AI in practice: Advancing intelligent pig breeding with AI-driven image extraction
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[1] Xu H, Zhang Z, Zhao W, et al. (2025). Deep learning-driven automated carcass segmentation and composition quantification in live pigs via large-scale CT imaging and its application in genetic analysis of pig breeding. Smart Agric. Technol. 11:100898. DOI:10.1016/j.atech.2025.100898 [2] Wu J, Yang Y, Yang W, et al. (2025). Non-destructive and efficient prediction of intramuscular fat in live pigs based on ultrasound images and machine learning. Comput. Electron. Agric. 234:110291. DOI:10.1016/j.compag.2025.110291 [3] Zhang J., Jiao H., Li G., et al. (2026). YIP: Optimized YOLOv11-seg for accurate intramuscular fat percentage prediction in pigs using non-invasive ultrasound imaging. Sci. China Life Sci. 69:683−686. DOI:10.1007/s11427-025-3120-2 [4] Hou G., Li R., Tian M., et al. (2024). Improving efficiency: Automatic intelligent weighing system as a replacement for manual pig weighing. Animals. 14:1614. DOI:10.3390/ani14111614 [5] García-vázquez F A. (2024). Artificial intelligence and porcine breedin. Anim. Reprod. Sci. 269:107538. DOI:10.1016/j.anireprosci.2024.107538 -
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Editorial Team (2026). AI in practice: Advancing intelligent pig breeding with AI-driven image extraction. The Innovation Life 4:100210. https://doi.org/10.59717/j.xinn-life.2026.100210
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Editorial Team (2026). AI in practice: Advancing intelligent pig breeding with AI-driven image extraction. The Innovation Life 4:100210. https://doi.org/10.59717/j.xinn-life.2026.100210
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Figure 1.
Process of the AI-assisted pork quality evaluation and the research team.
