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Using Water Monitoring to Analyze the Livability of White Shrimp

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6GN for Future Wireless Networks (6GN 2020)

Abstract

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.

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References

  1. Jing, P., Jing, H., Zhan, F., Chen, Y., Shi, Y.: Agent-based simulation of autonomous vehicles: A systematic literature review. IEEE Access, vol. 1, pp. 1 (2020)

    Google Scholar 

  2. Abbassy, M.M., Ead, W.M.: Intelligent greenhouse management system. In: 2020 6th International Conference on Advanced Computing and Communication Systems (2020)

    Google Scholar 

  3. Gomathi, S., Prabha, S.U., Sureshkumar, G.: Photovoltaic powered induction motor drive using a high efficient soft switched sazz converter. In: 2020 6th International Conference on Advanced Computing and Communication Systems (2020)

    Google Scholar 

  4. Prince Samuel, S., Malarvizhi, K., Karthik, S., Mangala Gowri, S.G.: Machine learning and internet of things based smart agriculture. In: 2020 6th International Conference on Advanced Computing and Communication Systems (2020)

    Google Scholar 

  5. Madiwalar, S., Patil, S., Meti, S., Domanal, N., Ugare, K.: A survey on solar powered autonomous multipurpose agricultural robot. In: 2020 2nd International Conference on Innovative Mechanisms for Industry Applications (2020)

    Google Scholar 

  6. Vijay, Saini, A.K., Banerjee, S., Nigam, H.: An IoT instrumented smart agricultural monitoring and irrigation system. In: 2020 International Conference on Artificial Intelligence and Signal Processing (2020)

    Google Scholar 

  7. Huang, K., et al.: Photovoltaic agricultural internet of things towards realizing the next generation of smart farming. IEEE Access, vol. 1, pp. 1 (2020)

    Google Scholar 

  8. Zhu, L., Walker, J.P., Rdiger, C., Xiao, P.: Identification of agricultural row features using optical data for scattering and reflectance modelling over periodic surfaces. IEEE J. Sel. Topics Appl. Earth Obs. Remote Sens., vol. 1, pp. 1 (2020)

    Google Scholar 

  9. Jerosheja, B.R., Mythili, C.: Solar powered automated multi-tasking agricultural robot. In: 2020 International Conference on Innovative Trends in Information Technology (2020)

    Google Scholar 

  10. Iglesias, N.C., Bulacio, P., Tapia, E.: Internet of agricultural machinery: Integration of heterogeneous networks. In: 2020 IEEE International Conference on Industrial Technology (2020)

    Google Scholar 

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Acknowledgements

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.

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Correspondence to Hsin-Te Wu .

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© 2020 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Hu, WC., Wu, HT., Zhan, JW., Zhang, JM., Tseng, FH. (2020). Using Water Monitoring to Analyze the Livability of White Shrimp. In: Wang, X., Leung, V.C.M., Li, K., Zhang, H., Hu, X., Liu, Q. (eds) 6GN for Future Wireless Networks. 6GN 2020. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 337. Springer, Cham. https://doi.org/10.1007/978-3-030-63941-9_37

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  • DOI: https://doi.org/10.1007/978-3-030-63941-9_37

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-63940-2

  • Online ISBN: 978-3-030-63941-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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