Leaf area index (LAI) is a significant phenotypic parameter to characterize maize growth information. Accurate estimation of LAI under disease stress using UAV with multispectral camera is important for phenotypic research and breeding engineering. Maize disease is a common problem in the process of germplasm resources identification and breeding. The remote sensing estimation of maize LAI under the condition of disease occurrence needs to be considered. Firstly, the LAI data of different disease severities (grade 1~9) were obtained from 41 maize inbred lines with different disease resistances by artificial inoculation. The leaf-scale ASD FieldSpec PRO 4 hyperspectral data of maize were collected to classify the disease resistance of different maize germplasm resources. The hyperspectral indexes related to leaf nitrogen, chlorophyll and specific leaf weight were constructed, and the recognition models of different maize disease grades were developed. To identify different maize disease grades, the hyperspectral indexes relating to leaf nitrogen, chlorophyll, and specific leaf weight were developed and used in the recognition models. Four integrated learning models, K-nearest neighbours (KNN), support vector machine (SVM), gradient boosting decision tree (GBDT) and decision tree (DT) were constructed to classify the disease resistance of different maize germplasm resources. Multi-spectral images were obtained from the Rededge-MX5 multispectral camera carried on DJI Matrice M600 Pro (UAV) at different stages of maize growth. Based on red edge and near infrared bands, a variety of vegetation indices were constructed to estimate the maize LAI. According to maize disease recognition model, different disease stress levels were classified and identified. The robustness of LAI estimation model under different disease stress was discussed by using the four integrated machine learning models of random forest regression (RFR), extreme gradient boosting (XGBoost), light gradient boosting machine (LightGBM), and GBDT. The results showed that based on the ground ASD hyperspectral recognition accuracy of OA: 84.72% (GBDT), 47.67% (SVM), 55.05% (KNN) and 83.02% (DT), respectively, according to the classification of maize disease degree in different growth stages. The early stages of maize infection were difficult to distinguish from healthy leaves due to the mild symptoms of the disease. As the severity of maize disease was increased, the difference between healthy leaves and diseased spots in images was gradually increased, resulting in higher accuracy than maize early growth stage. In order to estimate the LAI for maize under different disease grades, UAV multi-spectral image data were used based on the results of disease classification. Four integrated learning models were constructed to estimate the LAI of different disease grades. The result accuracy (rRMSE) of the LAI estimation model was 19.11% (GBDT), 15.94% (LightGBM), 14.51% (RFR) and 15.45% (XGBoost), respectively. The XGBoost model had the best estimation result, and the accuracy of rRMSE: 15.19% (Mild), 17.46% (Moderate), 9.12% (Severe) and 9.63% (No resistant) under different disease grades.The LAI estimation model generally had little difference in the estimation accuracy of maize LAI in the early and middle stages of the disease (disease grade 1~7). The accuracy of maize LAI estimation results was quite different for maize samples with extremely serious disease, and disease grades in the field would affect the accuracy of maize LAI estimation.