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Efficient and timely acquisition of crop growth information plays an important role in crop production management. At present, crop growth monitoring in small areas is mostly achieved through the inversion of spectral information from UAV. However, further research is needed to comprehensively consider the surface feature information of crops at different growth stages for monitoring crop growth in small areas. Taking winter wheat as the research object, comprehensive growth monitoring indicators (CGMI) was constructed based on the plant height and leaf area index (LAI) of winter wheat according to the coefficient of variation method, and a comprehensive growth monitoring method was proposed for winter wheat that combining UAV spectral information and texture features. A drone equipped with a multispectral lens was used to acquire images of winter wheat in four growth stages, and 12 vegetation indices and 8 types of texture features in each band were obtained. The Person correlation analysis method was used to screen the vegetation index and texture features that had good correlation with CGMI, and then random forest regression (RF), partial least squares regression (PLSR) and support vector regression (SVR) methods were used to construct growth monitoring models based on vegetation index and growth monitoring models based on vegetation index and texture features, respectively. Through comparison, the superior growth monitoring model was obtained, and finally the spatial distribution information of winter wheat growth in the study area was obtained. The results showed that among the three machine learning methods, the SVR growth monitoring model based on vegetation index and texture features had the highest accuracy (training set R2 was 0.789, MAE was 0.03, NRMSE was 4.8%, RMSE was 0.04). Compared with SVR growth monitoring model based on vegetation index, the coefficient of determination of this model was increased by 5.1%, the average absolute error was decreased by 3.3%, the standard root mean square error was decreased by 8.3%, and the root mean square error was decreased by 10%. The research results showed that the method was accurate and reliable, which can provide an important reference for winter wheat growth monitoring.