Applying Artificial Intelligence (AI) Techniques to Implement a Practical Smart Cage Aquaculture Management System

Autor: Ren-Jie Huang, Yan-Tsung Peng, Chen-Chou Lin, Yen-Hsiang Liao, Cheng-Ting Huang, Chin-Yang Lin, Jung-Hua Wang, Jih-Gau Juang, Yii-Shing Huang, Te-Hua Hsu, Tzong-Dar Wu, Shyi-Chy Cheng, Chung-Cheng Chang, Jia-Yao Jhang, Hsieh Yi-Zeng, Jenq-Lang Wu, Chin-Chun Chang, Chyng-Hwa Liou
Rok vydání: 2021
Předmět:
Zdroj: Journal of Medical and Biological Engineering.
ISSN: 2199-4757
1609-0985
DOI: 10.1007/s40846-021-00621-3
Popis: Purpose This paper presents our team’s results to establish an AIoT smart cage culture management system. Methods According to the built system, the farmed field information is transmitted to the data platform of Ocean Cloud, and all collected data and analysis results can be applied to the cage culture field after the bigdata analysis. Results This management system successfully integrates AI and IoT technologies and is applied in cage culture. Using underwater biological analysis images and AI feeding as examples, this paper explains how the system integrates AI and IoT into a feasible framework that can constantly acquire information about the health status of fish, survival rate of fish, as well as the feed residuals. Conclusion The results of our research enable the aquaculture operators or owners to efficiently reduce the feed residual, monitor the growth of fish, and increase fish survival rate, thereby increasing the feed conversion rate.
Databáze: OpenAIRE
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