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


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.

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This research was supported, in part, by the Korea Institute for Advancement of Technology (KIAT) Grant funded by the Korea Government (MOTIE) (P0008473, HRD Program for Industrial Innovation); in part, by Korea Institute of Planning and Evaluation for Technology in Food, Agriculture and Forestry (IPET) through Agriculture, Food and Rural Affairs Convergence Technologies Program for Educating Creative Global Leader Program, funded by Ministry of Agriculture, Food and Rural Affairs (MAFRA) (716001-7).

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Correspondence to Jaehwi Seol, Jeongeun Kim or Hyoung Il Son.

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Seol, J., Kim, J. & Son, H.I. Field evaluations of a deep learning-based intelligent spraying robot with flow control for pear orchards. Precision Agric (2021).

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  • Variable flow rate control
  • Deep learning
  • Field experiments
  • Pulse width modulation