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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Ackermann, Andrea
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Assmann, Denise
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Aue, Alexander
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Becker, Rolf
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Conrad, Christopher
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Author: search.filters.author.Doerfel, StephanSubject: search.filters.subject.machine learning

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  • Conference Paper
    Exploring AI for interpolation of combine harvester yield data
    (Gesellschaft für Informatik e.V., 2024) Johannsen, Lucas; Ramm, Sebastian; Reckleben, Yves; Doerfel, Stephan
    In 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.
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