Review of computer vision for livestock body conformation assessment

Abstract

Livestock body conformation is a key indicator for evaluating an animal's production performance, health status, and breeding value. Traditional conformation assessment methods, which rely on manual measurements and visual scoring, are not only time-consuming and labor-intensive but also prone to subjective. With the rapid development of computer vision and artificial intelligence technologies, novel approaches leveraging two-dimensional (2D) images, three-dimensional (3D) point cloud processing, and multimodal data fusion have become research hotspots in the field of automated conformation assessment. This paper reviews the progress of computer vision applications in livestock body conformation assessment, highlighting key methods and their potential practical value. The review encompasses core technologies such as expert knowledge-based approaches, data collection and preprocessing techniques, classical machine learning algorithms, and advanced deep learning models. Specifically, it elaborates on the implementation methods, application scenarios, and typical outcomes of these techniques in body size measurement, limb and hoof detection, reproductive organ detection, and udder detection. Furthermore, the main challenges in applying computer vision to livestock conformation assessment are outlined, including data quality issues, algorithm generalization capability, real-time performance limitations, and the cost and complexity of device deployment. Future research should aim to improve data quality, model adaptability, and deployment efficiency, ensuring scalable and cost-effective conformation assessment solutions.