  Agricultural robotics is an active research area due to global population
growth and expectations of food and labor shortages. Robots can potentially
help with tasks such as pruning, harvesting, phenotyping, and plant modeling.
However, agricultural automation is hampered by the difficulty in creating high
resolution 3D semantic maps in the field that would allow for safe manipulation
and navigation. In this paper, we build toward solutions for this issue and
showcase how the use of semantics and environmental priors can help in
constructing accurate 3D maps for the target application of sorghum.
Specifically, we 1) use sorghum seeds as semantic landmarks to build a visual
Simultaneous Localization and Mapping (SLAM) system that enables us to map
78\\% of a sorghum range on average, compared to 38% with ORB-SLAM2; and 2) use
seeds as semantic features to improve 3D reconstruction of a full sorghum
panicle from images taken by a robotic in-hand camera.
