Sutter, MichaelMichaelSutterAebischer, PhilippePhilippeAebischerReidy, BeatBeatReidy2024-11-192024-11-192021-05-17978-3-00-068789-110.24451/arbor.15032https://doi.org/10.24451/arbor.15032https://arbor.bfh.ch/handle/arbor/43794A prerequisite for efficient pasture management is the regular estimation of the dry matter yield (DMY) by means of a rising plate meter (RPM). With the latest generation of unmanned aerial vehicles (UAV) equipped with a real-time kinematic (RTK) positioning system and a multispectral camera, it should be possible to measure sward heights and to estimate dry matter yields. To investigate this possibility, we developed an algorithm enabling a digital terrain model to be calculated from the digital surface model of grassland. DMY is estimated using a random forest estimator. Initial estimates at a previously unseen site achieved a root-mean-square error (RMSE) of 332 kg DM ha-1. The results demonstrate that UAVs enable DMY predictions with an accuracy level close to RPM measurements. The underlying algorithm will be further developed and adapted to a wider variety of pasture types and meadows.engrasslandmachine learningrandom forestNDVIremote sensingdry matter yieldQCS1SBconference_item