  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.
