## 3 Application of Emerging Technologies in Seeding Selection for Large Yellow Croaker
### Intelligent Monitoring and Data Analysis Technology  
Intelligent monitoring and data analysis technology provides real-time and comprehensive data support for the selection of high-quality _Larimichthys crocea_ seedlings [70]. By relying on Internet of Things (IoT) sensors, video monitoring systems, and data pro  
Figure 3: Methods applied in gene editing in fish [58; 67; 68]. Notes:The top-left and top-right insets show Manhattan plots from QTL and GWAS analyses. Alternating colors indicate different chromosomes for visual clarity. Each point represents a SNP, with the y-axis showing -log\({}_{10}\)(\(p\)-value) of association with the trait. The red horizontal line denotes the genome-wide significance threshold; points above it are significantly associated loci.  
cessing platforms, aquaculture managers can fully monitor the growth dynamics, health status, and environmental parameters of the seedlings, providing data-based evidence and decision-making support for scientific selection (Figure 4).  
Video monitoring, combined with image recognition algorithms, enables real-time [PERSON] and recording of external features, swimming behaviors, and growth status of the fish, aiding in the selection of individuals with superior growth performance [71]. At the same time, [PERSON] technology, by deploying multi-parameter sensors for dissolved oxygen, temperature, pH, ammonia nitrogen, and other factors, ensures continuous monitoring of the aquaculture water environment, providing optimal growth conditions for the seedlings [72]. By integrating big data and artificial [PERSON] technologies, growth data and environmental adaptability can be modeled and analyzed, assisting in predicting growth trends and health status, thus improving the selection efficiency [73; 74]. [PERSON] et al. [75] monitored water quality factors, meteorological factors, and biological factors, and established a disease forecasting model for cage-cultured _Larimichthys croca_ based on multivariate statistical analysis, gray theory, and BP neural networks. The model identified key environmental factors, such as water temperature, transparency, and suspended solids, with a prediction accuracy of up to 81.53%, providing scientific evidence for early warning and prevention of diseases in _Larimichthys crocea_. [PERSON] et al. [71] proposed an abnormal behavior monitoring method for _Larimichthys crocea_ in recirculating aquaculture systems based on computer vision. Using the improved YOLOX-S algorithm, abnormal behaviors were detected in real time, and the Bytetrack algorithm was used for tracking, achieving a detection accuracy of 98.4% and tracking accuracy over 95%, effectively improving aquaculture monitoring efficiency. [PERSON] et al. [70] introduced an automated method based on computer image recognition technology for diagnosing Ichthyophyllhirius multifilis disease in industrial marine aquaculture of _Larimichthys crocea_. The model was trained using transfer learning with the YOLOv3 algorithm, achieving an accuracy of 92% for disease recognition. Notably, the \"Conson No. 1\" smart aquaculture vessel--equipped with automated waste removal, circulating seawater to simulate natural currents, noise-reduction systems, and IoT-based environment monitoring--has been certified by the Aquaculture Stewardship Council. It sustainability produces approximately 3700 tonnes of large yellow croaker annually [76].  
Figure 4: Overall framework of intelligent monitoring technology and data analysis system [77; 78].  
Intelligent technology provides real-time, comprehensive monitoring and data analysis, reducing human operational errors, improving the efficiency and accuracy of selection, and enhancing the level of automation in aquaculture management [79]. However, the high capital investment, technical support requirements, and long-term maintenance costs remain significant challenges for its widespread application [80].