Automated retrieval of cattle body measurements from unmanned aerial vehicle-based LiDAR point clouds

Abstract

Accurate body measurements are crucial for effective management of cattle growth in precision livestock farming. This study introduces a novel noncontact approach that leverages point clouds acquired by unmanned aerial vehicle (UAV) based LiDAR to obtain body measurements of cattle within their natural husbandry conditions. The experiment encompasses 36 LiDAR scanning campaigns, six during the nighttime, using various combinations of flight speed and height. An automated procedure for retrieving body measurements is applied to pre-processed cattle point clouds, with a total of 276 individual animals segmented from campaign-generated point clouds using an automated segmentation procedure. The procedure uses identified body-marks to extract six body measurements from each cattle point cloud. To enhance the accuracy of the extracted body measurement dataset, multivariate analysis of variance (MANOVA) is used, facilitating the adjustment of the dataset that excludes data derived at flight heights of $30m$ and $50m$ or flight speeds of $7\mathrm{m/s}$ and $9\mathrm{m/s}$. The reference dataset validates the adjustment effectiveness, demonstrating substantial reductions in mean absolute error (MAE), such as the vertical gap measurement ($h1$) on reference objects, from $2\mathrm{cm}$ to $2\mathrm{mm}$. Furthermore, the study delves into anatomical hip height (HH’) estimation by developing a 10-fold cross-validation linear regression model based on the training dataset of 136 pairs of waist height and hip height (HH) derived with a manually added auxiliary plane. The model yields the estimation of the HH’ with $R^2$ of 0.84, MAE of $0.012m$, and RMSE of $0.015m$. Moreover, this study proposes a dual rotation algorithm to normalise cattle head orientation. The results of this study contribute to the advancement of using UAV-based LiDAR for cattle growth management.