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Drinking event detection of dairy cows using deep learning
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Datum
2025
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Verlag
Gesellschaft für Informatik e.V.
Zusammenfassung
Water 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.