Using Transfer Learning for Quality Improved Forecasting of Temporal Agricultural Processes by Adapting Convolutional Neural Networks
| dc.contributor.author | Münzberg,Alexander | |
| dc.contributor.author | Troost,Christian | |
| dc.contributor.author | Bernardi,Ansgar | |
| dc.contributor.editor | Demmler, Daniel | |
| dc.contributor.editor | Krupka, Daniel | |
| dc.contributor.editor | Federrath, Hannes | |
| dc.date.accessioned | 2022-09-28T17:10:11Z | |
| dc.date.available | 2022-09-28T17:10:11Z | |
| dc.date.issued | 2022 | |
| dc.description.abstract | AI-based decision support can help farmers to reach improved productivity in an environmentally sustainable way. Through transfer learning, an existing Convolutional Neural Network is progressively adapted to provide high quality forecasting results using agricultural time series in the context of different locations, growth and soil types, climate zones, and management variations. The delivered results are validated by appropriate statistical methods and show improved prediction accuracy. | en |
| dc.identifier.doi | 10.18420/inf2022_128 | |
| dc.identifier.isbn | 978-3-88579-720-3 | |
| dc.identifier.pissn | 1617-5468 | |
| dc.identifier.uri | https://dl.gi.de/handle/20.500.12116/39502 | |
| dc.language.iso | en | |
| dc.publisher | Gesellschaft für Informatik, Bonn | |
| dc.relation.ispartof | INFORMATIK 2022 | |
| dc.relation.ispartofseries | Lecture Notes in Informatics (LNI) - Proceedings, Volume P-326 | |
| dc.subject | Decision Support in Agriculture | |
| dc.subject | AI-based Methods | |
| dc.subject | Transfer Learning | |
| dc.subject | Time Series Analysis | |
| dc.subject | Convolutional Neural Network | |
| dc.title | Using Transfer Learning for Quality Improved Forecasting of Temporal Agricultural Processes by Adapting Convolutional Neural Networks | en |
| gi.citation.endPage | 1503 | |
| gi.citation.startPage | 1495 | |
| gi.conference.date | 26.-30. September 2022 | |
| gi.conference.location | Hamburg | |
| gi.conference.sessiontitle | Künstliche Intelligenz in der Umweltinformatik (KIU-2022) |
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