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Orchard wind-sending plant protection machinery has low real-time performance, large drift, and poor environmental adaptability. To reduce the drift loss of orchard air-assisted plant protection machinery and improve the deposition amount and uniformity within the canopy, a control system based on the tent sparrow search algorithm （TSSA） to optimize PID （proportional-integralderivative） parameters was proposed on the basis of the orchard air-assisted anti-drift sprayer. This system enhanced the optimization ability for PID parameters by introducing tent chaotic mapping,，random following strategy, and dimension-by-dimension lens imaging reverse learning into the sparrow search algorithm, avoiding the PID system from falling into local minima, and improving the level of automation in wind curtain speed regulation. Consequently, it reduced the drift loss of droplets and enhanced the canopy deposition amount and uniformity. Simulation test results showed that compared with the contrast algorithms, the response time was reduced by 45.77%, and the overshoot was reduced by 13.22%, demonstrating superior automatic regulation capability. Actual test results indicated that the average error and the longest response time for adjusting the wind curtain speed were 2.11% and 0.8 s, respectively, which were 24.1% and 20% lower than those of other algorithms. Compared with the orchard air-assisted anti-drift sprayer, after applying this system, the drift of droplets, ground loss, and the coefficient of variation of droplet deposition distribution were reduced by 13%,，16.13%, and 29.62%, respectively, while the canopy deposition amount was increased by 11.97%. This research achievement provided a technical solution for addressing the problems of pesticide drift loss and canopy internal deposition in orchards.