In order to improve the overfitting problem of small sample data and improve the generalization ability and prediction accuracy of the wheat stripe rust remote sensing monitoring model， this paper uses the Solar Induced Chlorophyll Fluorescence （SIF） in the canopy obtained by the Chinese Academy of Agricultural Sciences Experimental Station in 2018 as the data source， The Cost Complexity Pruning （CCP） algorithm is used to prune the Random Forest Regression （RFR） method， and the Bayesian Optimization （BO） algorithm is used to select hyperparameter for random forest regression， and a prediction model of wheat stripe rust severity based on the constrained random forest regression （CO-RFR） algorithm is Constructed， And compare the accuracy of the remote sensing monitoring model for wheat stripe rust with the Classification And Regression Tree （CART） algorithm， traditional RFR algorithm， and Multiple Linear Regression （MLR） method. The results indicate that： （1） The CO-RFR model has the highest estimation accuracy and is more suitable for monitoring the severity of wheat stripe rust under small sample data. Among them， in the validation dataset， the average RMSE between the Severity Level （SL） predicted by the CO-RFR model and the measured SL was reduced by 43%， 50%， and 40%， respectively， compared to the RFR， CART， and MLR models， and the average R² was increased by 56%， 47%， and 40%， respectively. （2） Adding constraints can effectively improve the overfitting phenomenon of the model and enhance its generalization ability. Among them， the average RMSE between the predicted SL value and the measured SL value in the RFR model training set decreased by 62% compared to the validation set， indicating that the accuracy of the model training set was much higher than that of the validation set， and the model showed overfitting. However， the average RMSE between the predicted SL value and the measured SL value in the CO-RFR model training set decreased by 8% compared to the validation set， indicating that the model fitting effect was good and the overfitting phenomenon was significantly improved. eat stripe rust disease under small sample data， and also provides application reference for stress monitoring of other crops.