Machine learning-based core body temperature estimation and thermal coupling correction from subcutaneous implants in dairy calves

Continuous biothermal monitoring is valuable for evaluating dairy calf health, but conventional methods are usually labor-intensive or invasive. This study evaluated a minimally invasive subcutaneous implant (SCI) system and assessed a LightGBM regression model to mitigate peripheral environmental thermal coupling. In a two-farm trial (n = 720 calves), SCIs were deployed under three presurgical skin preparation protocols. Telemetry was evaluated across development (n = 327 calves; 164,383 hourly sliding-window prediction epochs) and out-of-time (OOT) evaluation cohorts (n = 123 calves; 82,724 epochs; 100 novel, 23 overlapping), separated by an empirical washout buffer (≥ 532 h) to prevent feature leakage. Early implantation failure was low at 1.39% (10/720), with increasing procedural sequence associated with lower failure odds (p = 0.007), whereas skin preparation protocol revealed no statistically detectable difference in 60-day retention (p = 0.77). System performance was constrained by a 9.3% hardware failure rate (67/720; predominantly pre-insertion initialization failures). Although active devices achieved 90.5% telemetry integrity, intention-to-treat data completeness was 82.7%. Raw subcutaneous temperature showed marked disagreement with the gastrointestinal reference (T C ) (RMSE = 1.094°C, CCC = 0.16). The LightGBM model reduced ambient dependence, decreasing error to RMSE = 0.403°C on matched OOT epochs, though concordance remained modest (CCC = 0.35). The model systematically underestimated hyperthermic reference temperatures (T C > 39.5°C), dampening peak febrile excursions. While subcutaneous implants demonstrate procedural feasibility, commercial implementation requires eliminating electronic infant mortality, accommodating telemetry gaps, and resolving hyperthermic attenuation.