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Leaf area index (LAI) is one of the important indicators for crop growth monitoring and yield prediction. In order to explore the potential of wheat LAI estimation models-based on UAV multispectral technology, taking wheat breeding materials as the research object, the multispectral images were obtained-based on the UAV platform at jointing, booting, heading and flowering stages of wheat, and further calculated 12 vegetation indices (VI) and eight types of texture features (TF) in each band. Then, the Pearson correlation analysis method was employed to identify VI and TF which strongly correlated with LAI, and the recursive feature elimination method (RFE) was used to screen the comprehensive features (CF) on the bases of the preferred two types of features. Finally, based on the three types of features, three machine learning algorithms including multiple linear regression (MLR), support vector regression (SVR) and gradient boosting regression (GBR) were employed to establish LAI estimation models, and the estimation accuracy of the models was compared at different growth stages. The results showed that the CF effectively improved the accuracy of wheat LAI estimation models at each growth stages;among the three machine learning algorithms, GBR performed greater stability, and had better LAI fitting for the three types of features;specifically, the LAI estimation model-based on GBR, using vegetation indices RVI, NDVI, and texture features NIR_COR, R_MEA as input variables, performed best, with R2 of 0.91 and RMSE of 0.45 in the training set, R2 of 0.84 and RMSE of 0.67 in the testing set for all stages. The research result can provide an application reference for LAI estimation of wheat-based on multispectral technology.