Abstract: Monitoring the maturity of multi-species maize based on remote sensing and thus mastering the optimal harvesting time is crucial for improving its yield and quality. The traditional method to monitor the maturity progress of maize is to use field surveys, and the disappearance of the kernel "milkline" is usually taken as a sign of maturity. However, the traditional field survey method is a labor-intensive activity that is not conducive to high-throughput field monitoring. Therefore, this study aims to construct a maize maturity index (MMI) to quantify the maturity of maize and monitor it through UAV multispectral monitoring, so as to grasp the dynamics of maize maturity stage in the field. Firstly, the UAV platform was used to acquire multispectral images at five time points of the maize maturity stage, and ground-based measured data such as the percentage of milkline, kernel water content and leaf chlorophyll content were collected accordingly. Secondly, based on the weighted analysis of the measured data, the MMI was constructed. Finally, based on the MMI and the vegetation index, a model was constructed using regression models and random forests to realize the UAV multispectral monitoring of corn maturity, and the effects of different varieties on MMI were analyzed. The results showed that: 1) for different varieties of maize at maturity stage, there were differences in the change patterns of leaf chlorophyll content and kernel water content, the leaf chlorophyll content and kernel water content of Zhengdan 958 and Jingjiuqingzhu16 were always higher than that of Jiyuan 1 and Jiyuan 168, while the rate of decline of leaf chlorophyll content and milkline percentage of two varieties of maize at maturity stage was lower than that of Jiyuan 1 and Jiyuan 168. 2) The correlations between MMI and selected vegetation indices in the experiment could reach 0.01 significant level, among which the correlations with normalized difference vegetation index (NDVI) and transformed chlorophyll absorbtion ratio index (TCARI) were highest with correlation coefficients above 0.87, in addition, the wide dynamic range vegetation index (WDRVI) has the most obvious changes, and the variance fluctuates less, which is similar to MMI. 3) The study was verified based on data sets of different combinations. Among them, the random forest model has the highest estimation accuracy of MMI. The test set coefficient of determination (R²) is 0.84, and the root mean squared error (RMSE) is 8.77%, and the normalized root mean squared error (nRMSE) is 12.05%. In the revalidation scheme 2-1, the RF model test set has high accuracy, in which R² is 0.65, RMSE is 13.02%, nRMSE is 19.17%. In addition, the random forest model has better estimated accuracy of different varieties of MMI. The Jingjiuqingzhu 16 has the best accuracy. Among them, R², RMSE, and nRMSE are 0.76, 10.67%, and 15.88%. The model accuracy proves that the drone platform can be monitored to monitor the maturity of different varieties of corn. The results of the research can provide a reference for the dynamic changes in multi-spectrum drones to monitor the dynamic changes in multi-variety of corn in farmland.