Field Evaluations of A Deep Learning-based Intelligent Spraying Robot with Flow Control for Pear Orchards
This work addresses pesticide efficiency and safety for orchard farming, but it is incremental as it applies existing deep learning and control methods to a specific agricultural context.
The paper tackled the problem of pesticide overuse and exposure risk in pear orchards by developing a deep learning-based variable flow control system for spraying robots, which reduced pesticide use in field experiments.
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.