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Developing Intelligent Feeding Systems based on Deep Learning

Published: 27 September 2021 Publication History

Abstract

1The system can reduce the calculating workload of the IoT development board, as well as lowering the power consumption and guard the pool against water pollution. The intelligent feeding system offered by this study can effectively ease the workforce of the aquaculture industry. In the future, cage culture can also implement such a method to increase the safety of the operators. According to the experimental result of this study, the approach is feasible.

References

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Aleta C. Fabregas, Debrelie Cruz and Mark Daniel Marmeto, "SUGPO: A White Spot Disease Detection in Shrimps Using Hybrid Neural Networks with Fuzzy Logic Algorithm", The 6th International Conference, 2018.
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Joel Janek Dabrowski, Ashfaqur Rahman, Andrew George, Stuart Arnold and John McCulloch, "State Space Models for Forecasting Water Quality Variables: An Application in Aquaculture Prawn Farming", The 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 2018.
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Paul B. Bokingkito Jr. and Lomesindo T. Caparida, "Using Fuzzy Logic for Real - Time Water Quality Assessment Monitoring System", The 2018 2nd International Conference on Automation, Control and Robots, pp. 21--25, 2018.
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D. A. Konovalov, J. A. Domingos, C. Bajema, R. D. White and D. R. Jerry, "Ruler Detection for Automatic Scaling of Fish Images", the International Conference on Advances in Image Processing, pp. 90--95, 2017.
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Yu Wang, Rui Tan, Guoliang Xing, Xiaobo Tan, Jianxun Wang and Ruogu Zhou, "Spatiotemporal Aquatic Field Reconstruction Using Cyber-Physical Robotic Sensor Systems", ACM Transactions on Sensor Networks, Vol. 10, No.4, 2014.

Cited By

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  • (2022)A Computer Vision-Based Intelligent Fish Feeding System Using Deep Learning Techniques for AquacultureIEEE Sensors Journal10.1109/JSEN.2022.315177722:7(7185-7194)Online publication date: 1-Apr-2022

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cover image ACM Conferences
ACM ICEA '20: Proceedings of the 2020 ACM International Conference on Intelligent Computing and its Emerging Applications
December 2020
219 pages
ISBN:9781450383042
DOI:10.1145/3440943
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Publication History

Published: 27 September 2021

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Author Tags

  1. Convolutional Neural Network
  2. Deep Learning
  3. Intelligent aquaculture

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View all
  • (2022)A Computer Vision-Based Intelligent Fish Feeding System Using Deep Learning Techniques for AquacultureIEEE Sensors Journal10.1109/JSEN.2022.315177722:7(7185-7194)Online publication date: 1-Apr-2022

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