Nitrogen (N) is an essential nutrient for crop growth. The rapid and non-destructive monitoring of N nutrition in crops through remote sensing is important for the accurate diagnosis and quality evaluation of crop growth status. Leaf nitrogen concentration (LNC), which has been widely utilized in remote sensing, serves as a crucial indicator for the monitoring of crops growth status. In this study, the first-derivative fluorescence spectrum (FDFS) based on laser-induced fluorescence (LIF) was proposed for LNC estimation in paddy rice. First, the correlation between the LNC and FDFS at each wavelength was analyzed in detail using different excitation light wavelengths (ELWs; 355, 420, and 556 nm). Then, FDFS was used as an input parameter to train a back-propagation neural networks (BPNN) model for LNC estimation. The coefficients of determination (R2) of the linear regression analysis between the measured and predicted LNC were 0.823, 0.743, and 0.837, corresponding to 355, 420, and 556 nm ELWs, respectively. Second, the principal components analysis was performed for the extraction of the main characteristics of FDFS, and the calculated variables were used for LNC inversion. The R2 values were 0.891, 0.815, and 0.907 for 355, 420, and 556 nm ELWs, respectively. In addition, the correlation between the ratio of FDFS and LNC was also analyzed, which can provide a reference for the selection of optimal wavelengths for LNC monitoring. The experimental results exhibited the promising potential of FDFS combined with multivariate analysis for LNC monitoring, which can allow additional fluorescence characteristics to improve the accuracy of LNC monitoring.