P344 - 44. GIL-Jahrestagung 2024 - Fokus: Biodiversität fördern durch digitale Landwirtschaft
Permanent URI for this collectionhttps://dl.gi.de/handle/20.500.12116/43863
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Conference Paper Adaptive real-time crop row detection through enhancing a traditional computer vision approach(Gesellschaft für Informatik e.V., 2024) Hussaini, Mortesa; Voigt, Max; Stein, AnthonyCrop row detection is important to enable precise management of fields and optimize the use of resources such as fertilizers and water. Autonomous machines need an effective but also robust real-time row detection system to be able to adapt to different field conditions. In this paper, we present an enhanced crop row detection approach which integrates traditional computer vision methods with further techniques such as k-means clustering or probabilistic Hough transformation. The resulting hybrid method allows for efficient and robust detection of straight and curved crop rows in image and video material. We validate our approach empirically on the crop row benchmark dataset (CRBD) and compare it with other state-of-the-art approaches. Furthermore, we demonstrate that our approach is designed to be adaptive and thus becomes straightforwardly transferable to other experimental setups. To corroborate that, we report on results when our approach is validated on representative corner cases which have been collected in the scope of a research project. Observations and current limitations of our approach are discussed along with possible solutions to overcome them in future work.Conference Paper Exploring AI for interpolation of combine harvester yield data(Gesellschaft für Informatik e.V., 2024) Johannsen, Lucas; Ramm, Sebastian; Reckleben, Yves; Doerfel, StephanIn the wake of eco-schemes introduced by the EU's Common Agricultural Policy, this study evaluates AI-based interpolation methods for generating yield maps as one component of a decision support system, aiding farmers in eco-scheme implementation. The research contrasts ordinary Kriging (OK) with AI techniques – Random Forest (RF) enhanced with spatial fea-tures (RFsp), covariates (RFspco) and DeepKriging (DK), utilizing combine harvester yield data. Performance metrics show AI, especially RF variants, surpassing OK. For a 0.7 split, R² were 0.6 (OK), 0.77 (RF), 0.81 (RFsp), 0.78 (DK); MSE were 0.6 (OK), 0.34 (RF), 0.28 (RFsp), 0.32 (DK). Spatial features boosted accuracy, while incorporating Terrain Models had no rele-vant impact on the results. These findings are crucial for an automated, accurate decision support system, facilitating eco-scheme adoption for farmers. The efficiency of AI methods underscores their potential in promoting sustainable, informed agricultural practices.Conference Paper Organic sugar beet (Beta vulgaris L.) cultivation using the field robot Uckerbot as a system for sustainable farming(Gesellschaft für Informatik e.V., 2024) Steinherr, Leonie; Belo, Miguel; Trappe, Rodja; Acosta-Ortiz, Dafne; Birkmann, Amanda; Krachunova, Tsvetelina; Bloch, RalfThe field robot Uckerbot is an autonomous mobile robot developed in a co-design process by farmers, industry and researchers for intra-row weed control in organic sugar beets. First on-farm results indicated better performance of the Uckerbot compared with common weed hoeing strategies. The robot showed 90% accuracy of sugar beet detection and 88% weed efficiency with a drill mechanism. Further development of the robot includes enabling it to work in a robot swarm for an increase in efficiency and working speed and enabling the image recognition system to distinguish between different weed types. This will allow the Uckerbot to skip tolerable wild field herbs for increased biodiversity.
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