The United States Green Industry faces a persistent labor shortage that limits the adoption of existing agricultural automation, largely because such systems are not designed for the
unstructured, densely planted environment of a tree nursery. This thesis presents a robotic
system intended to alleviate this shortage while remaining usable by non-technical farmers,
built around a map-based representation of the nursery environment. A custom robotic
platform, the mini-Amiga, and an accompanying LiDAR-camera-IMU sensor rig were developed to satisfy the maneuverability and payload requirements of tight nursery inter-row
spacing. Point cloud maps constructed with this platform, using the GLIM LiDAR-inertial
SLAM framework augmented with a custom GNSS georeferencing extension, were processed
with a Constrained Gaussian Mixture Model to segment individual trees without requiring
trunk visibility or large annotated training datasets; evaluated against 422 manually labeled
trees at a commercial nursery, this method achieved a precision of 0.94, a recall of 0.91,
and an F1 score of 0.93, substantially outperforming a forestry-domain baseline (F1: 0.71).
The resulting per-tree map was further augmented with photographic colorization and encoded as a hierarchically organized Universal Scene Description (USD) scene, supporting
non-destructive, multi-mode visualization and per-tree metadata storage intended for intuitive interaction by non-technical operators, and was used to derive a Nav2-compatible
occupancy grid and row-traversal paths intended for autonomous task execution. These results demonstrate that individual nursery trees can be accurately and efficiently segmented
from point cloud data, and that the resulting map can be represented in a form suited
to both non-technical human interaction and autonomous navigation, providing a practical
foundation for future work integrating localization and autonomous task execution to fully
realize the labor-saving potential of this system.