Evaluation of hybrid models based on image segmentation and inference for pig weight estimation

Valles-Coral M.A.; Rojas-Córdova K.L.; Pinedo L.; Injante R.; Vidaurre-Rojas P.; Saavedra-Ramírez J.; Ruiz-Saavedra F.; Ramirez W. · Frontiers in Artificial Intelligence (2026) · DOI: 10.3389/frai.2026.1876396

Resumen

Introduction – Accurate weight estimation in pig production is essential for optimizing management, feeding, and commercialization decisions; however, traditional weighing methods are invasive, time-consuming, and prone to operational errors. This study proposes a non-invasive computer vision–based approach to estimate pig weight under real farm conditions in San Martín, Peru. Methods – A dataset of 3, 800 lateral images paired with their corresponding ground-truth weights was collected. A computational pipeline was implemented, including geometric standardization, instance segmentation using YOLOv8n-seg, and feature extraction through EfficientNet-B0. The resulting embeddings were used as input for supervised regression models (SVR, XGBoost, and CatBoost), evaluated using repeated stratified cross-validation and an independent test set, with MAE, RMSE, and R² as performance metrics. Statistical comparisons were conducted using the Friedman test followed by Wilcoxon post hoc analysis with Holm correction. Results – The results demonstrated strong predictive performance, with the SVR model achieving the best results (RMSE = 2.68 kg, MAE = 1.81 kg, R2 = 0.85), showing statistically significant differences compared to the other models. Discussion – These findings indicate that combining computer vision techniques with models capable of capturing non-linear relationships effectively models the relationship between animal morphology and body weight, providing a low-cost, non-invasive solution applicable to real-world production systems and supporting the advancement of precision livestock farming. Copyright

Instituciones: Universidad Nacional Autónoma de Alto Amazonas; Universidad Nacional de San Martín; Universidad Privada Norbert Wiener