  This paper proposes a variable flow control system in real time with deep
learning using the segmentation of fruit trees in a pear orchard. The flow rate
control in real time, undesired pressure fluctuation and theoretical modeling
may differ from those in the real world. Therefore, two types of preliminary
experiments were designed to examine the linear relationship of the flow rate
modeling. Through a preliminary experiment, the parameters of the pulse width
modulation (PWM) controller were optimized, and an actual field experiment was
conducted to confirm the performance of the variable flow rate control system.
As a result of the field experiment, the performance of the proposed system was
satisfactory, as it showed that it could reduce pesticide use and the risk of
pesticide exposure. Especially, since the field experiment was conducted in an
unstructured environment, the proposed variable flow control system is expected
to be sufficiently applicable to other orchards.
