Predicting Live Body Weight of Beef Heifers Using 3D Imaging

Abstract Accurately measuring body weight (BW) is of great importance for cow-calf producers in the United States, which can lead to better marketing and nutrient management decisions. Traditionally, BW has been recorded using a walk-over scale or approximated by visual observation. Recently, technology has come out that allows BW prediction using three-dimensional (3D) images. The objective of this study was to determine the efficacy of 3D imaging technology as a method to predict BW of beef heifers. Dorsal 3D images were collected on 69 Red Angus/Simmental yearling heifers. Heifers were approximately 12 mo of age that ranged from 282.1 to 440.0 kg of BW. For image collection, a depth camera (Kinect Azure, Microsoft) was placed above the animals, approximately 3 to 4 meters above the floor surface within the alley of a working facility. The image collection software was written in Python (version 3.8) programming language. For a high-quality image to be recorded, heifers needed to be handled in small groups and kept separate. Heifers also needed to have all four feet on the ground and no interference from other animals in the image. Corresponding scale-measured BW were also recorded for each animal concurrent with depth images collection. Heifers were limit fed and weighed in the morning before feeding for both scale measured BW and image collection. The depth images were analyzed using a customized program written in MATLAB (R2022a). Heifer dorsal projected volume was estimated by adding height pixel values that formed the heifer’s dorsal area, excluding the head region. Body weights were regressed from projected heifer body volumes extracted from the depth images, and then metabolic BW (MBW) was calculated using the 0.75 power adjustment of BW. Performance of the prediction model was analyzed using the coefficient of determination (R2) obtained from the regression of projected volume and actual scale BW and the standard error of the mean (SEM) of the predicted BW. Data were analyzed using the PROC REG and PROC CORR procedures in SAS (v9.4). Prediction of BW using image-extracted dorsal projected volume produced an R 2 = 0.89 and SEM = 3.3 kg. When analyzing the correlation of volume to BW, the Pearson correlation coefficient of r = 0.94 (P < 0.0001) was obtained. Regression of projected volume to MBW produced an R 2 = 0.89 and SEM = 0.57 kg. Results show that this model demonstrates the ability to accurately predict BW using depth images projected dorsal volumes on yearling heifers. These results illustrate that 3D images can be used to accurately measure BW of growing heifers that may enhance the ability of cow-calf producers to pro-actively manage grazing livestock.