Pheno-Robot: An Auto-Digital Modelling System for In-Situ Phenotyping in the Field
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arXiv
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| Auteurs principaux: | , , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866916126253907968 |
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| author | Pan, Yaoqiang Hu, Kewei Liu, Tianhao Chen, Chao Kang, Hanwen |
| author_facet | Pan, Yaoqiang Hu, Kewei Liu, Tianhao Chen, Chao Kang, Hanwen |
| contents | Accurate reconstruction of plant models for phenotyping analysis is critical for optimising sustainable agricultural practices in precision agriculture. Traditional laboratory-based phenotyping, while valuable, falls short of understanding how plants grow under uncontrolled conditions. Robotic technologies offer a promising avenue for large-scale, direct phenotyping in real-world environments. This study explores the deployment of emerging robotics and digital technology in plant phenotyping to improve performance and efficiency. Three critical functional modules: environmental understanding, robotic motion planning, and in-situ phenotyping, are introduced to automate the entire process. Experimental results demonstrate the effectiveness of the system in agricultural environments. The pheno-robot system autonomously collects high-quality data by navigating around plants. In addition, the in-situ modelling model reconstructs high-quality plant models from the data collected by the robot. The developed robotic system shows high efficiency and robustness, demonstrating its potential to advance plant science in real-world agricultural environments. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_09685 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Pheno-Robot: An Auto-Digital Modelling System for In-Situ Phenotyping in the Field Pan, Yaoqiang Hu, Kewei Liu, Tianhao Chen, Chao Kang, Hanwen Robotics Accurate reconstruction of plant models for phenotyping analysis is critical for optimising sustainable agricultural practices in precision agriculture. Traditional laboratory-based phenotyping, while valuable, falls short of understanding how plants grow under uncontrolled conditions. Robotic technologies offer a promising avenue for large-scale, direct phenotyping in real-world environments. This study explores the deployment of emerging robotics and digital technology in plant phenotyping to improve performance and efficiency. Three critical functional modules: environmental understanding, robotic motion planning, and in-situ phenotyping, are introduced to automate the entire process. Experimental results demonstrate the effectiveness of the system in agricultural environments. The pheno-robot system autonomously collects high-quality data by navigating around plants. In addition, the in-situ modelling model reconstructs high-quality plant models from the data collected by the robot. The developed robotic system shows high efficiency and robustness, demonstrating its potential to advance plant science in real-world agricultural environments. |
| title | Pheno-Robot: An Auto-Digital Modelling System for In-Situ Phenotyping in the Field |
| topic | Robotics |
| url | https://arxiv.org/abs/2402.09685 |