  We propose a novel hybrid cable-based robot with manipulator and camera for
high-accuracy, medium-throughput plant monitoring in a vertical hydroponic farm
and, as an example application, demonstrate non-destructive plant mass
estimation. Plant monitoring with high temporal and spatial resolution is
important to both farmers and researchers to detect anomalies and develop
predictive models for plant growth. The availability of high-quality,
off-the-shelf structure-from-motion (SfM) and photogrammetry packages has
enabled a vibrant community of roboticists to apply computer vision for
non-destructive plant monitoring. While existing approaches tend to focus on
either high-throughput (e.g. satellite, unmanned aerial vehicle (UAV),
vehicle-mounted, conveyor-belt imagery) or high-accuracy/robustness to
occlusions (e.g. turn-table scanner or robot arm), we propose a middle-ground
that achieves high accuracy with a medium-throughput, highly automated robot.
Our design pairs the workspace scalability of a cable-driven parallel robot
(CDPR) with the dexterity of a 4 degree-of-freedom (DoF) robot arm to
autonomously image many plants from a variety of viewpoints. We describe our
robot design and demonstrate it experimentally by collecting daily photographs
of 54 plants from 64 viewpoints each. We show that our approach can produce
scientifically useful measurements, operate fully autonomously after initial
calibration, and produce better reconstructions and plant property estimates
than those of over-canopy methods (e.g. UAV). As example applications, we show
that our system can successfully estimate plant mass with a Mean Absolute Error
(MAE) of 0.586g and, when used to perform hypothesis testing on the
relationship between mass and age, produces p-values comparable to ground-truth
data (p=0.0020 and p=0.0016, respectively).
