AI-enabled smart pig farming has evolved from isolated sensing applications to integrated systems for health monitoring, precision feeding, welfare assessment, and environmental control. However, evidence remains fragmented across computer vision, sensor engineering, nutritional management, and thermal monitoring, obscuring mature and commercially viable technological pathways. This review evaluates deployment-relevant AI technologies for commercial pig production through structured evidence mapping and critical thematic synthesis. We analyzed 707 publications from the Web of Science Core Collection (WoSCC; 1991–2025) and evaluated candidate themes based on publication activity, citation patterns, temporal persistence, and thematic convergence. Three technical streams were examined in depth: vision-based pig detection, precision feeding, and AI-assisted infrared body-temperature monitoring. Across these areas, research has progressed from proof-of-concept algorithms to integrated sensing-to-decision systems. Vision-based detection is advancing toward robust, lightweight models; precision feeding toward individualized closed-loop control; and thermal monitoring toward automated region-of-interest (ROI) localization and AI-assisted temperature interpretation. Major gaps remain in dataset representativeness, cross-farm generalizability, methodological consistency, field-scale validation, system reliability, economic feasibility, thermal calibration, surface-to-core temperature inference, ROI localization, and false-alarm control. This review is limited by its reliance on a single bibliographic database, predefined search terms, and potential publication and citation biases. Future progress requires cross-site validation, multimodal sensing, interpretable decision models, cost-effective deployment, adaptive thermal calibration, and reliable alert strategies. Overall, AI-enabled smart pig farming is not only an algorithmic challenge but also a systems-integration task that must translate sensing and prediction into actionable farm management.

Identifying causal genetic variants and candidate genes underlying complex traits remains a central challenge in animal breeding and genetics. Genome-wide association studies (GWAS) are widely used for this purpose. However, their reliance on marginal variant effects and sensitivity to linkage disequilibrium (LD) can lead to redundant and less accurate identification of variants or genes of biological relevance. Here, we propose SNP prioritization (GWAS-based and fine-mapping-based) strategies within a unified framework, designed to improve the selection of more informative variants and candidate genes by explicitly modeling LD structure and genetic architectures of three pig teat-related traits (total teat number, teat symmetry, and teat adequacy). While GWAS prioritization favored variants with strong marginal effects, fine-mapping substantially improved joint explanatory performance and prediction accuracy. For total teat number, the best-performing fine-mapping-derived SNP subset achieved a mean PCC of 0.6599 across 10-fold cross-validation, compared with 0.3755 for GWAS-based prioritization. Similarly, for teat adequacy, the highest mean AUC increased from 0.7012 (GWAS) to 0.8547 (fine-mapping). Moreover, fine-mapping-derived SNP sets identified more coherent and trait-specific biological pathways and functionally relevant candidate genes. Taken together, our findings demonstrate that fine-mapping provides a more accurate and biologically meaningful framework for SNP and candidate gene prioritization, supporting its integration into genetic analysis and breeding applications.