For the rapid detection of leaf nitrogen content of summer corn, visible and near infrared (Vis/NIR) spectra of summer corn leaves, with different nitrogen levels at spinning stage, were measured by an ASD FieldSpec. Discrete approximation wavelet coefficient vectors of the second-scale were obtained via logarithmic transformation and multi-scale wavelet decomposition of the spectra data within “near infrared spectrum platform” (760~1000nm) and “green peak” (450~ 680nm). Then principal components (PCs) were selected from these vectors by principal component analysis (PCA), and used as inputs of a generalized regression neural network (GRNN). The model was employed for the prediction of leaf nitrogen content of summer corn. Results show that logarithmic transformation can highlight the differences in the spectral response of summer corn leaves with different level of nitrogen within “near infrared spectrum platform” and “green peak” at spinning stage. The wavelet-based PCs can manifest the changes in the spectra of summer corn leaves with different nitrogen levels. Trained GRNN model with wavelet-based PCs as inputs can predict leaf nitrogen content of summer corn. The model is reliable and practicable.