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Using Transfer Learning for Quality Improved Forecasting of Temporal Agricultural Processes by Adapting Convolutional Neural Networks

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dc.contributor.authorMünzberg,Alexander
dc.contributor.authorTroost,Christian
dc.contributor.authorBernardi,Ansgar
dc.contributor.editorDemmler, Daniel
dc.contributor.editorKrupka, Daniel
dc.contributor.editorFederrath, Hannes
dc.date.accessioned2022-09-28T17:10:11Z
dc.date.available2022-09-28T17:10:11Z
dc.date.issued2022
dc.description.abstractAI-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.doi10.18420/inf2022_128
dc.identifier.isbn978-3-88579-720-3
dc.identifier.pissn1617-5468
dc.identifier.urihttps://dl.gi.de/handle/20.500.12116/39502
dc.language.isoen
dc.publisherGesellschaft für Informatik, Bonn
dc.relation.ispartofINFORMATIK 2022
dc.relation.ispartofseriesLecture Notes in Informatics (LNI) - Proceedings, Volume P-326
dc.subjectDecision Support in Agriculture
dc.subjectAI-based Methods
dc.subjectTransfer Learning
dc.subjectTime Series Analysis
dc.subjectConvolutional Neural Network
dc.titleUsing Transfer Learning for Quality Improved Forecasting of Temporal Agricultural Processes by Adapting Convolutional Neural Networksen
gi.citation.endPage1503
gi.citation.startPage1495
gi.conference.date26.-30. September 2022
gi.conference.locationHamburg
gi.conference.sessiontitleKünstliche Intelligenz in der Umweltinformatik (KIU-2022)

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P326 - INFORMATIK 2022 - Informatik in den Naturwissenschaften