  We report promising results for high-throughput on-field soybean pod count
with small mobile robots and machine-vision algorithms. Our results show that
the machine-vision based soybean pod counts are strongly correlated with
soybean yield. While pod counts has a strong correlation with soybean yield,
pod counting is extremely labor intensive, and has been difficult to automate.
Our results establish that an autonomous robot equipped with vision sensors can
autonomously collect soybean data at maturity. Machine-vision algorithms can be
used to estimate pod-counts across a large diversity panel planted across
experimental units (EUs, or plots) in a high-throughput, automated manner. We
report a correlation of 0.67 between our automated pod counts and soybean
yield. The data was collected in an experiment consisting of 1463 single-row
plots maintained by the University of Illinois soybean breeding program during
the 2020 growing season. We also report a correlation of 0.88 between automated
pod counts and manual pod counts over a smaller data set of 16 plots.
