The estimation of rice leaf nitrogen content is important to achieve the goals of high rice yield and efficient fertilization in the field. In this paper， we propose a Particle Swarm Optimization-Deep Forest （PSO-DF） model-based method for estimating the nitrogen content of rice leaves， which determines the number of estimation layers in the optimal cascade and the optimal estimator in the Deep Forest （DF） model parameters by a particle swarm optimization algorithm. The number of trees in the optimal estimator is determined by the particle swarm optimization algorithm to improve the regression accuracy of the DF model on Rice datasets.To verify the effectiveness of PSO-DF， this paper used an unmanned aircraft with a hyperspectral image collector to obtain hyperspectral images of Ningxia japonica rice， and sampled， measured， and analyzed the rice leaves at the same period， and extracted the three feature bands with the highest correlation coefficients with rice leaf nitrogen content， which were used as spectral features for inversion with rice nitrogen content data， and compared PSO-DF， the original model DF， and six other common The rice nitrogen content estimation models constructed by machine learning algorithms were compared. The results show that the model constructed by the PSO-DF algorithm outperforms the other models， and its R² and RMSE indexes are significantly better than those of the other models.