Low‐cost visible and near‐infrared camera on an unmanned aerial vehicle for assessing the herbage biomass and leaf area index in an Italian ryegrass field

Automated monitoring systems with different temporal and spatial resolutions can achieve precision agriculture management. Unmanned aerial vehicle (UAV) systems open new possibilities for effectively characterizing the variability within cropping systems with high spatial and temporal resolution. In this study, a UAV with a low‐cost visible and near‐infrared camera assessed the spatial variability in the herbage biomass (BM) and leaf area index (LAI) in an Italian ryegrass field. Using multiple linear regression (MLR) models, high coefficients of determination (R2) and low root‐mean‐squared error (RMSE) values were obtained between the observed and predicted herbage BM (R2 = 0.84, RMSE = 90.43 g m−2) and LAI (R2 = 0.88, RMSE = 0.82). The MLR models successfully recovered high‐resolution spatial distributions of the herbage BM and LAI from the ortho‐photos. The reconstructed maps verified that the proposed method can effectively characterize spatial field variations and assess forage growth to optimize field‐level forage crop management.