ArticleAnimals : an open access journal from MDPI2026
Noninvasive Two-Phase Foaling Prediction in Thoroughbred Mares Using Thermal Imaging and AI-Based Behavioral Analysis.
Article in Animals : an open access journal from MDPI, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Early and accurate detection of foaling is important for reducing perinatal mortality and labor demands in commercial breeding farms. This study evaluated a fully noninvasive foaling prediction system integrating thermal imaging with artificial intelligence-based behavioral analysis in Thoroughbred mares. A total of 115 pregnant mares across 13 farms were monitored during the 2024 foaling season. Locomotor activity and body surface temperature relative to ambient temperature were extracted from thermal images, while posture changes and tail-raising behavior were identified from visible-light images using a trained prediction model. Prediction was performed at 5 min intervals using a pretrained neural network model implemented in Python, which generated logistic probabilities based on locomotor activity, body surface temperature, posture change frequency, and tail-raising behavior. Data obtained during the 5 h preceding foaling were analyzed. Locomotor activity and surface temperature significantly increased 70-90 min before foaling (
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