Global warming poses increasing thermal stress risks in aquaculture, but early screening of heat-resilient fish still relies largely on labor-intensive behavioral observation, contact-based morphometric measurement, or single-modality phenotyping. In this study, we developed a non-invasive AI-assisted screening framework integrating behavioral performance and image-derived morphology in juvenile largemouth bass ( Micropterus salmoides ) under thermal challenge. Behavioral and morphometric phenotyping were conducted under three temperature regimes: a reference condition (27–29 °C), a moderate heat-load condition (31–33 °C), and an acute heat-stress condition (35–37 °C). Swimming duration, critical swimming speed (Ucrit), and total swimming distance were quantified using YOLO-AWD + CBT, while side-view morphometric traits were extracted using MC-MobileFishNet. When individuals were pooled across performance groups, Ucrit was significantly lower under the 35–37 °C regime than under the 27–29 °C and 31–33 °C regimes, indicating that strong thermal challenge constrained sustained swimming performance. Within each temperature regime, high-performance individuals showed greater Ucrit and total swimming distance than low-performance individuals. The ratios of caudal peduncle length to fork length and to body length were higher in high-performance individuals under the 31–33 °C and 35–37 °C regimes, whereas no such difference was detected under the 27–29 °C regime. These results suggest that these ratios may serve as candidate image-derived indicators associated with superior locomotor performance under thermal challenge. These findings provide a controlled-environment proof-of-concept for integrating AI-assisted behavioral and morphological phenotyping and identify caudal-peduncle-related ratios as candidate image-derived indicators for future heat-resilience-oriented screening in largemouth bass.
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