Riekert, MartinZimpel, TobiasHoffmann, ChristaWild, AndreaGallmann, EvaKlein, AchimGandorfer, MarkusMeyer-Aurich, AndreasBernhardt, HeinzMaidl, Franz XaverFröhlich, GeorgFloto, Helga2020-03-042020-03-042020978-3-88579-693-0https://dl.gi.de/handle/20.500.12116/31908Animal welfare monitoring has the potential to improve animal welfare and provide quality-oriented differentiation for producers at the same time. However, early approaches to animal welfare monitoring use manual injury scoring and evaluation of slaughter data and other biological data. These approaches are often characterized by manual data collection, with data being evaluated infrequently. Thus, production costs would increase substantially. However, with the advent of high-tech commercial sensor technology, monitoring can be conducted automatically, objectively, and at low cost. The aim of this study is to review the suitability of environmental sensors in combination with machine learning in an intelligent animal welfare monitoring system. The system automatically analyzes data from commercially available low-cost sensors, identifies animal welfare risks and recommends actions for animal welfare.enAnimal welfare monitoringenvironmental sensorsmachine learningText/Conference Paper1617-5468