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prospective.HARVEST – Optimizing Planning of Agricultural Harvest Logistic Chains

dc.contributor.authorde Wall, Arne
dc.contributor.authorDanowski-Buhren, Christian
dc.contributor.authorWytzisk-Arens, Andreas
dc.contributor.authorLingemann, Kai
dc.contributor.authorFocke Martinez, Santiago
dc.contributor.editorGandorfer, Markus
dc.contributor.editorMeyer-Aurich, Andreas
dc.contributor.editorBernhardt, Heinz
dc.contributor.editorMaidl, Franz Xaver
dc.contributor.editorFröhlich, Georg
dc.contributor.editorFloto, Helga
dc.date.accessioned2020-03-04T13:06:51Z
dc.date.available2020-03-04T13:06:51Z
dc.date.issued2020
dc.description.abstractThe research and development project “prospective.HARVEST” aims at optimizing the process chain of silo maize harvesting, based on a predictive approach using prognosis data. New methods and tools have been developed utilizing remote and in-situ (geo-) data from a variety of data sources in order to enable farmers to optimize their logistic chains. Optimizations are computed as recommendations on several layers of the harvest process, from monitoring the crop over planning the inter-field and in-field coordination of harvesters and transport vehicles up to the surveillance and dynamic replanning of the ongoing harvest execution.en
dc.identifier.doiPrecision Farming, Smart Farming, Planning, Maize Harvesting, Logistic Chains
dc.identifier.isbn978-3-88579-693-0
dc.identifier.pissn1617-5468
dc.identifier.urihttps://dl.gi.de/handle/20.500.12116/31927
dc.language.isoen
dc.publisherGesellschaft für Informatik e.V.
dc.relation.ispartof40. GIL-Jahrestagung, Digitalisierung für Mensch, Umwelt und Tier
dc.relation.ispartofseriesLecture Notes in Informatics (LNI) - Proceedings, Volume P-299
dc.titleprospective.HARVEST – Optimizing Planning of Agricultural Harvest Logistic Chainsen
dc.typeText/Conference Paper
gi.citation.endPage60
gi.citation.publisherPlaceBonn
gi.citation.startPage55
gi.conference.date17.-18. Februar 2020
gi.conference.locationWeihenstephan, Freising

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