An adaptive and robust backstepping method based on wavelet network approximation was proposed to solve the problems of load variation, unmodeled uncertainties, physical parameter perturbation and external disturbance in the sprayer boom profiling system. Firstly, a complete mathematical model for the boom system with uncertainties, unknowns and nonlinear terms was established and transformed into a state space form with strict feedback. Secondly, the designed wavelet primitive was used to construct the neural network, to approximate the virtual equivalent control part of the backstepping method under the condition that the optimal error is bounded. The adaptive update law was selected to estimate the unknown parameters. The robust compensation term was introduced to reduce the adverse effect of the composite interference on the system. The input command signal order requirement was reduced. Finally, suitable functions were constructed by means of the Lyapunov stability theory, to prove that the position tracking error of the closed-loop system asymptotically converges to the origin. The simulation results show that, the proposed control method can realize the rapid maneuver adjustment of the sprayer boom position and posture, and effectively enhance the robust stability and control accuracy of the boom system.