A dynamic individual method for yak heifer live body weight estimation using the YOLOv8 network and body parameter detection algorithm.

The list of standard abbreviations for JDS is available at adsa.org/jds-abbreviations-24. Nonstandard abbreviations are available in the Notes. Live body weight (LBW) is one of the most important parameters for supervising the growth and development of livestock. The yak (Bos grunniens) is a special species of cattle that lives on the Qinghai-Tibetan Plateau. Yaks are more untamed than regular cattle breeds, so it is more challenging to measure their LBW. In this study, YOLOv8 yak detection and LBW estimation models were used to automatically estimate yak LBW in real time. First, the proper posture (normal posture) and individual yak identification was confirmed and then the YOLOv8 detection model was used for LBW estimation from 2-dimensional images. Yak LBW was estimated through yak body parameter extraction and a simple linear regression between the estimated yak LBW and the actual measured yak LBW. The results showed that the overall detection performance for normal yak posture was described by precision, recall, and mean average precision 50 (mAP50) indicators, reaching 81.8%, 86.0%, and 90.6%, respectively. The best yak identification results were represented by precision, recall, and mAP50 values of 97.8%, 96.4%, and 99.0%, respectively. The yak LBW estimation model achieved better results for the 12-mo-old yaks with shorter hair, with values for R2, root mean square error, mean absolute percentage error, and multiple R of 0.96, 2.43 kg, 1.69%, and 0.98, respectively. The results demonstrate that yak LBW can be estimated and monitored in real time using this approach. This study has the potential to be used for daily yak LBW monitoring in an unstressed manner and to save considerable labor resources for large-scale livestock farms. In the future, to reduce the limitations caused by the impacts of yak hair and light condition, datasets of dairy cows and yaks of different ages will be used to improve and generalize the model. [Display omitted] [ABSTRACT FROM AUTHOR]