Phenobot: An Autodigital Modeling System for in situ Phenotyping in Horticulture - Kewei Hu; Yaoqiang Pan; Tianhao Liu; Hugh Zhou; Chao Chen; Hanwen Kang (Advanced Intelligent Systems)
Accurate reconstruction of plant models for phenotyping analysis is critical for optimizing 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. Results demonstrate the effectiveness of the system in agricultural environments. The phenorobot system autonomously collects high‐quality data by navigating around plants. In addition, the in situ modeling 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.