Estimating body weight and body condition score of mature beef cows using depth images

Authors

Yijie Xiong, University of Nebraska - LincolnFollow
Isabella C.F.S. Condotta, University of Illinois at Urbana-Champaign
J. A. Musgrave, University of Nebraska–LincolnFollow
Tami M. Brown-Bandl, University of Nebraska - LincolnFollow
J. Travis Mulliniks, University of Nebraska-Lincoln,

Document Type

Article

Date of this Version

7-4-2023

Citation

Translational Animal Science, 2023, 7, txad085 https://doi.org/10.1093/tas/txad085

Comments

Open access.

Abstract

Obtaining accurate body weight (BW) is crucial for management decisions yet can be a challenge for cow–calf producers. Fast-evolving technologies such as depth sensing have been identified as low-cost sensors for agricultural applications but have not been widely validated for U.S. beef cattle. This study aimed to (1) estimate the body volume of mature beef cows from depth images, (2) quantify BW and metabolic weight (MBW) from image-projected body volume, and (3) classify body condition scores (BCS) from image-obtained measurements using a machine-learning-based approach. Fifty-eight crossbred cows with a mean BW of 410.0 ± 60.3 kg and were between 4 and 6 yr of age were used for data collection between May and December 2021. A low-cost, commercially available depth sensor was used to collect top-view depth images. Images were processed to obtain cattle biometric measurements, including MBW, body length, average height, maximum body width, dorsal area, and projected body volume. The dataset was partitioned into training and testing datasets using an 80%:20% ratio. Using the training dataset, linear regression models were developed between image-projected body volume and BW measurements. Results were used to test BW predictions for the testing dataset. A machine-learning-based multivariate analysis was performed with 29 algorithms from eight classifiers to classify BCS using multiple inputs conveniently obtained from the cows and the depth images. A feature selection algorithm was performed to rank the relevance of each input to the BCS. Results demonstrated a strong positive correlation between the image-projected cow body volume and the measured BW (r = 0.9166). The regression between the cow body volume and the measured BW had a co-efficient of determination (R²) of 0.83 and a 19.2 ± 13.50 kg mean absolute error (MAE) of prediction. When applying the regression to the testing dataset, an increase in the MAE of the predicted BW (22.7 ± 13.44 kg) but a slightly improved R² (0.8661) was noted. Among all algorithms, the Bagged Tree model in the Ensemble class had the best performance and was used to classify BCS. Classification results demonstrate the model failed to predict any BCS lower than 4.5, while it accurately classified the BCS with a true prediction rate of 60%, 63.6%, and 50% for BCS between 4.75 and 5, 5.25 and 5.5, and 5.75 and 6, respectively. This study validated using depth imaging to accurately predict BW and classify BCS of U.S. beef cow herds.