Neural network-based method for contactless estimation of carcass weight from live beef images

Abstract

Accurately estimating the carcass weight of the live beef cattle is crucial in the breeding industry as it is essential for evaluating the quality and production capacity of beef cattle and directly impacts the economic benefits of breeding farms. Although current animal husbandry research predominantly focuses on estimating live body weight, few studies explore the relationship between live images and carcass weight. Additionally, existing methods for estimating carcass weight rely on manually measured body dimensions, a process that is time-consuming, laborious, and compromises animal welfare. In this study, we propose a contactless method utilizing dual-input deep neural networks to estimate the carcass weight of live beef cattle, and explore the impact of both top and side views images on the estimation results while performing experimental analyses of specific scenarios encountered in practical applications to highlight the model’s robustness. The feature extraction network employs two SE-ResNeXt-50 models to extract back features from top view images and abdominal features from side view images, respectively. By merging the extracted information from both views, the combined features are processed through a network to obtain the estimated carcass weight. The proposed model has been trained and tested on a dataset collected by our team, demonstrating superior performance compared to other typical deep learning models across four indicators: MAE, RMSE, MAPE, and R², particularly achieving a notable RMSE of 17.713 kg. Ablation experiments are conducted to validate the contributions of the group convolution structure and the Squeeze and Excitation (SE) block. Overall, the method presented in this study bears significant implications for animal quality and production capacity evaluation in the breeding industry.