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In order to accurately and rapidly predict citrus yield to precisely guide orchard production management, remote sensing image data of citrus fruit ripening stage was obtained by DJI multispectral version of UAV, and visible and multispectral band indices were extracted as feature variables from the images. The eXtreme gradient boosting (XGB), random forest (RF) and support vector machine (SVM) model were used to construct citrus fruit presence and absence classification model, fruit number and quality estimation model, respectively. The results showed that the excess red index was the most important in the classification of citrus fruit presence and absence while the modified excess green index was the most important in the estimation of number and quality through the screening analysis of feature variables by the XGB model. All three models in combination modeling had better accuracy in combination 4. For the classification model, the optimal model was the SVM model with AUC of 0.969 and accuracy of 0.919.While the XGB model was the best model for estimating both number and quality,with the number estimation model’s R2 value being 0.79 and RMSE being 466, and the quality estimation model’s R2 value being 0.79 and RMSE being 19.51kg. Finally, the Shapley additive explanations (SHAP) method was utilized to reveal the importance of vegetation index features in the construction of the yield estimation model and to elucidate the interaction effects of the features with the top four SHAP values. The research results can provide an application reference and theoretical basis for the research of UAV remote sensing in citrus yield.