High-throughput in vivo phenotyping of chicken carcass traits based on digital radiographic imaging

BACKGROUND: Accurate phenotyping of breast, drumstick, and wing yield is essential for genetic improvement in poultry breeding. However, these key economic traits can traditionally be measured only after slaughter, forcing breeding programs to rely on time-consuming, costly, and operator-biased sibling testing. This has become a major challenge for large-scale, high-precision, and non-destructive phenotypic evaluation in commercial poultry breeding. In this study, a non-destructive in vivo phenotyping framework based on digital radiography (DR) imaging combined with deep learning and machine learning methods is established. RESULTS: A standardized DR image acquisition platform is constructed for in vivo phenotyping of live chickens. A lightweight multi-objective segmentation network, DRSegNet, is designed to achieve precise segmentation of breast, drumstick, and wing regions, with Dice coefficients higher than 97%. Morphological features were extracted from the segmentation masks and fed into machine learning models for weight regression. Five-fold cross-validation revealed strong predictive performance: the best-performing models achieved mean Pearson correlation coefficients of 0.8972, 0.7696, and 0.8049 for breast, drumstick, and wing weights, respectively. A phenotypic index (PI) and carcass balance index (CBI) are further constructed to enable comprehensive and balanced multi-trait selection. The deployed system supports a complete detection cycle of approximately 40 s per chicken, allowing high-throughput in vivo detection. CONCLUSIONS: This DR-based non-destructive framework enables reliable, real-time, and high-throughput in vivo prediction of key carcass traits in live chickens. It transforms traditional post-slaughter measurements into in vivo phenotypes obtainable directly from individual birds, may improve the accuracy and efficiency of breeding selection, and has the potential to shorten the breeding cycle. This study provides a practical and scalable technical solution for intelligent phenotyping in poultry, and may contribute to data-driven precision breeding systems.