Cattle weight estimation using 2D side-view images and estimated depth-based 3D modeling

Abstract

Weighing cattle is a vital practice in livestock farming, as it provides essential data for effective herd management. Recent advancements in computer vision and machine learning have led to the development of non-invasive techniques that estimate cattle weight using images. These methods offer a way to gauge weight without needing physical scales, which helps reduce stress on the animals and minimizes labor-intensive processes. However, existing techniques often rely on dorsal (top-down) views of cattle, which can be difficult to capture in practice. In this study, we propose a method for estimating cattle weight using only side-view images, which are more accessible and easier to obtain. We utilized public datasets to extract a comprehensive set of features, including body measurements and shape descriptors from the images. We also employed advanced techniques such as cattle pose estimation, segmentation, monocular depth estimation, and point cloud generation to derive volume and area features. Our goal was to extract as much relevant information as possible from the images to accurately predict the cattle's weight. We used both linear and non-linear regression models to forecast weight based on the extracted features. Our results indicate that the proposed method can accurately predict cattle weight from side-view images, providing valuable insights for livestock management and monitoring.