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Deep-learning-based quantification of sow activity during farrowing

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2025

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Gesellschaft für Informatik e.V.

Zusammenfassung

The activity of sows after farrowing significantly influences the occurrence of crushing losses. For this reason, sow activity is often quantified in scientific research through video analysis. To improve practicality, this study developed an automated approach based on the YOLOv10 object detection framework, addressing the big data challenge. The core idea of this approach was to calculate an activity score based on the number of postural changes. Therefore, the model detects the sow’s posture for each frame in the videos. The raw data from the model is then filtered, and the number of postural changes is calculated, from which an activity score is derived for each video. For comparison with a human-generated activity assessment, the videos were finally categorized into three classes based on the activity score. The final performance evaluation across activity classes shows an accuracy of 0.945. Based on these results, it can be concluded that this approach provides an automated alternative to manual video analysis for determining sow activity.

Beschreibung

Wahmhoff, Johann; Wutke, Martin; Traulsen, Imke (2025): Deep-learning-based quantification of sow activity during farrowing. 45. GIL-Jahrestagung, Digitale Infrastrukturen für eine nachhaltige Land-, Forst- und Ernährungswirtschaft. DOI: 10.18420/giljt2025_50. Bonn: Gesellschaft für Informatik e.V.. PISSN: 1617-5468. ISBN: 978-3-88579-802-6. pp. 405-410. Wieselburg, Austria. 25/26. Februar 2025

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