 This research developed a system for the rapid estimation of May's seedling emergence using deep learning algorithms. It was found that the system had good prediction performance for May's seedling count with an average R2 value of 0.96 and an accuracy of 92%. However, the prediction accuracy reduced significantly when the planting density was above 90,000 plants slash Ha. Additionally, the distribution characteristics of seedling emergence and growth were calculated based on the average value and variation coefficient of seedling spacing, seedling area, and seedling length. This article was authored by Ming Walu, Wenhao Su, and Shiqing Wang.