Towards rapid weight assessment of finishing pigs using a handheld, mobile RGB-D camera.

Pig weight measurement is essential for monitoring performance, welfare, and production value. Weight measurement using a scale provides the most accurate results; however, it is time consuming and may increase animal stress. Subjective visual evaluations, even when conducted by an experienced caretaker, lack consistency and accuracy. Optical sensing systems provide alternative methods for estimating pig weight, but studies examining these systems only focus on images taken from stationary cameras. This study fills a gap in existing technology through examining a handheld, portable RGB-D imaging system for estimating pig weight. An Intel RealSense camera collected RGB-D data from finishing pigs at various market weights. 3D point clouds were computed for each pig, and latent features from a 3D generative model were used to predict pig weights using three regression models (SVR, MLP and AdaBoost). These models were compared to two baseline models: median prediction and linear regression using body dimension measurements as predictor variables. Using 10-fold cross validation mean absolute error (MAE) and root-mean-square error (RMSE), all three latent feature models performed better than the median prediction model (MAE = 12.3 kg, RMSE = 16.0 kg) but did not outperform linear regression between weight and girth measurements (MAE = 4.06 kg, RMSE = 4.94 kg). Of the models under consideration, SVR performed best (MAE = 9.25 kg, RMSE = 12.3 kg, mean absolute percentage error = 7.54%) when tested on unseen data. This research is an important step towards developing rapid pig body weight estimation methods from a handheld, portable imaging system by leveraging deep learning feature outputs and depth imaging technology. • A new weight prediction method was developed using mobile, low-cost RGB-D technology. • Method does not restrict the position of the camera relative to pig's body. • Latent features predicted pig weight more accurately than median prediction model. [ABSTRACT FROM AUTHOR]