Optimization of Water Quality in Shrimp-Shallot Aquaponic Systems: A Machine Learning-Integrated IoT Approach
Keywords:
Shrimp Aquaponics, Low Salinity Water, Water Quality Regulation, IoT and Machine Learning, Tropical ClimateAbstract
This research presents a pioneering water quality regulation system designed for low salinity shrimp-shallot aquaponics in tropical climates. The system integrates cutting-edge sensor technologies, intelligent feedback loops, and precise parameter adjustments within an IoT framework. It aims to optimize critical water quality parameters, including pH, temperature, salinity, nitrite, and dissolved oxygen, to ensure the health and vitality of shrimp populations while fostering the co-cultivation of shallots. The methodology involves comprehensive model training using Genetic Algorithm on a computer, followed by real-time inference and control through an Arduino microcontroller and dispensing actuators. Thirty days of testing in a tropical aquaponics setup demonstrated the system's effectiveness in maintaining optimal water quality conditions for shrimp and shallot growth. The successful integration of Machine Learning with IoT technology signifies a transformative advancement in shrimp-shallot aquaponics, offering sustainable and intelligent solutions for commercial agriculture in tropical regions. Further scalability, adaptability to diverse climates, and integration of additional water quality parameters are envisaged for future developments, along with the exploration of remote monitoring and sustainability metrics.
Downloads
References
K. Taji, A. Sohail, F. Ghanimi, I. Ghanimi, S. ilyas, and Y. T. Ahmad, “A Systematic Literature Review of Computational Studies in Aquaponic System,” International Journal of Advanced Computer Science and Applications, vol. 14, no. 9, 2023, doi: 10.14569/ijacsa.2023.0140936.
V. M. Jothiprakash and M. V. Sezhian, “Nanocomposites based Aquaponic Cropping System Design and Control for Agriculture Industry,” International Journal of Scientific Methods in Engineering and Management, vol. 01, no. 04, pp. 12–22, 2023.
P. Kumar, P. Tiwari, and U. S. Reddy, “Estimating fish weight growth in aquaponic farming through machine learning techniques,” in 2023 3rd International Conference on Intelligent Technologies (CONIT), Jun. 2023. doi: 10.1109/conit59222.2023.10205680.
K. K. Gayam et al., “Imperative Role of Automation and Wireless Technologies in Aquaponics Farming,” Wirel Commun Mob Comput, vol. 2022, pp. 1–13, Jun. 2022, doi: 10.1155/2022/8290255.
S. B. Dhal et al., “A Machine-Learning-Based IoT System for Optimizing Nutrient Supply in Commercial Aquaponic Operations,” Sensors, vol. 22, no. 9, p. 3510, May 2022, doi: 10.3390/s22093510.
C. S. Arvind, R. Jyothi, K. Kaushal, G. Girish, R. Saurav, and G. Chetankumar, “Edge Computing Based Smart Aquaponics Monitoring System Using Deep Learning in IoT Environment,” in 2020 IEEE Symposium Series on Computational Intelligence (SSCI), Dec. 2020.
F. L. Valiente et al., “Internet of Things (IOT)-Based Mobile Application for Monitoring of Automated Aquaponics System,” in 2018 IEEE 10th International Conference on Humanoid, Nanotechnology, Information Technology,Communication and Control, Environment and Management (HNICEM), Oct. 2018. doi: 10.1109/hnicem.2018.8666439.
C. S. Arvind, R. Jyothi, K. Kaushal, G. Girish, R. Saurav, and G. Chetankumar, “Edge Computing Based Smart Aquaponics Monitoring System Using Deep Learning in IoT Environment,” in 2020 IEEE Symposium Series on Computational Intelligence (SSCI), Dec. 2020.
S. Akshay and T. K. Ramesh, “Efficient Machine Learning Algorithm for Smart Irrigation,” in 2020 International Conference on Communication and Signal Processing (ICCSP), Oct. 2020.
U. Arora, S. Shetty, R. Shah, and D. K. Sinha, “Automated Dosing System in Hydroponics with Machine Learning,” in 2021 International Conference on Communication information and Computing Technology (ICCICT), Jun. 2021.
L. S. Kondaka, R. Iyer, S. Jaiswal, and A. Ali, “A Smart Hydroponic Farming System Using Machine Learning,” in 2023 International Conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics (IITCEE), Jan. 2023.
U. Arora, S. Shetty, R. Shah, and D. K. Sinha, “Automated Dosing System in Hydroponics with Machine Learning,” in 2021 International Conference on Communication information and Computing Technology (ICCICT), Jun. 2021.
H. Jaiswal, K. R. P, R. Singuluri, and S. A. Sampson, “IoT and Machine Learning based approach for Fully Automated Greenhouse,” in 2019 IEEE Bombay Section Signature Conference (IBSSC), Oct. 2019. doi: 10.1109/ibssc47189.2019.8973086.
H. K. Srinidhi, H. S. Shreenidhi, and G. S. Vishnu, “Smart Hydroponics system integrating with IoT and Machine learning algorithm,” in 2020 International Conference on Recent Trends on Electronics, Information, Communication & Technology (RTEICT), Nov. 2020.
M. A. A. Ahmed and S. M. Reddy, “Smart Hydroponic System and Monitoring of Plants Health through Machine Learning,” Int J Res Appl Sci Eng Technol, vol. 11, no. 6, pp. 4261–4263, Jun. 2023.
M. A. A. Ahmed and S. M. Reddy, “Smart Hydroponic System and Monitoring of Plants Health through Machine Learning,” Int J Res Appl Sci Eng Technol, vol. 11, no. 6, pp. 4261–4263, Jun. 2023.
S. B. Dhal, M. Bagavathiannan, U. Braga-Neto, and S. Kalafatis, “Can Machine Learning classifiers be used to regulate nutrients using small training datasets for aquaponic irrigation?: A comparative analysis,” PLoS One, vol. 17, no. 8, p. e0269401, Aug. 2022, doi: 10.1371/journal.pone.0269401.
M. Lavanaya and R. Parameswari, “Soil Nutrients Monitoring For Greenhouse Yield Enhancement Using Ph Value with Iot and Wireless Sensor Network,” in 2018 Second International Conference on Green Computing and Internet of Things (ICGCIoT), Oct. 2018. doi: 10.1109/icgciot.2018.8753083.
G. Soltana, M. Sabetzadeh, and L. C. Briand, “Synthetic data generation for statistical testing,” in 2017 32nd IEEE/ACM International Conference on Automated Software Engineering (ASE), Oct. 2017.
F. Romanelli and F. Martinelli, “Synthetic Sensor Data Generation Exploiting Deep Learning Techniques and Multimodal Information,” IEEE Sens Lett, vol. 7, no. 7, pp. 1–4, Oct. 2023.
K. Miok, D. Nguyen-Doan, D. Zaharie, and M. Robnik-Sikonja, “Generating Data using Monte Carlo Dropout,” in 2019 IEEE 15th International Conference on Intelligent Computer Communication and Processing (ICCP), Oct. 2019.
R. Guzman, “Monte Carlo Methods on High Dimensional Data,” in LatinX in AI at Neural Information Processing Systems Conference 2018, Dec. 2018.
S. Roychowdhury, “Krushi Care: An Integrated Smart Precision Farming App for Enhanced Agricultural Productivity, Profitability and Sustainability,” International Journal of Intelligent Systems and Applications in Engineering, vol. 11, no. 9s, pp. 653–660, Jul. 2023, [Online]. Available: https://ijisae.org/index.php/IJISAE/article/view/3214
B. Chhabada, V. Narawade, and V. Mane, “Solar-Powered Smart Irrigation System with Cloud Integration,” International Journal of Intelligent Systems and Applications in Engineering, vol. 11, no. 10s, pp. 655–659, Aug. 2023, [Online]. Available: https://ijisae.org/index.php/IJISAE/article/view/3320
Zulkarnaen, C.-L. Lee, G.-C. Chung, S.-K. Wong, and K.-Y. Chan, “Dual Mode Solar Power for IoT based Smart Farming System,” International Journal of Intelligent Systems and Applications in Engineering, vol. 11, no. 8s, pp. 375–380, Jul. 2023, [Online]. Available: https://ijisae.org/index.php/IJISAE/article/view/3062
H. N. Alsammak and Z. S. Mohammed, “A Smart IoT-based Automated Irrigation for Farms Using Node MCU (ESP 32F ESP8266 MC) and A Humidity Sensor,” International Journal of Intelligent Systems and Applications in Engineering, vol. 10, no. 3s, pp. 237–248, Dec. 2022, [Online]. Available: https://ijisae.org/index.php/IJISAE/article/view/2434
V. D. Devendra Singh Mohan Prateek Mishra Priya Singh Alok Srivastav, “IoT Framework for Precision Agriculture: Machine Learning Crop Prediction,” International Journal of Intelligent Systems and Applications in Engineering, vol. 11, no. 5s, pp. 300–313, Apr. 2023, [Online]. Available: https://ijisae.org/index.php/IJISAE/article/view/2778
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
All papers should be submitted electronically. All submitted manuscripts must be original work that is not under submission at another journal or under consideration for publication in another form, such as a monograph or chapter of a book. Authors of submitted papers are obligated not to submit their paper for publication elsewhere until an editorial decision is rendered on their submission. Further, authors of accepted papers are prohibited from publishing the results in other publications that appear before the paper is published in the Journal unless they receive approval for doing so from the Editor-In-Chief.
IJISAE open access articles are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. This license lets the audience to give appropriate credit, provide a link to the license, and indicate if changes were made and if they remix, transform, or build upon the material, they must distribute contributions under the same license as the original.
