25 feature parameters were extracted from chlorophyll fluorescence image of pepper leaf, including 18 parameters which was significantly correlated with the nitrogen content at the 0.01 level. Principal component analysis (PCA) was used to extract the main parameters as input variables of genetic algorithm to optimize back–propagation artificial neural network (BPNN), generalized regression neural network (GRNN) and multiple linear regression (MLR), to establish the forecast model of hot pepper leaf nitrogen content, respectively. The correlation coefficient of three model set were 0.959 2, 0.963 3, 0.943 5, and correlation coefficient of prediction set were 0.914 5, 0.821 3, 0.774 1, respectively.