Auflistung nach Autor:in "Bald, Janis"
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- KonferenzbeitragComparison of UAV- and mowing machine-mounted LiDAR for grassland canopy height estimation(44. GIL - Jahrestagung, Biodiversität fördern durch digitale Landwirtschaft, 2024) Bracke, Justus; Storch, Marcel; Bald, Janis; Jarmer, ThomasTowards autonomous process monitoring, canopy height estimation in grassland based on data from a mowing machine-mounted LiDAR and a UAV-LiDAR system is compared to manually measured ground truth heights. In a field trial, a LiDAR mounted on the cabin roof of the mowing machine recorded data during the mowing process, while two recording flights before and after the mowing were conducted with a UAV-LiDAR. The data from both systems were processed similarly and parameters such as height estimation method, spatial resolution and percentile filters were systematically varied to investigate their influence on height estimation accuracy. Statistical evaluation showed that canopy height estimates based on the UAV-LiDAR (R² = 0.89, RMSE = 0.05 m) were more accurate and precise than those based on the mowing machine-mounted LiDAR (R² = 0.51, RMSE = 0.08 m). The influence of the different investigated parameters varied.
- KonferenzbeitragTowards selective hoeing depending on evaporation from the soil(43. GIL-Jahrestagung, Resiliente Agri-Food-Systeme, 2023) Manss, Christoph; von Szadkowski, Kai; Bald, Janis; Richard, David; Scholz, Christian; König, Daniel; Ruckelshausen, ArnoThis paper presents how to generate an artificial dataset to test different hoeing rules. Therefore, images that have been obtained on two days of a field trial are analysed to infer weed and crop sizes. Then, weather data from 2021 and 2022 is gathered from open-source data for 100 synthetically generated fields. The generated dataset is then used to test hoeing rules that are conditioned to keep as much moisture in the soil as possible. The analysis with these hoeing rules indicates that much less hoeing would be applied if the proposed hoeing rules are used.