  To automate harvesting and de-leafing of tomato plants using robots, it is
important to search and detect the task-relevant plant parts. This is
challenging due to high levels of occlusion in tomato plants. Active vision is
a promising approach to viewpoint planning, which helps robots to deliberately
plan camera viewpoints to overcome occlusion and improve perception accuracy.
However, current active-vision algorithms cannot differentiate between relevant
and irrelevant plant parts and spend time on perceiving irrelevant plant parts,
making them inefficient for targeted perception. We propose a semantics-aware
active-vision strategy that uses semantic information to identify the relevant
plant parts and prioritise them during view planning. We evaluated our strategy
on the task of searching and detecting the relevant plant parts using
simulation and real-world experiments. In simulation, using 3D models of tomato
plants with varying structural complexity, our semantics-aware strategy could
search and detect 81.8% of all the relevant plant parts using nine viewpoints.
It was significantly faster and detected more plant parts than predefined,
random, and volumetric active-vision strategies. Our strategy was also robust
to uncertainty in plant and plant-part position, plant complexity, and
different viewpoint-sampling strategies. Further, in real-world experiments,
our strategy could search and detect 82.7% of all the relevant plant parts
using seven viewpoints, under real-world conditions with natural variation and
occlusion, natural illumination, sensor noise, and uncertainty in camera poses.
Our results clearly indicate the advantage of using semantics-aware active
vision for targeted perception of plant parts and its applicability in
real-world setups. We believe that it can significantly improve the speed and
robustness of automated harvesting and de-leafing in tomato crop production.
