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Exploring AI for interpolation of combine harvester yield data

Kurzanzeige
dc.contributor.authorJohannsen, Lucas
dc.contributor.authorRamm, Sebastian
dc.contributor.authorReckleben, Yves
dc.contributor.authorDoerfel, Stephan
dc.date.accessioned2024-04-08T11:56:37Z
dc.date.available2024-04-08T11:56:37Z
dc.date.issued2024
dc.description.abstractIn 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.en
dc.identifier.doi10.18420/giljt2024_17
dc.identifier.isbn978-3-88579-738-8
dc.identifier.issn2944-7682
dc.identifier.pissn1617-5468
dc.identifier.urihttps://dl.gi.de/handle/20.500.12116/43925
dc.language.isoen
dc.publisherGesellschaft für Informatik e.V.
dc.relation.ispartof44. GIL - Jahrestagung, Biodiversität fördern durch digitale Landwirtschaft
dc.relation.ispartofseriesLecture Notes in Informatics(LNI) - Proceedings, Volume P - 344
dc.subjectartificial intelligence
dc.subjectmachine learning
dc.subjectyield map
dc.subjectspatial prediction
dc.subjectinterpolation
dc.titleExploring AI for interpolation of combine harvester yield dataen
dc.typeText/Conference Paper
gi.citation.endPage118
gi.citation.publisherPlaceBonn
gi.citation.startPage107
gi.conference.date27.-28. Februar 2024
gi.conference.locationStuttgart
gi.conference.reviewfull

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