Estimating body dimensions and weight of cattle on pasture with 3D models from UAV imagery

Abstract

Monitoring traits of cattle with an Unmanned Aerial Vehicle (UAV) could assist farmers in monitoring the status and health of their herd without actually visiting them. This feasibility study presents 3D methods to manually estimate body dimensions, such as height and weight, of individual Holstein Friesian cows with stereo RGB imagery, video and LiDAR data from UAVs. In total, 25 different cows over 3 years were monitored and 4,611 images, ∼10 videos, and a LiDAR dataset were analysed. The methods used were estimating weight by analysing 3D models made with RGB imagery and video from UAV, and estimating height and weight from LiDAR data. Software used in this study to process the UAV data and to extract 3D models are Agisoft Metashape Professional, CloudCompare, RiPROCESS 1.8.4 Riegl Software and Potree Desktop. LiDAR showed accurate results for estimating withers height (mean error 6cm) and weight (mean error 38 kg). Estimating weight with 3D models extracted with Structure for Motion (SfM) from overlapping RGB imagery resulted in less accurate results (mean error 62kg). The latter result improved to a mean error of 31 kg when three outliers were removed which exceeded twice the standard deviation. These results suggest that the use of UAVs is promising for estimating body dimensions in extensive beef production systems. The main challenge remains to record individual cows from all sides without too much movement or motion from the cow to enable 3D modelling. Another challenge was to decrease the mean error in weight estimations so that changes in body weight over time can be monitored. Future research should focus on improving the overall mean error of cattle weight estimation, finding solutions for moving cattle and shifting from manual to automated processing of UAV imagery into 3D models.