Maschinelle Lernverfahren zur Prognose von Tierwohlrisiken in der Schweinehaltung
DOI:
https://doi.org/10.15150/lt.2021.3261Abstract
Tierwohl ist ein Qualitätsmerkmal moderner Schweinehaltung und zunehmend im Blickpunkt der Öffentlichkeit. Tierwohlrisiken sind multifaktoriell und müssen erkannt werden, bevor das Tierwohl gefährdet ist. Diese Arbeit nutzt Maschinelles Lernen (ML) zur frühzeitigen Prognose von Tierwohlrisiken. Der verwendete Datensatz umfasst Daten von über 57.000 Schweinen, gegliedert nach 10 Tierwohlrisiken und 14 Indikatoren für die Säugephase. Der zentrale Beitrag ist ein ML-Modell zur Prognose von Todesfällen in der Säugephase mit einer Accuracy von 80,4 %. Die Accuracy des Majority-Vote-Klassifikators für den Todesfall in der Säugephase beträgt hingegen nur 53,1 %. Somit könnte die Methode dazu beitragen, drohende Todesfälle in der Säugephase von Schweinen frühzeitig zu erkennen und Maßnahmen zu ergreifen.
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