Ocular infrared thermography combined with stacked ensemble machine learning enables contact-free rectal temperature prediction in Murrah buffaloes.

Introduction: Accurate core body temperature monitoring is central to health assessment in buffaloes, yet conventional rectal thermometry demands physical restraint and direct animal contact, causing stress to animals and posing injury risk to handlers. Methods: This study developed and validated a contact-free prediction framework combining ocular infrared thermography with stacked ensemble machine learning, using medial canthus temperatures of both eyes alongside environmental variables as predictors of rectal temperature in 471 adult female Murrah buffaloes. Normalisation parameters were derived solely from the training set. Eight supervised algorithms were trained under five-fold cross-validation; the five best-performing learners were integrated into a stacked ensemble with a penalised linear meta-learner. A 48-h continuous monitoring experiment ( Results: On the independent test set, the stacked ensemble achieved the strongest performance (RMSE = 0.2089, 95% CI: 0.179-0.240; Discussion: Ocular medial canthus thermography combined with ensemble machine learning offers a reliable, welfare-friendly alternative to rectal thermometry, with practical potential for integration into precision livestock health monitoring systems.