Advancing Non‐Contact Fish Phenotyping via Optical Sensing: Toward Precision Aquaculture

作者：Kai Lin, Shiyu Zhang, Wei Wang, Tian Dong, Wen Ding, Chenfan Geng, Yihang Lu, Hongxia Hu · 发表于：Reviews in Aquaculture · 年份：2025 · DOI：10.1111/raq.70123 · 被引用次数：2 · 研究领域：Water Quality Monitoring Technologies、Cell Image Analysis Techniques、Zebrafish Biomedical Research Applications

ABSTRACT Non‐contact fish phenotyping has become an essential strategy for precision aquaculture, enabling accurate, high‐throughput, and non‐invasive trait measurement. In this review, a digital phenotyping framework is summarized, including application‐driven trait definition, multimodal data acquisition (2D, 2.5D, and 3D imaging), and automated phenotypic analysis based on computer vision and deep learning. According to current research paradigms, optical phenotyping applications are systematically categorized into three major domains: morphological, behavioral, and appearance‐based phenotyping. Multimodal optical sensing systems now support high‐throughput morphological, behavioral, and appearance‐based phenotyping, enabling applications ranging from biomass estimation and intelligent feeding control to selective breeding, product quality evaluation, species identification, and disease diagnosis. Despite rapid methodological progress, most existing studies remain confined to laboratory or semi‐controlled environments, and large‐scale implementation in commercial production systems is still limited. Key challenges persist in underwater image degradation, posture‐induced measurement uncertainty, data heterogeneity, model generalizability, computational constraints, and the integration of phenotypic and genomic data for precision breeding. Finally, future development pathways are discussed, including efficient and flexible data acquisition platforms, lightweight and real‐tim...