On-barn pig weight estimation based on body measurements by a Kinect v1 depth camera

Information on the daily growth rate of pigs enables the stockman to monitor their performance and health and to predict and control their market weight and date. Manual measurements are among the most common ways to get an indication of animal growth. However, this approach is laborious and difficult, and it may be stressful for both the pigs and the stockman. As a consequence, manual measurements can be very time-consuming, induce costs and sometimes cause injuries to the animals and the stockman. The present work proposes the implementation of a Microsoft Kinect v1 depth camera for the fast, non-contact measurement of pig body dimensions such as heart girth, length and height. In the present work, these dimension values were related to animal weight, and two models (linear and non-linear) were developed and applied to the Kinect and manual measurement data. Both models were highly correlated with the direct weight measurements considered as references, as demonstrated by high coefficients of determination (R2> 0.95). Specifically, in the case of the non-linear model based on non-contact depth camera measurements, the mean absolute error exhibited a reduction of over 40% compared to the same non-linear model based on manual measurements (from 0.82 to 0.48 kg).