Agricultural specialists are actively turning to robotic and computer vision-based systems to reduce the manual labor required to inspect and manipulate crops. These tasks require robots to perceive and interact with plants from partial, localized observations, often in dense and cluttered environments. For perception, a central challenge is that crops are small, are easily occluded, and may change in appearance and position over time. For manipulation, the ability to learn visuomotor policies is limited by the lack of available datasets and the difficulty of collecting robot demonstrations in the field. This thesis addresses these challenges by aligning and associating partial observations across viewpoint and time for agricultural perception, and across viewpoint and embodiment for learning wrist-camera manipulation policies from human demonstrations.

In the first part of this thesis, we develop perception-based methods for visually inspecting small crops in agriculture from limited observations. We present a 3D reconstruction pipeline for non-destructive seed counting of sorghum panicles, a next-best-view planning approach for autonomously imaging and sizing apple fruitlets, and a transformer-based method for spatio-temporally associating apple fruitlets across days and viewpoints.

The second part of this thesis shifts towards robot manipulation and learning from human demonstrations. We present a method that transforms monocular egocentric human demonstrations into wrist-camera observations and robot actions for training visuomotor policies, without requiring depth sensors, multi-view camera setups, or custom data collection hardware. Building on this work, we propose to align egocentric and wrist-camera observations and actions in latent space, reducing reliance on explicit object tracking and image-space rendering. We further propose to incorporate visuo-tactile sensing for grape cluster inspection and harvesting. Together, these efforts investigate how aligning observations can support agricultural robots that reason from limited visual information and learn manipulation policies when robot data is difficult to collect.

Thesis Committee Members: