Evaluating machine learning algorithms based on thermal imaging and milk-based parameters to identify subclinical mastitis in dairy cows.

The objective was to evaluate the potential of machine learning algorithms utilizing thermal imaging and/or milk-based parameters for the identification of subclinical mastitis(SCM) in dairy cows. Holstein dairy cows (n = 194) were enrolled, and milk samples were collected from each quarter separately (n = 776 quarter-level samples) and analyzed for milk-based parameters. Four images per cow, representing all four quarters, were captured using a handheld infrared camera. Skin temperatures were extracted using Fluke Connect software, yielding 776 images for analysis. SCM was defined at the quarter level as SCC > 200,000 cells/mL, identifying 139 infected and 637 healthy quarters. The data were analyzed to test Random Forest Classifier(RFC), Logistic Regression Classifier, AdaBoost Classifier(ABC), and Naïve Bayes Classifier algorithms. The RFC models using milk features and temperature features achieved accuracy and precision scores of 0.88 and 0.88, respectively, yielding 0.90 AUC values, with high feature importance for milk lactose and SNF. Using only thermal imaging features, the ABC model yielded accuracy and precision of 0.82 and 0.67, respectively, with 0.56 AUC. We conclude that SCM prediction using machine learning algorithms is most promising when combined with thermal images and milk-based parameters. Future studies with larger datasets and refined methods for thermal image capture and analysis are warranted to validate these findings.