COUPLING UNMANNED AERIAL VEHICLE (UAV) MULTISPECTRAL IMAGERY AND INTEGRATED LEARNING TO CONSTRUCT A MONITORING AND PREDICTION MODEL FOR RELATIVE CHLOROPHYLL CONTENT (RCC) AND LEAF AREA INDEX (LAI) OF SORGHUM IN FIELDS

This study mainly investigates the feasibility of monitoring and estimating the RCC (Relative Chlorophyll Content) and the LAI (Leaf Area Index) of sorghum by coupling integrated learning model with UAV multispectral image, clarifies the quantitative relationship between RCC and LAI of sorghum and the vegetation index based on different spatial resolutions, and constructs a Monitoring and prediction model for the RCC and the LAI of sorghum based on the UAV multispectral image and the vegetation index at different spatial resolutions. The model constructed based on integrated learning, and using the stacking approach had good prediction accuracies at three spatial resolutions, with the stacking model predicting R2=0.87, MAE=18.27, and RMSE=22.23 for the RCC at spatial resolution of 0.017 m; R2=0.86, MAE=17.38, and RMSE=23.21 for RCC at spatial resolution of 0.024 m; R2=0.80, MAE=18.62, and RMSE=24.12 for RCC at spatial resolution of 0.030 m; R2=0.93, MAE=0.34, and RMSE=0.37 for LAI at spatial resolution of 0.017 m; and R2=0.89, MAE=0.44, and RMSE=0.55 for LAI at spatial resolution of 0.024 m. The model established by combining the vegetation index and integrated learning can quickly and accurately monitor and predict RCC and LAI of sorghum, which provides a scientific methodology and theoretical basis for scientific monitoring and predicting RCC and LAI of sorghum in the field.