Design of a Precision Spraying System for Plant‑Protection UAV Based on Deep Learning
Traditional plant‑protection UAVs mostly adopt the extensive full‑coverage spraying mode, which suffers from problems such as low pest‑and‑disease identification accuracy, serious pesticide‑liquid waste, poor spraying uniformity, and easy farmland environmental pollution. Such systems can hardly meet the precise, green, and efficient plant‑protection demands of modern agriculture. Accordingly, this paper designs a precision spraying system for plant‑protection UAVs based on deep learning. Taking an embedded UAV platform as the hardware carrier, it adopts a lightweight deep‑learning object‑detection algorithm to realize intelligent identification of field pests and diseases as well as accurate division of operation areas. Combined with PID closed‑loop control, it dynamically adjusts flight attitude and spraying parameters to complete fixed‑point, quantitative on‑demand pesticide application. This paper systematically elaborates the overall architecture, hardware‑software design, and algorithm‑optimization strategies of the scheme, and verifies system performance through field comparative experiments. The test results show that the pest‑and‑disease identification accuracy of this system can reach 96.2%. Its pesticide‑liquid utilization rate is improved by more than 35% compared with the traditional operation mode. It can effectively reduce pesticide pollution and improve the quality and efficiency of plant‑protection operations, and possesses favorable field‑application value and promotion prospects.
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