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This study aimed to develop empirical pasture dry matter (DM) yield prediction models using an unmanned aerial vehicle (UAV)-borne sensor at four flying altitudes. Three empirical models were developed using features generated from the multispectral sensor: Structure from Motion only (Sf M), vegetation indices only (VI), and in combination (Sf M+VI) within a machine learning modelling framework. Four flying altitudes were tested (25 m, 50 m, 75 m and 100 m) and based on independent model validation, combining features from Sf M+VI outperformed the other models at all heights. However, the importance of Sf M-based features changed with altitude, with limited importance at 25 m but at all higher altitudes Sf M-based features were included in the top 10 features in a variable importance plot. Based on the independent validation results, data generated at 25 m flying altitude reported the best model performances with model accuracy of 328 kg DM/ha. In contrast, at 100 m flying altitude, the model reported an accuracy of 402 kg DM/ha which demonstrates the potential of scaling up this technology at farm scale. The spatial-temporal maps provide valuable information on pasture DM yield and DM accumulation of herbage mass over the time, supporting on-farm management decisions.

remote sensing

The Fusion of Spectral and Structural Datasets Derived from an Airborne Multispectral Sensor for Estimation of Pasture Dry Matter Yield at Paddock Scale with Time