Estimating body weight, body condition score, and type traits in dairy cows using three dimensional cameras and manual body measurements

This study aimed to determine body weight (BW), body condition score (BCS), and dairy type traits (DTT) of Holstein heifers and lactating cows using three-dimensional (3D) cameras, in addition to evaluating the sensitivity of this system over time and the best sensor position. Twenty-eight cows and twenty-seven heifers were used. Measurements of BW and BCS were taken over five months, and one single measurement was taken for each of the 23 lactating cows that had the official Holstein register to evaluate DTT. Images were taken using a Microsoft Kinect 3D camera from lateral and dorsal perspectives to predict BW and BCS, synced with MATLAB software. Fourteen and thirteen measurements were taken from dorsal and lateral perspectives, respectively. Then, the SAS GLMSELECT LASSO procedure was used to WA the linear and quadratic effects, and the ratios of the obtained variables. Subsequently, selected characteristics were tested using PROC MIXED of SAS to fit the models and predict BW and BCS. In addition, DTT were evaluated using 3D camera images to estimate the Holstein Association official grade. The udder, chest, and close back side were used to complement the lateral and dorsal images. Biometric measurements and 3D camera data were also compared to each other using a paired t-test. The obtained models to predict BW had an R-2 of 0.89 and 0.96 and RMSE of 49.20 and 26.89 for lateral and dorsal perspectives, respectively. The lateral model was composed of body weight, height, body depth, and body lateral volume. The dorsal model was composed of rump width, thorax width, and dorsal area. The model obtained to predict BCS had an R-2 of 0.63 and 0.61 and RMSE of 0.16 and 0.17 for lateral and dorsal images respectively. The lateral model was composed of body depth, lateral area, and body weight divided by height. The dorsal model was composed of dorsal length, dorsal area, dorsal volume, and body weight to dorsal area ratio. Among all official 15 evaluated DTT, 4 were adequately predicted (P < 0.05) by 3D cameras. Ten DTT were adequately predicted (P < 0.05) by biometric measurements. In conclusion, 3D cameras have a good prospective future commercial use and either lateral or dorsal images could be used for BW prediction however the BCS models still need improvements. The udder traits were those DTT with the best prospective use, due to the highest accuracy.