Feature extraction using multi-view video analytics for dairy cattle body weight estimation

Abstract

Accurate body weight (BW) estimation of livestock provides valuable information about animal health and welfare and can be used to assess changes in nutrient status of individuals or groups of animals. To obtain BW information regularly, automated low-cost methods that do not intrude upon normal farm operation are essential. Measurements of BW from 196 lactating, Holstein dairy cattle were collected across 5 days (655 +/- 77.1 kg; range 458 - 876 kg). This population of animals was used to develop a BW estimation system for dairy cattle using a synchronized two-camera system. The system applies deep-learning models and domain knowledge to both camera views to estimate BW of dairy cattle despite challenges such as occluding fences and crowded backgrounds. Videos from side and front-view were used to extract features about the anatomical locations and shape of the individual cows, and a regression model was applied to the features for BW prediction. We demonstrate that each view provides valuable information for BW estimation, where combining two views outperforms either view separately. This is despite the side view having distractors such as occluding fences and crowded backgrounds. The experimental results, applied to videos captured within the constraints of an operating dairy farm, demonstrate a root-mean-squared prediction error of 34 kg and a mean absolute percentage error of 4.1%, when a Random Forest is trained on four days of data and tested on the fifth unseen day. This model enabled 57% of cattle BW to be estimated within 25 kg of their measured BW. In addition, we demonstrated that linear regression generalized well to unseen data.