Abstract
Fish phenotyping plays an important role in growth evaluation, selective breeding, and precision aquaculture. Conventional phenotypic measurement methods are labor-intensive, time-consuming, and susceptible to observer variability. To improve measurement efficiency and reproducibility, this study proposes an automated fish phenotyping framework based on Transformer-enhanced instance segmentation. Specifically, a Mask2Former decoder was integrated into the Mask R-CNN architecture to improve boundary delineation and segmentation quality. Based on segmentation outputs, phenotypic parameters, including body length, body height, and projected area, were automatically extracted using PCA-assisted orientation estimation and geometric measurement. In addition, a standardized anatomical landmark annotation framework consisting of 12 reference points was introduced to support reproducible phenotypic description and future extensible morphometric analysis. Body weight was further estimated using polynomial regression based on extracted morphological traits. Experiments were conducted using images from three crucian carp varieties under controlled imaging conditions. The proposed framework achieved 92.7% mAP and 89.4% Boundary IoU, improving segmentation performance over the baseline model. Automated measurement yielded average relative errors of 2.16% for body length and 3.85% for body height, while weight prediction achieved an R2 of 0.9479 and a mean relative error of 7.31%. These results demonstrate that Transformer-enhanced segmentation can support accurate and efficient automated phenotyping under standardized conditions and provide a foundation for future deployment in more complex aquaculture environments.