## 3 Application of Emerging Technologies in Seeding Selection for Large Yellow Croaker
### Intelligent Feeding Technology  
Feed, as a critical input in aquaculture, directly affects the growth rate and disease resistance of large yellow croaker fry, with its type, nutritional composition, and particularly the content of animal protein being key factors [97; 98]. To improve feed utilization efficiency and reduce farming costs, intelligent feeding technologies are gradually becoming an important development direction in modern aquaculture management. Intelligent feeding systems can dynamically adjust the feed amount and frequency based on real-time monitoring data, including fish feeding behavior and water quality parameters, thereby optimizing feed supply, reducing waste, and enhancing feed conversion efficiency [99; 100]. Furthermore, culturists can adjust feed formulations based on real-time monitoring data from intelligent systems, tailoring nutritional profiles according to the growth stage, health status, and environmental conditions of seedlings to ensure adequate and balanced nutritional support [101]. In recent years, the application of artificial intelligence (AI) technology in intelligent feeding has made significant progress. [PERSON] et al. [102] achieved accurate identification and tracking of large yellow croaker behavior based on an optimized YOLOv5 object detection algorithm, providing an effective basis for scientific feeding. [PERSON] et al. [103] studied the spatial distribution characteristics of large yellow croaker in cage farming areas using a small fish-finding unmanned boat and found that they were mainly concentrated in the middle and lower water layers, where the water flow was slower, rocks were abundant, and light was weaker, providing data support for spatially precise feeding. \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Environmental Factors** & **Specific Parameters** & **Mechanisms of Influence** & **Optimization Recommendations** \\ \hline
**Water conditions** & Temperature, dissolved oxygen, ammonia nitrogen concentration, pH, and salinity & High temperatures may trigger stress responses, while low temperatures slow growth. Insufficient dissolved oxygen suppresses metabolism; excessive ammonia causes toxicity. pH affects immune function, and improper salinity disrupts feed conversion efficiency. & Apply smart temperature control systems to maintain optimal ranges; ensure adequate oxygen supply; stabilize water quality, and adjust salinity appropriately. \\ \hline
**Stocking density** & Larval stage: 1000–6000 fish/m3; Grow-out stage: 12–30 fish/m3 & Excessive density leads to water quality deterioration, poor growth, and lower survival rates; overly low density wastes resources and reduces farming efficiency. & Adjust stocking density according to growth stage; use aeration systems to maintain water quality; prevent overcrowding to improve survival rates. \\ \hline
**Light**[95; 96] & Light intensity, photoperiod & Insufficient light suppresses feeding motivation; overly strong light causes fry to cluster, leading to localized hypoxia. & Adjust light intensity and duration to simulate natural lighting conditions. \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Influence of environmental factors on the selection of large yellow croaker (_Lartimicththts crocea_) broodstock. REN et al. [104] studied the vocalization characteristics of large yellow croaker in different behavioral states and found that the fish emitted sounds with a frequency of approximately 800 Hz and a simple single pulse when foraging, while their spawning behavior was characterized by double or triple pulses with longer intervals between pulses. This finding provides a possible basis for acoustic recognition-based feeding timing judgment.