  Traditional field phenotyping methods are often manual, time-consuming, and
destructive, posing a challenge for breeding progress. To address this
bottleneck, robotics and automation technologies offer efficient sensing tools
to monitor field evolution and crop development throughout the season. This
study aimed to develop an autonomous ground robotic system for LiDAR-based
field phenotyping in plant breeding trials. A Husky platform was equipped with
a high-resolution three-dimensional (3D) laser scanner to collect in-field
terrestrial laser scanning (TLS) data without human intervention. To automate
the TLS process, a 3D ray casting analysis was implemented for optimal TLS site
planning, and a route optimization algorithm was utilized to minimize travel
distance during data collection. The platform was deployed in two cotton
breeding fields for evaluation, where it autonomously collected TLS data. The
system provided accurate pose information through RTK-GNSS positioning and
sensor fusion techniques, with average errors of less than 0.6 cm for location
and 0.38$^{\circ}$ for heading. The achieved localization accuracy allowed
point cloud registration with mean point errors of approximately 2 cm,
comparable to traditional TLS methods that rely on artificial targets and
manual sensor deployment. This work presents an autonomous phenotyping platform
that facilitates the quantitative assessment of plant traits under field
conditions of both large agricultural fields and small breeding trials to
contribute to the advancement of plant phenomics and breeding programs.
