@article{Wutke.2023,
 abstract = {Die {\"U}berwachung des Abferkelungsverlaufs ist in der Schweinehaltung von gro{\ss}er Bedeutung, um auftretende Geburtsst{\"o}rungen fr{\"u}hzeitig erkennen und geeignete Ma{\ss}nahmen ergreifen zu k{\"o}nnen. Da eine zeitnahe Geburtserkennung und -betreuung aufgrund intensivierter Haltungsbedingungen oftmals nur schwer zu erzielen ist, war das Ziel der vorliegenden Studie, die Eignung neuronaler Netzwerke zur automatischen Identifikation des Geburtsmomentes zu untersuchen. Anhand einer YoloV5-Netzwerkarchitektur bestimmten wir auf Basis der Detektion unterschiedlicher K{\"o}rperteile der Muttersau den potentiellen Geburtsbereich innerhalb der Abferkelbucht und identifizierten den Moment der Geburt des ersten Ferkels anhand der Objektdetektion des Ferkels innerhalb des Zielbereichs. Wir validierten unser Analysemodell durch zweistufigen Ansatz und erreichten einen Precision-, Recall- und MAP-Wert von 0.982, 0.989 und 0.993 im Rahmen der Objektdetektion sowie einen Accuracy-, Recall- und Precision-Wert von 0.9, 0.8 und 1 bei der Bestimmung des Geburtszeitpunktes.},
 author = {Wutke, Martin and Lensches, Clara and Witte, Jan-Hendrik and Gerberding, Johann and Lieboldt, Marc-Alexander and Traulsen, Imke},
 file = {Wutke, Lensches et al. 2023 - Entwicklung eines automatischen Monitoringsystems:Attachments/Wutke, Lensches et al. 2023 - Entwicklung eines automatischen Monitoringsystems.pdf:application/pdf},
 issn = {1617-5468},
 journal = {1617-5468},
 title = {{E}ntwicklung eines automatischen {M}onitoringsystems f{\"u}r die {G}eburts{\"u}berwachung bei {S}auen},
 url = {https://dl.gi.de/handle/20.500.12116/40306},
 year = {2023}
}


              

                @article{Witte.2023,
 author = {Witte, Jan-Hendrik and Gerberding, Johann and Marx G{\'o}mez, Jorge},
 pages = {33--42},
 title = {{U}sing {D}eep {L}earning for {A}utomated {T}ail {P}osture {D}etection of {P}igs},
 url = {http://thinkmind.org/index.php?view=article\&articleid=data\_analytics\_2022\_2\_40\_60037},
 urldate = {26.01.2023},
 year = {2022}
}


              

                @incollection{Witte_2022,
 author = {Witte, Jan-Hendrik},
 booktitle = {Progress in IS},
 doi = {10.1007/978-3-031-15420-1\_10},
 link = {https://doi.org/10.1007/978-3-031-15420-1\_10},
 pages = {215--232},
 publisher = {Springer International Publishing},
 title = {{I}ntroducing a {N}ew {C}ar-{S}haring {C}oncept to {B}uild {D}riving {C}ommunities for {W}ork-{C}ommuting},
 url = {https://doi.org/10.1007\%2F978-3-031-15420-1\_10},
 year = {2022}
}


              

                @inproceedings{Witte_2022,
 author = {Witte, Jan-Hendrik and G{\'o}mez, Jorge Marx},
 booktitle = {Proceedings of the Annual Hawaii International Conference on System Sciences},
 doi = {10.24251/hicss.2022.140},
 link = {https://doi.org/10.24251/hicss.2022.140},
 publisher = {Hawaii International Conference on System Sciences},
 title = {{I}ntroducing a new {W}orkflow for {P}ig {P}osture {C}lassification based on a combination of {Y}OL{O} and {E}fficient{N}et},
 url = {https://doi.org/10.24251\%2Fhicss.2022.140},
 year = {2022}
}


              

                @incollection{Kruse_2022,
 author = {Kruse, Felix and Kessler, Ren{\'e} and Witte, Jan-Hendrik},
 booktitle = {Progress in IS},
 doi = {10.1007/978-3-031-15420-1\_19},
 link = {https://doi.org/10.1007/978-3-031-15420-1\_19},
 pages = {391--406},
 publisher = {Springer International Publishing},
 title = {{S}tructured and {T}argeted {C}ommunication as an {E}nabler for {S}ustainable {D}ata {S}cience {P}rojects},
 url = {https://doi.org/10.1007\%2F978-3-031-15420-1\_19},
 year = {2022}
}


              

                @article{Witte.2022b,
 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.},
 author = {Witte, Jan-Hendrik and Gerberding, Johann and Lensches, Clara and Traulsen, Imke},
 file = {Witte, Gerberding et al. 2022 - Using Deep Learning for automated:Attachments/Witte, Gerberding et al. 2022 - Using Deep Learning for automated.pdf:application/pdf},
 issn = {1617-5468},
 journal = {1617-5468},
 title = {{U}sing {D}eep {L}earning for automated birth detection during farrowing},
 url = {https://dl.gi.de/handle/20.500.12116/39412},
 year = {2022}
}


              

                @article{Witte_2021,
 author = {Witte, Jan-Hendrik and Gerberding, Johann and Melching, Christian and G{\'o}mez, Jorge Marx},
 doi = {10.52825/bis.v1i.59},
 journal = {Business Information Systems},
 link = {https://doi.org/10.52825/bis.v1i.59},
 month = {jul},
 pages = {209--220},
 publisher = {TIB Open Publishing},
 title = {{E}valuation of {D}eep {L}earning {I}nstance {S}egmentation {M}odels for {P}ig {P}recision {L}ivestock {F}arming},
 url = {https://doi.org/10.52825\%2Fbis.v1i.59},
 year = {2021}
}