Intelligent Recirculating Aquaculture System of Oreochromis Niloticus: A Feed-Conversion-Ratio-Based Machine Learning Approach
Keywords:
FCR, Multiple Linear Regression, intelligent RAS, Water quality parametersAbstract
Aquaculture has emerged as a critical component in satisfying the world's increasing demand for high-quality protein while relieving strain on wild fish populations. Oreochromis niloticus, also known as Nile tilapia, is one of the most economically important aquaculture species. Optimizing manufacturing efficiency and limiting resource waste, on the other hand, remains a difficulty. The construction of an Intelligent Recirculating Aquaculture System (IRAS) powered by a Feed-Conversion-Ratio (FCR)-based Machine Learning (ML) framework is used in this study to improve the sustainability and productivity of Oreochromis niloticus aquaculture. To establish a closed-loop aquaculture environment, the IRAS incorporates advanced sensor technologies, real-time data monitoring, and control systems. The use of machine learning algorithms trained on historical and real-time FCR data to anticipate and improve the feeding regime for Nile tilapia is central to this approach. The ML model modifies feeding schedules and quantities to enhance growth while decreasing feed waste and the associated environmental effects by continuously learning from FCR patterns. Furthermore, this study shows the feasibility and usefulness of the FCR-based ML strategy in enhancing feed utilization efficiency, growth rates, and overall performance of Oreochromis niloticus in an IRAS through a series of studies. The results show a significant reduction in feed waste and expenses, resulting in improved economic viability and environmental sustainability of the aquaculture system. Furthermore, the ML-driven system adapts to changing environmental conditions and improves the fish population's general health and well-being..
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V. Vishwakarma, A. Gurav, H. Patel, and S. Sahasrabudhe, Acqua culture monitoring system. 2018.
J. A. V. Magsumbol, V. J. Almero, M. Rosales, A. A. Bandala, and E. P. Dadios, “A Fuzzy Logic Approach for Fish Growth Assessment,” 2019 IEEE 11th Int. Conf. Humanoid, Nanotechnology, Inf. Technol. Commun. Control. Environ. Manag. HNICEM 2019, pp. 9–12, 2019, doi: 10.1109/HNICEM48295.2019.9072756.
Zhao, S., Zhang, S., Liu, J., Wang, H., Zhu, J., Li, D., & Zhao, R. (2021a). Application of machine learning in intelligent fish aquaculture: A review. Aquaculture, 540(March), 736724. https://doi.org/10.1016/j.aquaculture.2021.736724
S. Mcginty and J. E. Rakocy, “Cage Culture Of Tilapia,” Water, no. 281, pp. 1–13, 2021.
O’Neill, E. A., Stejskal, V., Clifford, E., & Rowan, N. J. (2020). Novel use of peatlands as future locations for the sustainable intensification of freshwater aquaculture production – A case study from the Republic of Ireland. Science of the Total Environment, 706, 136044. https://doi.org/10.1016/j.scitotenv.2019.136044
Yang, X., Zhang, S., Liu, J., Gao, Q., Dong, S., & Zhou, C. (2021). Deep learning for smart fish farming: applications, opportunities and challenges. Reviews in Aquaculture, 13(1), 66–90. https://doi.org/10.1111/raq.12464
Jie, C., Yingying, S., Junhui, W., Yusheng, W., Huiping, S., & Kaiyan, L. (2019). Intelligent Control and Management System for Recirculating Aquaculture. 2019 IEEE 2nd International Conference on Electronics and Communication Engineering, ICECE 2019, 438–443. https://doi.org/10.1109/ICECE48499.2019.9058567
Liakos, K. G., Busato, P., Moshou, D., Pearson, S., & Bochtis, D. (2018). Machine learning in agriculture: A review. Sensors (Switzerland), 18(8), 1–29. https://doi.org/10.3390/s18082674
Shen, S., Jiang, C., & Zhu, D. (2010). Modeling and simulation of milkfish farming and coral growth in Bolinao. 2010 International Conference on Computational Intelligence and Software Engineering, CiSE 2010. https://doi.org/10.1109/CISE.2010.5677121
Africa, A. D. M., Aguilar, J. C. C. A., Lim, C. M. S., Pacheco, P. A. A., & Rodrin, S. E. C. (2017). Automated aquaculture system that regulates Ph, temperature and ammonia. HNICEM 2017 - 9th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment and Management, 2018-Janua, 1–6. https://doi.org/10.1109/HNICEM.2017.8269494
Nicolas-Mindoro, J. (2020). Fuzz-Fish-v2-IJATCSE. International Journal of Advanced Trends in Computer Science and Engineering.
V. Vishwakarma, A. Gurav, H. Patel, and S. Sahasrabudhe, Acqua culture monitoring system. 2018.
Ebeling, J. M., & Timmons, M. B. (2012). Recirculating Aquaculture Systems. Aquaculture Production Systems, 245–277. https://doi.org/10.1002/9781118250105.ch11
X. Peng, L. Chen, Y. Yu, and D. Wang, “PCA-GRNN-GA Based PH Value Prediction Model Applied in Penaeus Orientalis Culture,” Proc. - 2016 Int. Conf. Digit. Home, ICDH 2016, pp. 227–232, 2017, doi: 10.1109/ICDH.2016.054.
T. J. Akaahan, “Evaluation of Water Quality Suitability in River Benue at Makurdi for Aquaculture Production in Benue State, Nigeria,” Int. Sci. Res. J., vol. 1, no. 06, pp. 66–73, 2015, doi: 10.18483/irjsci.64.
D. M. Africa, J. C. C. A. Aguilar, C. M. S. Lim, P. A. A. Pacheco, and S. E. C. Rodrin, “Automated aquaculture system that regulates Ph, temperature and ammonia,” HNICEM 2017 - 9th Int. Conf. Humanoid, Nanotechnology, Inf. Technol. Commun. Control. Environ. Manag., vol. 2018-Janua, pp. 1–6, 2017, doi: 10.1109/HNICEM.2017.8269494.
S. Shen, C. Jiang, and D. Zhu, “Modeling and simulation of milkfish farming and coral growth in Bolinao,” 2010 Int. Conf. Comput. Intell. Softw. Eng. CiSE 2010, 2010, doi: 10.1109/CISE.2010.5677121.
W. Shan, S. Cai, and C. Liu, “A new comprehensive evaluation method for water quality: Improved fuzzy support vector machine,” Water (Switzerland), vol. 10, no. 10, 2018, doi: 10.3390/w10101303.
X. Xia, Z. Yang, G. H. Huang, and I. Maqsood, “Integrated Evaluation of Water Quality and Quantity of the Yellow River,” Water Int., vol. 29, no. 4, pp. 423–431, Dec. 2004, doi: 10.1080/02508060408691804.
L. L. Loyola, L.G., Lacatan, “Survivability of Aqua Marine Products in Fish Ponds through Water Quality Evaluation Using Machine Learning Algorithm,” Test Eng. Manag., vol. 82, no. February, pp. 3954–3960, 2020, [Online]. Available: https://www.academia.edu/42475808/Survivability_of_Aqua_Marine_Products_in_Fish_Ponds_through_Water_Quality_Evaluation_Using_Machine_Learning_Algorithm?from=cover_page.
C. Feng, J. Yuan, Y. Sun, and J. You, “Design of Water Quality Monitoring System,” Proc. - 2020 Int. Conf. Artif. Intell. Comput. Eng. ICAICE 2020, pp. 264–267, 2020, doi: 10.1109/ICAICE51518.2020.00057.
R. Islam et al., “Assessment of pH and Total Dissolved Substances (TDS) in the Commercially Available Bottled Drinking Water,” vol. 6, no. 5, pp. 35–40, 2017, doi: 10.9790/1959-0605093540.
M. Endler, J. P. Briot, F. Silva E Silva, V. P. De Almeida, and E. H. Haeusler, “Towards stream-based reasoning and machine learning for IoT applications,” 2017 Intell. Syst. Conf. IntelliSys 2017, vol. 2018-Janua, no. September, pp. 202–209, 2018, doi: 10.1109/IntelliSys.2017.8324292.
R. Medar, V. S. Rajpurohit, and B. Rashmi, “Impact of Training and Testing Data Splits on Accuracy of Time Series Forecasting in Machine Learning,” 2017 Int. Conf. Comput. Commun. Control Autom. ICCUBEA 2017, no. April 2020, pp. 1–6, 2018, doi: 10.1109/ICCUBEA.2017.8463779.
N. Li, Z. Fu, W. Cai, and X. Zhang, “Using Support Vector Machines to Predict the Variation of Organic Pollutants in Pond Water,” IEEE, 2007. https://www.infona.pl/resource/bwmeta1.element.ieee-art-000004304482/tab/summary (accessed Jan. 15, 2022).
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