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Plant and animal phenotypes are quantitative descriptions of their characteristics and traits. Accurate analysis of phenotypic features is an important prerequisite for the development of digital agriculture. The traditional phenotypic analysis task heavily relies on manual identification and measurement by agricultural experts, which is labor-intensive, costly, and sensitive to subjective judgments. Also, the traditional approach can hardly process high-throughput data. Benefited by the rapid development of the deep learning technique, as one of the most representative computer vision models, the YOLO family algorithms have shown excellent performance and great potential in plant and animal phenotypic analysis tasks, including disease diagnosis, behavior quantification, biomass estimation, and so on. In this review, livestock, poultry, crops, fruits, vegetables, and other plants and animals were chosen as the research targets. The research progress of YOLO family algorithm applications was summarized from three aspects, namely, object detection, key point detection, and object segmentation. Along the same lines, some commonly used datasets for plant and animal phenotyping tasks for subsequent researchers were presented. Finally, the potential problems faced by current researching and the future development trend of YOLO family algorithms were highlighted, including lightweight architecture design, accurate detection of small targets, weakly supervised learning, complex scene deployment, and large model for target detection. The research aimed at providing summarization and guidance for plant and animal phenotypic analysis based on YOLO family algorithms and promoting the further development of digital agriculture.