prospective.HARVEST - Optimizing Planning of Agricultural Harvest Logistic Chains

Arne de Wall, Christian Danowski-Buhren, Andreas-Wytzisk-Arens, Kai Lingemann, Santiago Focke Martínez

In: Lecture Notes in Informatics (LNI). Gesellschaft für Informatik in der Land-, Forst- und Ernährungswirtschaft (GIL-2020) February 17-18 Weihenstephan Germany Köllen Druck & Verlag GmbH 2020.


The 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 in order to enable farmers to optimize their logistic chains.


German Research Center for Artificial Intelligence
Deutsches Forschungszentrum für Künstliche Intelligenz