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Feature extraction and similarity measurement are two key problems of crop pest recognition based on image processing. The leaves of wheat powdery mildew were treated as the research objects, and an algorithm of wheat leaf disease severity recognition based on elliptical metric learning was proposed. Firstly, a method of moving window maximum (MWM) feature extraction was presented in the algorithm. The HSV color features and LBP texture features were extracted by using the sliding window method from the segmented lesion images. The maximum value of each dimension feature on the same horizontal sliding window was taken as the feature of this horizontal bar. The MWM feature representation method can effectively reduce the influence of curvature, tilt and different shooting angles of wheat leaves on the recognition rate. Then, an elliptical metric with better distinguishability for sample data was introduced, and the elliptic metric matrix was defined based on the log-likelihood ratio of Gaussian distributions on the intrapersonal sample and the extrapersonal sample. In order to maintain the maximal classification information, the feature subspace learning and elliptic metric learning were performed simultaneously. Finally, to recognize the severity of diseases, the elliptic metric was used to calculate the distance between the eigenvectors. The results of comparison experiments showed that the recognition rate of wheat powdery mildew severity was 100%, which was better than 88.33% for SVM method and 90% for BP neural network method. The research result can provide valuable help for the intelligent recognition of crop disease severity.