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Drinking event detection of dairy cows using deep learning

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dc.contributor.authorMukunda, Vishal Sharbidar
dc.contributor.authorJagadale, Anish Bipin
dc.contributor.authorGirmay, Mengisti Berihu
dc.contributor.authorMöhrle, Felix
dc.contributor.authorBurkhardt, Franziska Katharina
dc.contributor.authorHayer, Jason Jeremia
dc.contributor.authorDoerr, Joerg
dc.contributor.authorSteinhoff-Wagner, Julia
dc.contributor.editorDörr, Jörg
dc.contributor.editorSteckel, Thilo
dc.date.accessioned2025-02-04T14:38:03Z
dc.date.available2025-02-04T14:38:03Z
dc.date.issued2025
dc.description.abstractWater is crucial for dairy cows, making up 55-70% of their body weight and 85% of milk [Be12]. Restricted access to water affects health, welfare, milk quality and quantity, so adequate water intake is essential. Dairy cows’ drinking behavior is influenced by trough design and cleanliness, making monitoring important but tedious [Bu22]. This study introduces a deep-learning approach to detect the drinking event and monitor the total duration of drinking. The approach is divided into cow detection, identification, drinking event detection and tracking the total duration of the cow’s drinking. Various You Only Look Once (YOLO) models were used for cow detection, ResNet-18 and ResNet-50 for identification, and Deep SORT with OCR for detecting and tracking the drinking event. Various YOLO versions and ResNet models were compared for performance. The approach achieved 98% precision in cow detection, 98% accuracy in identification, and 95% accuracy in detecting the duration of drinking, with a 97% F1 Score, ensuring reliable monitoring of dairy cows’ health through their drinking behavior.en
dc.identifier.doi10.18420/giljt2025_08
dc.identifier.eissn2944-7682
dc.identifier.isbn978-3-88579-802-6
dc.identifier.pissn2944-7682
dc.identifier.urihttps://dl.gi.de/handle/20.500.12116/45716
dc.language.isoen
dc.publisherGesellschaft für Informatik e.V.
dc.relation.ispartof45. GIL-Jahrestagung, Digitale Infrastrukturen für eine nachhaltige Land-, Forst- und Ernährungswirtschaft
dc.relation.ispartofseriesLecture Notes in Informatics(LNI) - Proceedings, Volume P - 358
dc.subjectdairy farming
dc.subjectartificial intelligence
dc.subjectevent detection
dc.subjectprecision farming
dc.titleDrinking event detection of dairy cows using deep learningen
dc.typeText/Conference Paper
gi.citation.endPage118
gi.citation.publisherPlaceBonn
gi.citation.startPage107
gi.conference.date25/26. Februar 2025
gi.conference.locationWieselburg, Austria
gi.conference.reviewfull

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P358 - 45. GIL-Jahrestagung 2025 - Fokus: Digitale Infrastrukturen für eine nachhaltige Land-, Forst- und Ernährungswirtschaft