Earticle

초록

영어

The purpose of our study is to design datasets for Artificial Intelligence learning for cold sea fish farming. Salmon is considered one of the most popular fish species among men and women of all ages, but most supplies depend on imports. Recently, salmon farming, which is rapidly emerging as a specialized industry in Gangwon-do, has attracted attention. Therefore, in order to successfully develop salmon farming, the need to systematically build data related to salmon and salmon farming and use it to develop aquaculture techniques is raised. Meanwhile, the catch of pollack continues to decrease. Efforts should be made to improve the major factors affecting pollack survival based on data, as well as increasing the discharge volume for resource recovery. To this end, it is necessary to systematically collect and analyze data related to pollack catch and ecology to prepare a sustainable resource management strategy. Image data was obtained using CCTV and underwater cameras to establish an intelligent aquaculture strategy for salmon and pollock, which are considered representative fish species in Gangwon-do. Using these data, we built learning data suitable for AI analysis and prediction. Such data construction can be used to develop models for predicting the growth of salmon and pollack, and to develop algorithms for AI services that can predict water temperature, one of the key variables that determine the survival rate of pollack. This in turn will enable intelligent aquaculture and resource management taking into account the ecological characteristics of fish species. These studies look forward to achievements on an important level for sustainable fisheries and fisheries resource management.

목차

Abstract
1. Introduction
2. Dataset for Artificial Intelligence
2.1. Concept of dataset
2.2. Status of data labeling
3. Data collection for cold sea fish farming
3.1. Procedure of data collection
3.2. Considerations of data collection
4. Data refinement for cold sea fish farming
4.1. Procedure of data refinement
4.2. Considerations of data refinement
5. Data labeling for cold sea fish farming
5.1. Procedure of data labeling
5.2. Considerations of data labeling
6. Conclusions
Acknowledgement
References

키워드

저자

• Sung-Hyun KIM [ Executive Researcher, Dept. of Data, National Information Society Agency, Korea ]
• Seongtak OH [ Research Fellow, Dept. of ICT Infrastructure & Platform, National Information Society Agency, Korea ]
• Sangwon LEE [ Professor, Dept. of Computer & Software Engineering, Wonkwang Univ., Korea ] Corresponding Author

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  The International Journal of Advanced Smart Convergence

• 간기

  계간

• pISSN

  2288-2847

• eISSN

  2288-2855

• 수록기간

  2012~2025

• 십진분류

  KDC 326 DDC 380