Empowering precision livestock farming: Artificial intelligence applications in animal genomic breeding and multi-dimensional phenotypic measurement

作者：Liangyu Zhu, Liangyu Zhu, Xidi Yang, Yumei Xian, Wenyu Jiang, Xinyi Pu, Songlin Wang, J.P. Cheng, Lili Niu, Ye Zhao, Lei Chen, Xiaofeng Zhou, Yan Wang, Mailin Gan, Li Zhu, Li Zhu, Linyuan Shen · 发表于：Smart Agricultural Technology · 年份：2025 · DOI：10.1016/j.atech.2025.101655 · 被引用次数：4 · 研究领域：Genetic and phenotypic traits in livestock、Food Supply Chain Traceability、Animal Behavior and Welfare Studies

• AI enhances genetic locus screening via neural network-based prediction. • Deep learning outperforms linear models in capturing complex genetic patterns. • Computer vision enables automated animal ID and behavior phenotyping. • Multi-modal data fusion boosts precision in livestock management systems. • AI frameworks improve breeding efficiency and animal health monitoring. With the continuous advancement of sensor and artificial intelligence (AI) technologies, their integration with devices such as cameras, sound detectors, and odor sensors has provided massive amounts of raw data for precision livestock farming. By leveraging AI-driven data mining and analysis techniques, research on combining AI algorithms with traditional breeding technologies has deepened, particularly in the fields of genetic marker selection and genomic prediction model construction. This review explores application cases and transformative potential of AI in the animal husbandry industry, with a specific focus on machine learning (ML) and its subset, deep learning (DL). Currently, AI is catalyzing the formation of a synergistic feedback loop between "high-throughput phenotypic measurement" (HTP) and "high-precision genomic selection" (GS), thereby providing critical technical support for modern livestock farming and precision agriculture.