| Sumario: | Hyperspectral remote sensing using Unmanned Aerial Vehicles (UAVs) provides accurate, near real-time, and large-scale spatial estimation of the leaf area index (LAI), a significant crop variable for monitoring crop growth. In the present study, the LAI of wheat crops was estimated using high-resolution UAV-borne hyperspectral data. The PLS (Partial Least Squares) regression combined with the VIP (Variable Importance in the Projection) was used for selecting the optimum indices as feature vectors to the Extreme Gradient Boosting (Xgboost) model for predicting LAI. Twelve of twenty-seven vegetation indices were selected to develop the prediction model. On validation against the in situ measured LAI values, the prediction model shows good accuracy with an R2 of 0.71. The model was used to generate a spatial map showing the variability of the LAI. Accurate mapping of LAI from high-resolution hyperspectral UAV data using machine learning models facilitates near-real-time monitoring of crop health.
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