The large yellow croaker (Larimichthys crocea) is one of China’s most economically important marine fish species, with its cage culture production leading the nation for many years. However, the rapid expansion of aquaculture has brought challenges such as germplasm degradation, reduced disease resistance, inconsistent product quality, and low adoption of improved strains, which have hindered the sustainable development of the industry. The primary objective of this review is to summarize the current practices and challenges in seedling selection for L. crocea. The secondary objectives include discussing the influence of genetic, physiological, and environmental factors on growth performance and proposing future research directions for sustainable breeding programs. This review covers key topics including morphological screening, growth performance evaluation, genetic diversity conservation, disease resistance improvement, and adaptation to environmental stress. It also explores the application of modern technologies such as marker-assisted selection, intelligent monitoring, environmental control, precision feeding, and disease prevention. Moreover, it highlights core issues in current breeding practices, such as over-reliance on single-trait selection and insufficient integration of environmental adaptability and disease resistance. Finally, future trends are discussed, emphasizing the integration of genomic tools with artificial intelligence to promote intelligent, precise, and sustainable breeding approaches. These insights aim to enhance aquaculture productivity while supporting long-term ecological balance and industry sustainability.

Siniperca chuatsi, commonly known as mandarin fish, is one of the most economically valuable freshwater species in China. In 2022, the national aquaculture production of mandarin fish reached approximately 401,000 tons, accounting for a significant share of freshwater aquaculture in China and nearly dominating the global commercial farming landscape. With the rapid development of recirculating aquaculture systems (RASs), higher requirements have been raised for feeding efficiency and fish health monitoring. Traditional on-site visual observation methods are highly subjective, inefficient, difficult to quantify, and prone to misjudgment under conditions such as insufficient illumination, turbid water, or high stocking density. To address these challenges, this study proposes FishSegNet-PRL, an instance segmentation-based model designed to quantify the feeding intensity of mandarin fish. The model is built upon the YOLOv11-seg framework, enhanced with a P2 detection layer (P), a residual cross-stage spatial–channel attention module (RCSOSA, R), and a lightweight semantic-detail-enhanced cascaded decoder (LSDECD, L). These improvements collectively enhance small-target detection capability, boundary segmentation accuracy, and real-time inference performance. Experimental results demonstrate that FishSegNet-PRL achieves superior performance in mandarin fish instance segmentation, with a Box mAP50 of 85.7% and a Mask mAP50 of 79.4%, representing improvements of approximately 4.6% and 13.2%, respectively, compared with the baseline YOLOv11-seg model. At the application level, multiple feeding intensity quantification indices were constructed based on the segmentation results and evaluated, achieving a temporal intersection-over-union (IoU_time) of 95.9%. Overall, this approach enables objective and fine-grained assessment of mandarin fish feeding behavior, striking an effective balance between accuracy and real-time performance. It provides a feasible and efficient technical solution for intelligent feeding and behavioral monitoring in aquaculture.

Accurate morphometric analysis of Macrobrachium rosenbergii is essential for selective breeding, growth monitoring, and precision aquaculture, yet conventional manual measurements are labor-intensive, time-consuming, and prone to operator variability. This study presents ReViTA-UNet, an automated, non-contact morphometric analysis framework based on an enhanced semantic segmentation network coupled with a geometric topology refinement algorithm to accurately extract multiple morphological traits. The proposed framework integrates complementary feature extraction to improve segmentation of elongated anatomical structures and complex body boundaries. A complete automated measurement system was subsequently developed to convert segmented images into biologically meaningful morphometric parameters. The results demonstrated that ReViTA-UNet achieved a Dice coefficient of 97.7%, a mean Intersection over Union (mIoU) of 96.7%, a precision of 98.3%, and a recall of 98.4%, outperforming eight representative semantic segmentation models. The automated measurement system achieved a mean absolute percentage error of 1.83% for body length, with strong agreement with manual measurements (R² = 0.987), while maintaining high accuracy for other major morphometric traits. These results indicate that the proposed framework provides an accurate and efficient solution for automated prawn phenotyping under controlled imaging conditions. It establishes a practical foundation for future intelligent aquaculture applications following validation under commercial farming environments.