Abstract: Abstract: Wheat powdery mildew is one of the most serious diseases in wheat, so timely and effective monitoring of wheat powdery mildew is necessary for improving wheat yield and quality. The selection of suitable modeling method and feature selection algorithm play an important role in improving the performance of remote sensing monitoring of crop diseases. In this study, the GF-1 remote sensing image was used to extract 14 characteristic variables, including 4 band reflectance variables and 10 vegetation indices. An approach combining relief and minimum redundancy maximum relevance (mRMR) algorithm (relief+mRMR) is proposed for improving the ability to remove redundancy of relief algorithm. First, the relief algorithm was used to calculate the weight of each feature and filter out the disease independent features. Then the mRMR algorithm was used to remove the redundant features. Finally, the optimal feature set including NIR(near-infrared reflectance), SR (simple ratio index) and NDVI (normalized difference vegetation index) was as the input variables. Meanwhile, the other 2 feature sets such as SR, GNDVI (green normalized difference vegetation index) and TVI (triangular vegetation index) by relief algorithm and TVI, RTVI (ratio triangular vegetation index) and RDVI (re-normalized difference vegetation index) by mRMR algorithm were also obtained. Support vector machine (SVM) is a machine learning method based on statistical learning theory, and its working principle is to minimize the structural risk as the core, and it can improve the generalization ability, and resolve problems such as nonlinearity, small samples. Penalty factor and nuclear parameter should be considered in establishing monitoring model for wheat powdery mildew, and traditional parameter selection was mostly through multiple experiments. Presently, the commonly used grid search (GS) algorithm obtains the optimal parameters, but its efficiency is low and workload is large. The advantage of the genetic algorithm (GA) is to solve the global optimal problem, which is robust and can be independent of the domain of the problem when searching quickly. Therefore, the algorithm of SVM optimized by GA (GASVM) was used to monitor wheat powdery mildew in Hebei, China. For comparison and validation, SVM method, the approach of SVM optimized by GS algorithm (GSSVM), and 3 existing monitoring methods of wheat powdery mildew i.e. AdaBoost, particle group optimized least square support vector machine (PSO-LSSVM) and random forest (RF) were also been used. The results illustrated that the performance of models constructed using the feature set through relief+mRMR algorithm outperformed the models established using the feature set through only relief algorithm or only mRMR algorithm. The result demonstrated that the combination of relief and mRMR algorithms can effectively remove the redundancy between features while selecting high correlated features with disease. Additionally, in 3 monitoring models based on the features selected by relief+mRMR, the relief-mRMR-GASVM monitoring model had the highest overall accuracy of 85.7%, which increased by 21.4 and 7.2 percentage points compared with relief-mRMR-SVM and relief-mRMR-GSSVM monitoring models. The result indicated that the relief-mRMR-GASVM approach can effectively improve the monitoring accuracy and consistency of the wheat powdery mildew model, and further strengthen the reliability of the model in practical application. Furthermore, the monitoring accuracy of GF-1 data combined with relief-MRMR-GASVM model increased by 21.4, 14.3 and 7.1 percentage points compared with AdaBoost, PSO-LSSVM and RF methods, respectively. These results reveal that the GF-1 data combined with the relief-mRMR-GASVM model can be used for remote sensing monitoring of wheat powdery mildew.