Abstract: In order to improve the identification rate of soybean seedling morphology at true leaf stage（V1） and compound leaf stage（V2） under large-scale and high-throughput conditions, and to lay a foundation for judging seedling emergence rate and uniformity of seedling stage, a soybean seedling identification method based on ultra-low altitude UAV-based imagery was proposed.The method was based on the RGB image of the quadrotor UAV acquired, which was converted into HSV color space to extract the soybean seedling in the image, and used K-means find the best threshold, V1 point coordinates of（83,183,201）,（31,25,74）, and V2 point coordinates of（79,215,225）,（29,72,61）.The soybean seedling recognition model was obtained based on the extracted soybean seedling image training deep learning network.Tests results showed that the model could effectively identify soybean seedlings in the two key stages of V1 stage and V2 stage, and the identification accuracy reached 92.68% and 90.09%.The results showed that the soybean seedling stage judgment task based on ultra-low altitude UAV image could be effectively completed in the field and timely guide the soybean planting management decision.