(43. GIL-Jahrestagung, Resiliente Agri-Food-Systeme, 2023) Manss, Christoph; von Szadkowski, Kai; Bald, Janis; Richard, David; Scholz, Christian; König, Daniel; Ruckelshausen, Arno
This 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.