Cattle weight estimation model through readily photos

Lili Bai, Chaopeng Guo, Jie Song

Abstract: Cattle weight is a key indicator for assessing health, meat quality, productivity, and overall well-being. Manual measurement methods are time-consuming and induce stress reactions. In contrast, non-contact measurement methods based on computer vision can effectively and rapidly estimate cattle weight. These non-contact methods typically involve image processing and three-dimensional reconstruction, capturing image information with cameras and combining deep learning algorithms to estimate cattle weight. Despite their advantages, these methods are mainly designed for large-scale professional cattle farms and are characterized by high costs and complex operations, which do not meet the needs of individual cattle farmers. To address this issue, we propose the Lightweight Network-based Cattle Weight Estimation (LaWE) model, deployed on mobile phones and can measure cattle weight by capturing readily available photos. The LaWE model uses an optimized lightweight multi-scale fusion network to accurately extract keypoints from images and calculate relevant body measurements, which are then input into a specially designed deep regression network to estimate cattle weight. We conducted extensive experiments on both publicly available datasets and our self-collected dataset of 108 Horqin Yellow cattle. The results demonstrate that the LaWE model achieves an accuracy rate of over 97% in cattle weight estimation, meeting the requirements for low cost and real-time performance. Our research makes a significant contribution to the field of non-contact cattle weight estimation, providing practical technical support for smallholder farmers in the industry.