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arXiv · 2402.09685

Pheno-Robot: An Auto-Digital Modelling System for In-Situ Phenotyping in the Field

Yaoqiang Pan·Kewei Hu·Tianhao Liu·Chao Chen·Hanwen Kang
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Abstract

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.

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Yaoqiang Pan, Kewei Hu, Tianhao Liu, Chao Chen, Hanwen Kang. 2024-02-15. Pheno-Robot: An Auto-Digital Modelling System for In-Situ Phenotyping in the Field. https://arxiv.org/abs/2402.09685

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