To further improve the ability of UAV remote sensing to rapidly monitor the leaf area index (LAI) of winter wheat under mulching conditions, a UAV with a five-channel multispectral sensor was used to acquire remote sensing image data of winter wheat during the emergence, overwintering, rejuvenation, plucking, tasseling and filling stages from 2021 to 2022, using supervised classification to remove background and calculate 50 visible and near-infrared vegetation indices. The LAI inversion models of mulched winter wheat with different input feature variables were developed and evaluated in terms of accuracy by using six machine learning algorithms: partial least squares, ridge regression, support vector machine, random forest, extreme gradient boosting and artificial neural network. The results showed that removing the mulched background would make the reflectance of winter wheat canopy closer to the real value and improve the inversion accuracy. The inversion accuracy and stability of mulched winter wheat LAI can be improved by using a suitable feature reduction method combined with machine learning algorithm, and the inversion accuracy before feature reduction cannot be optimized by principal component analysis and correlation coefficient method, and the decision tree ranking was only applicable to random forest and extreme gradient boosting algorithm based on tree model, and the optimization effect of genetic algorithm was obvious, genetic algorithm-artificial neural network model inversion effect reached the optimal (R2 was 0.80, RMSE was 1.10, MAE was 0.69, and deviation was 1.25%). The research results can provide theoretical reference for UAV remote sensing to monitor the growth condition of mulched winter wheat.