Abstract:Plant protection unmanned aerial vehicles (UAVs) have achieved a high degree of automation in spraying agricultural chemicals, yet the process of agricultural chemical replenishment remains largely dependent on manual operation, which limits overall operational efficiency. Factors such as global navigation satellite system (GNSS) positioning inaccuracies, terrain variations, and sudden crosswinds often cause significant deviations between the actual landing point and the predefined target when the UAV returns autonomously. These deviations hinder precise docking with an automatic replenishment device. To overcome this challenge, a vision-based landing position correction system was presented for plant protection UAVs’ automatic agricultural chemicals replenishment. The system employed an OV7725 complementary metal oxide semiconductor (CMOS) imaging sensor to capture real-time images during the UAV’s landing phase. A series of image processing steps were applied, including color space conversion from red green blue (RGB) to hue saturation lightness (HSL) to improve illumination invariance, threshold segmentation for preliminary detection, and a morphological erosion algorithm for accurate boundary extraction and center localization of the UAV. An STM32 microcontroller computed the positional offset between the detected UAV center and the desired landing coordinates. Experimental validation showed that the proposed system achieved a recognition accuracy of 93.25% for the plant protection UAV and attained an average positioning error of 1.87 cm. The results confirmed the system’s capability to enable accurate alignment between the UAV and an automated liquid replenishment unit. This research offered a viable and precise technical solution for automated chemical replenishment, contributing to enhanced operational intelligence and efficiency in precision aerial agriculture. The proposed approach demonstrated strong potential for practical implementation in modern agricultural aviation systems.
