Using Deep Learning for automated birth detection during farrowing
| dc.contributor.author | Witte, Jan-Hendrik | |
| dc.contributor.author | Gerberding, Johann | |
| dc.contributor.author | Lensches, Clara | |
| dc.contributor.author | Traulsen, Imke | |
| dc.contributor.editor | Wohlgemuth, Volker | |
| dc.contributor.editor | Naumann, Stefan | |
| dc.contributor.editor | Arndt, Hans-Knud | |
| dc.contributor.editor | Behrens, Grit | |
| dc.contributor.editor | Höb, Maximilian | |
| dc.date.accessioned | 2022-09-19T09:20:53Z | |
| dc.date.available | 2022-09-19T09:20:53Z | |
| dc.date.issued | 2022 | |
| dc.description.abstract | Pig livestock farming has been undergoing major structural change for years. The number of animals per farm is constantly increasing, while competition is becoming more intense due to volatile slaughter prices. Sustainable, welfare-oriented livestock farming becomes increasingly difficult under these conditions. Studies have shown that animal-specific birth monitoring of sows can significantly reduce piglet losses. However, continuous monitoring by human staff is inconceivable, which is why systems need to be created that assist farmers in these tasks. For this reason, this paper aims to introduce the first step towards an automated birth monitoring system. The goal is to use deep learning methods from the field of computer vision to enable the detection of individual piglet births based on image data. This information can be used to develop systems that detect the beginning of a birth process, measure the duration of piglet births, and determine the time intervals between piglet births. | en |
| dc.identifier.isbn | 978-3-88579-722-7 | |
| dc.identifier.pissn | 1617-5468 | |
| dc.identifier.uri | https://dl.gi.de/handle/20.500.12116/39412 | |
| dc.language.iso | en | |
| dc.publisher | Gesellschaft für Informatik e.V. | |
| dc.relation.ispartof | EnviroInfo 2022 | |
| dc.relation.ispartofseries | Lecture Notes in Informatics (LNI) - Proceedings, Volume P-328 | |
| dc.subject | precision livestock farming | |
| dc.subject | birth monitoring | |
| dc.subject | deep learning | |
| dc.subject | computer vision | |
| dc.title | Using Deep Learning for automated birth detection during farrowing | en |
| dc.type | Text/Conference Paper | |
| gi.citation.publisherPlace | Bonn | |
| gi.citation.startPage | 141 | |
| gi.conference.date | 26.-30- September 2022 | |
| gi.conference.location | Hamburg |
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