Logo des Repositoriums

Auflistung nach Autor:in "Manss, Christoph"

1 - 2 von 2
  • Konferenzbeitrag
    Assessment of ground conditions in grassland on a mower with artificial intelligence
    (44. GIL - Jahrestagung, Biodiversität fördern durch digitale Landwirtschaft, 2024) Manss, Christoph; Martel, Viktor; Weisgerber, Roman
    Process-monitoring for autonomous mowers in agriculture is crucial to establish an online quality assessment. Here, neural networks (NNs) are employed to classify ground conditions, distinguishing between dry, mowed, unplanted, and grass. The data comprises RGB images that are captured by a camera mounted on a mower. These images are then used to train various NNs, with EfficientNet_V2_s emerging as the most accurate network and with ResNet18 to be the most efficient network in terms of training duration and accuracy. The study also reveals for this use-case that employing transfer learning enhances the overall network performance. The developed NNs is intended for deployment on mowers, enabling them to adjust their mowing blades, conserve energy, and enhance the quality of mowed grass. Beyond mowing, the NN can be applied in process control and the identification of other plant species or weeds in the agricultural field, contributing to biodiversity assessments and more sustainable farming practices.
  • Konferenzbeitrag
    Towards 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, 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.