  This paper presents high precision control and deep learning-based corn stand
counting algorithms for a low-cost, ultra-compact 3D printed and autonomous
field robot for agricultural operations. Currently, plant traits, such as
emergence rate, biomass, vigor, and stand counting, are measured manually. This
is highly labor-intensive and prone to errors. The robot, termed TerraSentia,
is designed to automate the measurement of plant traits for efficient
phenotyping as an alternative to manual measurements. In this paper, we
formulate a Nonlinear Moving Horizon Estimator (NMHE) that identifies key
terrain parameters using onboard robot sensors and a learning-based Nonlinear
Model Predictive Control (NMPC) that ensures high precision path tracking in
the presence of unknown wheel-terrain interaction. Moreover, we develop a
machine vision algorithm designed to enable an ultra-compact ground robot to
count corn stands by driving through the fields autonomously. The algorithm
leverages a deep network to detect corn plants in images, and a visual tracking
model to re-identify detected objects at different time steps. We collected
data from 53 corn plots in various fields for corn plants around 14 days after
emergence (stage V3 - V4). The robot predictions have agreed well with the
ground truth with $C_{robot}=1.02 \times C_{human}-0.86$ and a correlation
coefficient $R=0.96$. The mean relative error given by the algorithm is
$-3.78\%$, and the standard deviation is $6.76\%$. These results indicate a
first and significant step towards autonomous robot-based real-time phenotyping
using low-cost, ultra-compact ground robots for corn and potentially other
crops.
