The leaf area index (LAI) is an important growth indicator that reflects various maize characteristics and can effectively assist in selecting and breeding new maize varieties. The rapid, non-destructive, and accurate determination of maize LAI is very important in maize breeding. At present, unscrewed aerial vehicle (UAV) visible light remote sensing technology has been rapidly developed in applications for obtaining phenotypic information such as crop LAI because of its advantages in obtaining spatial information about crops in the field in a rapid, non-destructive and high-throughput manner; However, the presence of spectral saturation, due to the lack of information on the response mechanism between spectral parameters and phenotypic information, limits the further improvement of the accuracy of models for estimating phenotypic information; Obviously, the PROSAIL radiative transfer model has the advantage of simulating the response mechanism between crop physicochemical parameters and spectral index parameters, which can effectively enhance the potential of crop physicochemical parameter inversion. Therefore, this study combined UAV digital imagery with the PROSAIL model to invert summer maize LAI to further improve the accuracy of the LAI inversion model. Taking summer maize in the maize breeding experimental field as the research object, a UAV remote sensing system was used to obtain high-resolution digital images at the jointing stage, trumpet stage and tassel emergence stage and combined with the PROSAIL model to construct a summer maize LAI inversion model using partial least squares regression (PLSR), random forest regression (RFR) and convolutional neural network (CNN) regression methods. The results show that (1) based on UAV high-resolution digital images, the model constructed by PLSR regression method has the optimal accuracy, and the R², RMSE and nRMSE of the estimation model and validation model are 0.69, 0.37, 24.28% and 0.73, 0.35, 23.26%, respectively; (2) Based on the PROSAIL model, the model constructed using the RFR regression method has the best accuracy, with R², RMSE and nRMSE of 0.98, 0.28, 6.88% and 0.87, 0.64, 15.97% for the estimated and validated models, respectively; (3) Combining the UAV high-resolution digital imagery with the PROSAIL model, the RFR regression method constructed the model with optimal accuracy, and the R², RMSE and nRMSE of the estimation and validation models were 0.98, 0.27, 7.07% and 0.87, 0.65, 16.35%, respectively. The nRMSE of the optimal estimation model and the validation model were reduced by 17.21% and 6.91%, respectively, compared to using only UAV high-resolution digital imagery. The study shows that combining UAV digital imagery with the PROSAIL model effectively improves the accuracy and stability of the LAI inversion model for summer maize, and provides theoretical guidance to assist in selecting and breeding new maize varieties.