Pheno-Robot: An Auto-Digital Modelling System for In-Situ Phenotyping in the Field
<|reference_start|>Pheno-Robot: An Auto-Digital Modelling System for In-Situ Phenotyping in the Field: 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.<|reference_end|>
@article{pan2024pheno-robot:,
    title={Pheno-Robot: An Auto-Digital Modelling System for In-Situ Phenotyping in
  the Field},
    author={Yaoqiang Pan, Kewei Hu, Tianhao Liu, Chao Chen, and Hanwen Kang},
    journal={arXiv preprint arXiv:2402.09685},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.09685},
    primaryClass={cs.RO}
}