Field evaluations of a deep learning-based intelligent spraying robot with flow control for pear orchards

Abstract

This study proposes a deep learning-based real-time variable flow control system using the segmentation of fruit trees in a pear orchard. The real-time flow rate control, undesired pressure fluctuation and theoretical modeling may differ from those in the real world. Therefore, two types of preliminary experiments were conducted to examine the linear relationship of the flow rate modeling. Through preliminary experiments, the parameters of the pulse width modulation (PWM) controller were optimized, and a field experiment was conducted to confirm the performance of the variable flow rate control system. The field test was conducted for three cases: all open, on/off control, and variable flow rate control, showing results of 56.15 ( $$\pm 17.24$$ )%, 68.95 ( $$\pm 21.12)$$ % and 57.33 ( $$\pm 21.73$$ )% for each control. The result revealed that the proposed system performed satisfactorily, showing that pesticide use and the risk of pesticide exposure could be reduced.