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Based on the UAV multi-spectral image, the summer corn vegetation index was extracted as a feature variable, and the Pearson correlation coefficient combined with the random forest algorithm was used to reverse the verification weight method for feature selection and redundant features were removed. Random forest, gradient boosting tree, support vector machine and ridge regression were used as the primary learner, and ridge regression was used as the secondary learner to establish a summer corn coverage estimation model based on Stacking ensemble learning, and 5-fold cross-validation was used to further improve model generalization ability, a combination of random search and grid search was used to optimize model hyper parameters, four regression indicators were used for model accuracy evaluation, and the following year’s data was used to verify its robustness. The experimental results showed that compared with a single model and decision tree, Xgboost, Adaboost, and Bagging integrated framework, the Stacking integrated learning model had higher accuracy and stronger robustness. The R2 was 0.9509, which was an average improvement of 0.0369 than that of the single model. Compared with other integrated models, the average increase was 0.0417;RMSE, MAE and MAPE were 0.0432, 0.0330 and 5.01%, respectively, which were 0.0138, 0.0130 and 2.14 percentage points lower than that of the single model, and 0.0185, 0.0126 and 2.15 percentage points lower than that of the other integrated models. The research result provided a method and effective support for the estimation of summer corn coverage.