Intelligent Prediction of Spray Distribution and Hydraulic Behavior in an Automatic Spraying Machine for Precision Agriculture

Abstract

ABSTRACT Existing machine‐learning applications in agricultural spraying have mainly focused on droplet‐size descriptors, canopy coverage, deposition, or image‐based spray detection, while the simultaneous prediction of hydraulic and geometric spray responses from controllable operating variables remains limited. This study developed an experimental–computational framework for predicting mean nozzle discharge, effective spray width, derived spray angle, and mean inter‐nozzle overlap from nozzle model, operating pressure, and spray height. Four hollow‐cone ceramic nozzle models were evaluated at five pressures (6.0–9.5 bar) and three heights (30–50 cm), producing 60 operating conditions tested in triplicate. The resulting 180 experimental runs were averaged into 60 condition‐level records for machine‐learning analysis. Mean nozzle discharge ranged from 0.98 to 2.03 L , effective spray width ranged from 34.75 to 71.50 cm, the maximum derived spray angle reached 96.77°, and mean inter‐nozzle overlap ranged from 34.00 to 71.67 cm across the investigated operating conditions. Extra Trees Regressor provided the best predictions for mean nozzle discharge (, RMSE = 49.25 mL , MAE = 37.99 mL ), effective spray width (, RMSE = 2.37 cm), and mean overlap (, RMSE = 3.13 cm). Support vector regression with a radial basis function kernel performed best for derived spray angle (, RMSE = 3.53°). Pressure increased discharge and lateral footprint, whereas increasing height widened the footprint but generally reduced the geometrically derived angle. The models provide rapid condition‐level estimates within the investigated experimental domain and may complement physical calibration. Their transfer to other nozzle families, moving platforms, field environments, or real‐time control requires additional validation.