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Scientific and accurate prediction of the incidence of wheat stripe rust is of great significance for its precise control. Reflectance data can detect crop biochemical parameters, while chlorophyll fluorescence has obvious advantages in photosynthetic physiological diagnosis. In order to improve the detection accuracy of wheat stripe rust and determine the sensitive factors and suitable algorithms for detecting the severity of wheat stripe rust by remote sensing, two feature selection algorithms, filters and wrappers were used to select solar-induced chlorophyll fluorescence and visible light absorption features of wheat stripe rust of different severity. Firstly, the absorption features and SIF data were calculated. Then, the genetic algorithm (GA) and support vector regression (SVR) wrapping method were used to select sensitive features of wheat stripe rust. For comparison, the correlation coefficient method of filter method for feature selection was also used. Finally, GA-SVR model and CC-SVR model for predicting the severity of wheat stripe rust were established by using the characteristics selected by the two methods. The results showed that the GA-SVR model constructed with the combined features of GA and SVR algorithms had better accuracy than that of the CC-SVR model. The verification results of the plot experiment data showed that the determination coefficient between the predicted disease index (DI) and the measured DI of the GA-SVR model in the three sample groups was at least 2.7% higher than that of the CC-SVR model, and the root mean square error was at least 10.1% lower than that of the CC-SVR model. The field survey data verification results also confirmed that using GA-SVR algorithm to optimize the sensitive factors for wheat stripe rust remote sensing detection and model construction can improve the accuracy of wheat stripe rust remote sensing detection. The research results provided a new idea for further realizing large-scale high-precision remote sensing monitoring of crop health status.