Understanding Agricultural Landscape Dynamics with Explainable Artificial Intelligence
| dc.contributor.author | Stiller, Stefan | |
| dc.contributor.editor | Stolzenburg, Frieder | |
| dc.date.accessioned | 2023-09-20T04:20:43Z | |
| dc.date.available | 2023-09-20T04:20:43Z | |
| dc.date.issued | 2023 | |
| dc.description.abstract | Deep learning (DL) models, particularly those utilizing computer vision techniques such as proximal and remote sensing imagery, have witnessed extensive utilization within agriculture [KPB18]. These DL applications encompass diverse areas, including land cover and crop type mapping [Ku17], crop yield estimation [KS15, NNL19], drought monitoring [Sh19], plant disease spread analysis [Te20], and overall monitoring of agricultural systems. DL applications offer significant potential to enhance agricultural practices at various scales, spanning from individual organisms, field, landscape, to regional and even continental scales [Ry22b]. | en |
| dc.identifier.doi | 10.18420/ki2023-dc-09 | |
| dc.identifier.uri | https://dl.gi.de/handle/20.500.12116/42397 | |
| dc.language.iso | en | |
| dc.pubPlace | Bonn | |
| dc.publisher | Gesellschaft für Informatik e.V. | |
| dc.relation.ispartof | DC@KI2023: Proceedings of Doctoral Consortium at KI 2023 | |
| dc.subject | deep learning; precision agriculture; crop yield; explainable artificial intelligence; spatial cross validation; self-supervised learning | en |
| dc.title | Understanding Agricultural Landscape Dynamics with Explainable Artificial Intelligence | en |
| dc.type | Text | |
| gi.citation.endPage | 87 | |
| gi.citation.startPage | 77 | |
| gi.conference.date | 45195 | |
| gi.conference.location | Berlin | |
| gi.conference.sessiontitle | Doctoral Consortium at KI 2023 | |
| gi.document.quality | digidoc |
Dateien
Originalbündel
1 - 1 von 1