereconstructionpipelineforplantphenotyping AT lasseklingbeil a3dsurfacereconstructionpipelineforplantphenotyping AT heinerkuhlmann a3dsurfacereconstructionpipelineforplantphenotyping AT linastausberg 3dsurfacereconstructionpipelineforplantphenotyping AT beritjost 3dsurfacereconstructionpipelineforplantphenotyping AT lasseklingbeil 3dsurfacereconstructionpipelineforplantphenotyping AT heinerkuhlmann 3dsurfacereconstructionpipelineforplantphenotyping |