Automated Prediction of Phenotypic Traits for Commercial Swine Breeding Management via a Three-Dimensional Scanning System

Highlights A multi-view 3D camera system was built to capture depth maps and reconstruct point clouds of complete pigs in breeding farms. Geometric and morphological features were extracted from the reconstructed point clouds for measuring phenotypic traits via 3D processing algorithms. The minimal mean absolute error of predicting liveweight was 4.19 kg. The mean absolute percentage errors of predicting chest and abdominal circumferences were 2.33% and 2.06%, respectively. Abstract. Manually measuring the phenotypic traits of pigs in breeding farms can be time- and labor-intensive and cause animal stress, and subjective manual measurement errors slow down the progress of desired traits and genetic selection across multiple breeding generations. The objectives of this study were 1) developing a multi-view 3D camera system to capture depth maps and reconstruct point clouds of complete pigs in breeding farms and 2) extracting geometric and morphological features from the reconstructed point clouds for predicting phenotypic traits. The proposed 3D scanning system was installed in a swine breeding farm, and 481 PIC Camborough ® pigs in parent stocks were used with RGB-depth video acquisition. Core 3D processing algorithms merged point clouds from three camera views into one complete pig point cloud, including Iterative Closest Point, Fast Point Feature Histograms, Random Sample Consensus, and Sorting. Gaussian Mixture Clustering and point cloud slicing were implemented to extract critical features for predicting six traits: abdominal circumference, chest circumference, front body height, rear body height, ear-to-tail straight length, and ear-to-tail curve length. Three standard objects were used to test the system’s performance with mean absolute errors (MAE) of 0.04 to 0.07 m. The chest and abdominal circumference correlated highly (r = 0.74) with liveweight. After comparative studies of seven combinations of the six phenotypic traits, the best weight prediction performance (MAE = 4.19 kg) was obtained with the combination of the first four traits via the XGBoost regressor. The study highlighted the innovative applications of artificial intelligence and 3D computer vision for pig breeding and genetics selection. Keywords: Artificial intelligence, Automation, Depth sensing, Expert system, Precision livestock farming.