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

Authors : Yasin ALTAY, Rabia ALBAYRAK DELİALİOĞLU

Pages : 47-54

Doi:10.29136/mediterranean.1065527

View : 23 | Download : 9

Publication Date : 2022-04-01

Article Type : Research Paper

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