TY - GEN
T1 - Using Water Monitoring to Analyze the Livability of White Shrimp
AU - Hu, Wu Chih
AU - Wu, Hsin Te
AU - Zhan, Jun We
AU - Zhang, Jing Mi
AU - Tseng, Fan Hsun
N1 - Funding Information:
This work was supported by the Ministry of Science and Technology (MOST), Taiwan, under Grants MOST107-2221-E-346-007-MY2 and MOST109-2636-E-003-001.
Publisher Copyright:
© 2020, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.
PY - 2020
Y1 - 2020
N2 - The research develops an intelligent aquaculture system to detect the water quality of a culture pond. Additionally, using Fuzzy Logic to evaluate water quality that influences the aquaculture livability. Each species requires a different environment of water quality; therefore, the study utilizes an intelligent aquaculture system to detect the water quality of white shrimp ponds. After using Fuzzy Logic to analyze water quality, the result is delivered as equally divided into five levels of signals sections. The purpose of the research is to understand whether the aquaculture environment is suitable for white shrimps by detecting the water quality; consequently, through studying the livability to understand the importance of water quality. From the experimental results, the water quality of targeted aquaculture ponds are all within the livability range of white shrimp; the result has shown a livability rate of 33%, which is considered high livability in marine white shrimp farming. Hence, it is concluded that water quality has a high correlation with livability. Moreover, the study demonstrates that water monitoring and water quality analysis are beneficial to monitor the aquaculture environment, which can further increase the livability of white shrimp and boost income.
AB - The research develops an intelligent aquaculture system to detect the water quality of a culture pond. Additionally, using Fuzzy Logic to evaluate water quality that influences the aquaculture livability. Each species requires a different environment of water quality; therefore, the study utilizes an intelligent aquaculture system to detect the water quality of white shrimp ponds. After using Fuzzy Logic to analyze water quality, the result is delivered as equally divided into five levels of signals sections. The purpose of the research is to understand whether the aquaculture environment is suitable for white shrimps by detecting the water quality; consequently, through studying the livability to understand the importance of water quality. From the experimental results, the water quality of targeted aquaculture ponds are all within the livability range of white shrimp; the result has shown a livability rate of 33%, which is considered high livability in marine white shrimp farming. Hence, it is concluded that water quality has a high correlation with livability. Moreover, the study demonstrates that water monitoring and water quality analysis are beneficial to monitor the aquaculture environment, which can further increase the livability of white shrimp and boost income.
UR - http://www.scopus.com/inward/record.url?scp=85101326704&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85101326704&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-63941-9_37
DO - 10.1007/978-3-030-63941-9_37
M3 - Conference contribution
AN - SCOPUS:85101326704
SN - 9783030639402
T3 - Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST
SP - 458
EP - 463
BT - 6GN for Future Wireless Networks - Third EAI International Conference, 6GN 2020, Proceedings
A2 - Wang, Xiaofei
A2 - Leung, Victor C.
A2 - Li, Keqiu
A2 - Zhang, Haijun
A2 - Hu, Xiping
A2 - Liu, Qiang
PB - Springer Science and Business Media Deutschland GmbH
T2 - 3rd EAI International Conference on 6G for Future Wireless Networks, 6GN 2020
Y2 - 15 August 2020 through 16 August 2020
ER -
