Prediction of Water Quality Temperature in the Growth Pattern of Fish (Nile Tilapia) and Plant (Lettuce) in a Prototype-Safe, Automated Aquaponics Environment Using Deep Learning
The increasing demand for water resources, decreased land water availability, and concerns about food security have led to the development of innovative food production methods, such as aquaponics. Fish tank organic waste is fed to plants for growth, with perforated water returning to fish, reducing water consumption and reusing water compared to traditional agricultural practices. This hybrid technology combines aquaculture with hydroponics, requiring constant monitoring of water quality parameters to prevent fish death and specific fish such as Catfish and Salmon and specific plants including Spinach, Cabbage, Kintonmire, and Cauliflower fit well in the aquaponics system. This research aims to build an aquaponics system using a machine learning tool for monitoring water quality temperature, reducing manual tasks, and improving accuracy. The findings show aquaponics as an ideal solution for food production, focusing on heat exchanges and water temperature. The Convolutional LSTM model performed better than the Recurrent neural network, predicting high scores of 96%, 98%, and 99% with different power levels after 200 epochs and a 64-batch batch size respectively 5, 10, and 15 watts.
Cite this paper
Owusu, R. , Mayoko, J. C. and Lee, J. Y. (2024). Prediction of Water Quality Temperature in the Growth Pattern of Fish (Nile Tilapia) and Plant (Lettuce) in a Prototype-Safe, Automated Aquaponics Environment Using Deep Learning. Open Access Library Journal, 11, e1786. doi: http://dx.doi.org/10.4236/oalib.1111786.
Liu, X., Guo, J., Zheng, X., Yao, Z., Li, Y. and Li, Y. (2024) Intelligent Plant Growth Monitoring System Based on LSTM Network. IEEE Sensors Journal, 24, 15073-15081. https://doi.org/10.1109/jsen.2024.3376818
Goddek, S., Joyce, A., Kotzen, B. and Dos-Santos, M. (2019) Aquaponics and Global Food Challenges. In: Goddek, S., Joyce, A., Kotzen, B. and Burnell, G.M., Eds., Aquaponics Food Production Systems, Springer International Publishing, 3-17. https://doi.org/10.1007/978-3-030-15943-6_1
Tyson, R.V., Treadwell, D.D. and Simonne, E.H. (2011) Opportunities and Challenges to Sustainability in Aquaponic Systems. HortTechnology, 21, 6-13. https://doi.org/10.21273/horttech.21.1.6
Vermeulen, T. and Kamstra, A. (2013) The Need for Systems Design for Robust Aquaponic Systems in the Urban Environment. Acta Horticulturae, 1004, 71-77. https://doi.org/10.17660/actahortic.2013.1004.6
Frimpong, F., Amponsah, S., Danquah, E.O., et al. (2021) Assessment of Aquaponics-Based Food Production System Effluents on the Performance of Maize. International Journal of Agriculture: Research and Review, 5, 615-627.
Mayoko Biong J.C., Lubamba M.C., Tamina M. S., Faya M.H., and Kasiangula A.X. (2024) Place of Validation between the Conceptual and Logical Data Model of the Method of Study and Computer Implementation for the Company’s System. International Journal of Recent Innovations in Academic Research, 8, 67-79. https://doi.org/10.5281/zenodo.10938025
Yang, X., Zhang, S., Liu, J., Gao, Q., Dong, S. and Zhou, C. (2020) Deep Learning for Smart Fish Farming: Applications, Opportunities and Challenges. Reviews in Aquaculture, 13, 66-90. https://doi.org/10.1111/raq.12464
Bradley, D., Merrifield, M., Miller, K.M., Lomonico, S., Wilson, J.R. and Gleason, M.G. (2019) Opportunities to Improve Fisheries Management through Innovative Technology and Advanced Data Systems. Fish and Fisheries, 20, 564-583. https://doi.org/10.1111/faf.12361
Hassoun, M.H., Intrator, N., McKay, S. and Christian, W. (1996) Fundamentals of Artificial Neural Networks. Computers in Physics, 10, 137-137. https://doi.org/10.1063/1.4822376
Alam, G., Ihsanullah, I., Naushad, M. and Sillanpää, M. (2022) Applications of Artificial Intelligence in Water Treatment for Opti-mization and Automation of Adsorption Processes: Recent Advances and Prospects. Chemical Engineering Journal, 427, Article 130011. https://doi.org/10.1016/j.cej.2021.130011
Mamdouh, M., Ezzat, M. and Hefny, H. (2024) Improv-ing Flight Delays Prediction by Developing Attention-Based Bidirectional LSTM Network. Expert Systems with Applications, 238, Article 121747. https://doi.org/10.1016/j.eswa.2023.121747
Mayoko, J.C., Itakala, A., Lubamba, C., Nyazabe, S., Kikata, E., Lamanabwe, H. and Tamina, S. (2024) Facial Recognition System Based on the Convo-lutional Neural Network and OpenCV for Automatic Attendance at the Civil Service of Kwilu Provincial Government DR Congo. IOSR Journal of Computer Engineering, 26, 58-70. https://www.iosrjournals.org/iosr-jce/pages/26(2)Series-2.html
Jun Wang, (1993) Analysis and Design of a Recur-rent Neural Network for Linear Programming. IEEE Transactions on Circuits and Systems I: Fundamental Theory and Ap-plications, 40, 613-618. https://doi.org/10.1109/81.244913
Siami-Namini, S., Tavakoli, N. and Siami Namin, A. (2018) A Comparison of ARIMA and LSTM in Forecasting Time Series. 2018 17th IEEE International Conference on Ma-chine Learning and Applications (ICMLA), Orlando, 17-20 December 2018, 1394-1401. https://doi.org/10.1109/icmla.2018.00227
Kiperwasser, E. and Goldberg, Y. (2016) Simple and Accurate Depend-ency Parsing Using Bidirectional LSTM Feature Representations. Transactions of the Association for Computational Linguis-tics, 4, 313-327. https://doi.org/10.1162/tacl_a_00101
Zhang, Y., Xu, S., Zhang, L., Jiang, W., Alam, S. and Xue, D. (2024) Short-Term Multi-Step-Ahead Sector-Based Traffic Flow Prediction Based on the Attention-Enhanced Graph Convo-lutional LSTM Network (AGC-LSTM). Neural Computing and Applications. https://doi.org/10.1007/s00521-024-09827-3
Mikolov, T., Kombrink, S., Burget, L., Cernocky, J. and Khudanpur, S. (2011) Extensions of Recurrent Neural Network Language Model. 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Prague, 22-27 May 2011, 5528-5531. https://doi.org/10.1109/icassp.2011.5947611
Kayama, K., Kanno, M., Chisaki, N., Tanaka, M., Yao, R., Hanazono, K., et al. (2021) Prediction of PCR Amplification from Primer and Template Sequences Using Recurrent Neural Network. Scien-tific Reports, 11, Article No. 7493. https://doi.org/10.1038/s41598-021-86357-1
Zachary, C. and Berkowitz, J. (2014) A Critical Review of Recurrent Neural Networks for Sequence Learning. arXiv: 1506.00019. https://doi.org/10.48550/arXiv.1506.00019
Kratzert, F.K.D. (2019) Benchmarking a Catchment-Aware Long Short-Term Memory Network (LSTM) for Arge-Scale Hydrological Modeling. Hydrology and Earth System Sciences, Preprint. https://doi.org/10.5194/hess-2019-368
Alhamid M. (2021) LSTM and Bidirectional LSTM for Regression. Towards Data Science, 1-7. https://towardsdatascience.com/lstm-and-bidirectional-lstm-for-regression-4fddf910c655
Almu-tairi, M.S., Almutairi, K. and Chiroma, H. (2023) Hybrid of Deep Recurrent Network and Long Short Term Memory for Rear-End Collision Detection in Fog Based Internet of Vehicles. Expert Systems with Applications, 213, Article 119033. https://doi.org/10.1016/j.eswa.2022.119033
Kumbure, M.M., Lohrmann, C., Luukka, P. and Porras, J. (2022) Ma-chine Learning Techniques and Data for Stock Market Forecasting: A Literature Review. Expert Systems with Applications, 197, Article 116659. https://doi.org/10.1016/j.eswa.2022.116659
Jiang, W. (2021) Applications of Deep Learning in Stock Market Prediction: Recent Progress. Expert Systems with Applications, 184, Article 115537. https://doi.org/10.1016/j.eswa.2021.115537
Arvind, C.S., Jyothi, R., Kaushal, K., Girish, G., Saurav, R. and Chetan-kumar, G. (2020) Edge Computing Based Smart Aquaponics Monitoring System Using Deep Learning in IoT Environment. 2020 IEEE Symposium Series on Computational Intelligence (SSCI), Canberra, 1-4 December 2020, 1485-1491. https://doi.org/10.1109/ssci47803.2020.9308395
Sverdrup, H., Eklund, H. and Bjerle, I. (1981) Kalkning av rin-nande vatten-Erfarenheter från en fluidiserad kalkbrunn, Mover. Vatten: Tidskrift för vattenvård, 37, 388-394.
