Wu-Chih Hu
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.
- 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. Google Scholar
Digital Library
- 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. Google ScholarDigital Library
- 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. Google ScholarDigital Library
- 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. Google ScholarDigital Library
- 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. Google ScholarDigital Library
Index Terms
- Developing Intelligent Feeding Systems based on Deep Learning
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