Cockburn, MarianneMeyer-Aurich, AndreasGandorfer, MarkusHoffmann, ChristaWeltzien, CorneliaBellingrath-Kimura, SonokoFloto, Helga2021-03-022021-03-022021978-3-88579-703-6https://dl.gi.de/handle/20.500.12116/35715Digitalisation has reached agricultural production and specifically dairy farming, where a wide range of sensing technologies are now available. From farm management systems over body condition scoring systems to those that detect behavioural changes. All these systems have one aim: to offer decision support to the farmer and aid his management decisions. Currently, however, little is known about the return of investment that these systems offer, or even the effectiveness of their functionality. Only little information is available about the underlying algorithms, despite them presenting the essence of performance. Thus, we can only consider the published literature to get an impression of such systems’ outcome. In the current study, we therefore evaluated machine-learning related studies published in the scientific literature between 2015 and 2020. We found that machine-learning algorithms were implemented across all fields of dairy science, but only a minority of them could reliably aid management decisions in practice. In this publication, we aim to give an overview of the achievements of current machine-learning algorithms published in dairy science literature and give an outlook on how they could develop further in the future.endairysmart farmingmachine learningdigitalisationdecision support systemsText/Conference Paper1617-5468