Noninvasive Two-Phase Foaling Prediction in Thoroughbred Mares Using Thermal Imaging and AI-Based Behavioral Analysis

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 (p < 0.05), whereas posture changes and tail-raising behavior markedly increased 25-45 min before foaling. These temporal patterns suggest a biologically interpretable two-phase pre-foaling process derived from sequential changes in behavioral and thermal indicators. The model based on locomotor activity and surface temperature achieved an 80.0% detection rate with a mean lead time of 186 ± 20 min. Incorporating posture changes and tail-raising parameters improved detection to 94.8% and reduced the detection-to-foaling interval to 89 ± 14 min. The system achieved high predictive performance under commercial conditions without requiring invasive or wearable devices.