Prediction of Weight and Body Condition Score of Dairy Goats Using Random Forest Algorithm and Digital Imaging Data - Mateus Alves Gonçalves; Maria Samires Martins Castro; Eula Regina Carrara; Camila Raineri; Luciana Navajas Rennó; Erica Beatriz Schultz (Animals)
The aim of study was to evaluate the use of digital images to predict body weight (BW) and classify the body condition score (BCS) of dairy goats. A total of 154 female Saanen and Alpine goats were used to obtain eight body measurements features from digital images: withers height (WH), rump height (RH), body length (BL), chest depth (D), paw height (PH), chest width (CW), rump width (RW), rump length (RL). All animals were weighed using manual scales, and their BCS was evaluated on a scale of 1 to 5. For classification purposes, the BCS was grouped into three categories: low (1–2), moderate (2–3), and high (>3). Pearson’s correlation analysis and the Random Forest algorithm were performed. It was possible to predict BW using image features with an R2 of 0.87, with D (22.14%), CW (18.93%) and BL (15.47%) being the most important variables. For the BCS, the classification accuracy was 0.4054 with the CW (20.38%) the most important variable followed by RH and RL with 15.78% and 12.63%, respectively. It was concluded that digital image features can be used to obtain precise estimates of body weight, but it is necessary to increase data variability to improve the BCS classification of dairy goats.