Diagnosing lameness with the Random Forest classification algorithm using thermal cameras and digital colour parameters

Lameness is a serious disease that affects the health and welfare of dairy cattle whilst also causing yield economic losses. The primary goal this study to determine if lameness can be detected early on in herd management using Random Forest (RF) algorithm surface temperatures cows' hoof soles, as well digital colour parameters generated by processing these thermal camera images. Ages, sole temperatures, characteristics 40 Simmental were used independent variables study, while was evaluated scoring employed dependent variable after being updated binary variable. ntree= 100 mtry= 3 develop RF for predicting animals. As result, correctly classified 19 22 healthy animals incorrectly 3, it 15 18 unhealthy 3. classification success 85%, sensitivity, specificity area under ROC curve (AUC) 0.864, 0.833, 0.848±0.059, respectively, successful detecting lameness. Also, AUC, which one algorithm's performances, found statistically significant (P<0.05). direct consequence stated suitable classifier terms use animal obtained through image detection management.