A Ground Mobile Robot for Autonomous Terrestrial Laser Scanning-Based Field Phenotyping Article Swipe
Javier Rodriguez-Sanchez
,
Kyle Johnsen
,
Changying Li
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2404.04404
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2404.04404
Traditional field phenotyping methods are often manual, time-consuming, and destructive, posing a challenge for breeding progress. To address this bottleneck, robotics and automation technologies offer efficient sensing tools to monitor field evolution and crop development throughout the season. This study aimed to develop an autonomous ground robotic system for LiDAR-based field phenotyping in plant breeding trials. A Husky platform was equipped with a high-resolution three-dimensional (3D) laser scanner to collect in-field terrestrial laser scanning (TLS) data without human intervention. To automate the TLS process, a 3D ray casting analysis was implemented for optimal TLS site planning, and a route optimization algorithm was utilized to minimize travel distance during data collection. The platform was deployed in two cotton breeding fields for evaluation, where it autonomously collected TLS data. The system provided accurate pose information through RTK-GNSS positioning and sensor fusion techniques, with average errors of less than 0.6 cm for location and 0.38$^{\circ}$ for heading. The achieved localization accuracy allowed point cloud registration with mean point errors of approximately 2 cm, comparable to traditional TLS methods that rely on artificial targets and manual sensor deployment. This work presents an autonomous phenotyping platform that facilitates the quantitative assessment of plant traits under field conditions of both large agricultural fields and small breeding trials to contribute to the advancement of plant phenomics and breeding programs.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2404.04404
- https://arxiv.org/pdf/2404.04404
- OA Status
- green
- Cited By
- 1
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4394647983
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4394647983Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2404.04404Digital Object Identifier
- Title
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A Ground Mobile Robot for Autonomous Terrestrial Laser Scanning-Based Field PhenotypingWork title
- Type
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preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
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2024Year of publication
- Publication date
-
2024-04-05Full publication date if available
- Authors
-
Javier Rodriguez-Sanchez, Kyle Johnsen, Changying LiList of authors in order
- Landing page
-
https://arxiv.org/abs/2404.04404Publisher landing page
- PDF URL
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https://arxiv.org/pdf/2404.04404Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
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https://arxiv.org/pdf/2404.04404Direct OA link when available
- Concepts
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Mobile robot, Laser scanning, Robot, Computer science, Field (mathematics), Artificial intelligence, Computer vision, Laser, Remote sensing, Human–computer interaction, Geology, Physics, Mathematics, Optics, Pure mathematicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
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1Total citation count in OpenAlex
- Citations by year (recent)
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2024: 1Per-year citation counts (last 5 years)
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.conditions | 201 |
| abstract_inverted_index.contribute | 212 |
| abstract_inverted_index.throughout | 35 |
| abstract_inverted_index.LiDAR-based | 49 |
| abstract_inverted_index.Traditional | 0 |
| abstract_inverted_index.advancement | 215 |
| abstract_inverted_index.bottleneck, | 19 |
| abstract_inverted_index.collection. | 109 |
| abstract_inverted_index.deployment. | 183 |
| abstract_inverted_index.development | 34 |
| abstract_inverted_index.evaluation, | 120 |
| abstract_inverted_index.facilitates | 192 |
| abstract_inverted_index.implemented | 90 |
| abstract_inverted_index.information | 132 |
| abstract_inverted_index.phenotyping | 2, 51, 189 |
| abstract_inverted_index.positioning | 135 |
| abstract_inverted_index.techniques, | 139 |
| abstract_inverted_index.terrestrial | 71 |
| abstract_inverted_index.traditional | 172 |
| abstract_inverted_index.agricultural | 205 |
| abstract_inverted_index.autonomously | 123 |
| abstract_inverted_index.destructive, | 9 |
| abstract_inverted_index.localization | 156 |
| abstract_inverted_index.optimization | 99 |
| abstract_inverted_index.quantitative | 194 |
| abstract_inverted_index.registration | 161 |
| abstract_inverted_index.technologies | 23 |
| abstract_inverted_index.approximately | 167 |
| abstract_inverted_index.intervention. | 78 |
| abstract_inverted_index.0.38$^{\circ}$ | 151 |
| abstract_inverted_index.high-resolution | 63 |
| abstract_inverted_index.time-consuming, | 7 |
| abstract_inverted_index.three-dimensional | 64 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 3 |
| citation_normalized_percentile |