  Robots in tomato greenhouses need to perceive the plant and plant parts
accurately to automate monitoring, harvesting, and de-leafing tasks. Existing
perception systems struggle with the high levels of occlusion in plants and
often result in poor perception accuracy. One reason for this is because they
use fixed cameras or predefined camera movements. Next-best-view (NBV) planning
presents a alternate approach, in which the camera viewpoints are reasoned and
strategically planned such that the perception accuracy is improved. However,
existing NBV-planning algorithms are agnostic to the task-at-hand and give
equal importance to all the plant parts. This strategy is inefficient for
greenhouse tasks that require targeted perception of specific plant parts, such
as the perception of leaf nodes for de-leafing. To improve targeted perception
in complex greenhouse environments, NBV planning algorithms need an attention
mechanism to focus on the task-relevant plant parts. In this paper, we
investigated the role of attention in improving targeted perception using an
attention-driven NBV planning strategy. Through simulation experiments using
plants with high levels of occlusion and structural complexity, we showed that
focusing attention on task-relevant plant parts can significantly improve the
speed and accuracy of 3D reconstruction. Further, with real-world experiments,
we showed that these benefits extend to complex greenhouse conditions with
natural variation and occlusion, natural illumination, sensor noise, and
uncertainty in camera poses. Our results clearly indicate that using
attention-driven NBV planning in greenhouses can significantly improve the
efficiency of perception and enhance the performance of robotic systems in
greenhouse crop production.
