Exploring AI for interpolation of combine harvester yield data
| dc.contributor.author | Johannsen, Lucas | |
| dc.contributor.author | Ramm, Sebastian | |
| dc.contributor.author | Reckleben, Yves | |
| dc.contributor.author | Doerfel, Stephan | |
| dc.date.accessioned | 2024-04-08T11:56:37Z | |
| dc.date.available | 2024-04-08T11:56:37Z | |
| dc.date.issued | 2024 | |
| dc.description.abstract | 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. | en |
| dc.identifier.isbn | 978-3-88579-738-8 | |
| dc.identifier.pissn | 1617-5468 | |
| dc.identifier.uri | https://dl.gi.de/handle/20.500.12116/43925 | |
| dc.language.iso | en | |
| dc.publisher | Gesellschaft für Informatik e.V. | |
| dc.relation.ispartof | 44. GIL - Jahrestagung, Biodiversität fördern durch digitale Landwirtschaft | |
| dc.relation.ispartofseries | Lecture Notes in Informatics(LNI) - Proceedings, Volume P - 344 | |
| dc.subject | artificial intelligence | |
| dc.subject | machine learning | |
| dc.subject | yield map | |
| dc.subject | spatial prediction | |
| dc.subject | interpolation | |
| dc.title | Exploring AI for interpolation of combine harvester yield data | en |
| dc.type | Text/Conference Paper | |
| gi.citation.endPage | 118 | |
| gi.citation.publisherPlace | Bonn | |
| gi.citation.startPage | 107 | |
| gi.conference.date | 27.-28. Februar 2031 | |
| gi.conference.location | Stuttgart | |
| gi.conference.review | full |
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