Innovations in Brackish Water Aquaponics: Utilizing IoT and Genetic Algorithms for Water Quality Management and Organic Nutrient Optimization
Keywords:
Brackish water aquaponics, Internet of Things, Machine Learning, Genetic Algorithm, Organic liquid fertilizer, Whiteleg Shrimp, Plants, Nutrient optimizationAbstract
The use of brackish water aquaponics systems offers promising opportunities for achieving sustainable food production. However, improving water quality and nutrient parameters remains a significant barrier. This study presents a comprehensive intelligent system aimed at improving water quality and maximizing nutrient utilization in a brackish water aquaponics system. The system integrates the cultivation of Whiteleg Shrimp (Litopenaeus vannamei) with various vegetables including melon, pumpkin, watermelon, and cucumber. This novel method combines Internet of Things (IoT) technology, machine learning-based genetic algorithms, and precise nutrient management using organic liquid fertilizers. The system consists of a sensor subsystem that monitors various water quality parameters such as pH, temperature, dissolved oxygen, salinity, and nitrite levels. The system also includes an organic liquid fertilizer subsystem for nutrient addition, feedback for continuous evaluation, and an actuator system to implement corrective actions. The implementation of genetic algorithms is used to predict optimal parameters for water quality and organic liquid fertilizer dosage. The system control is achieved through the use of Arduino. After one month of testing, it was shown that the system successfully maintained water quality parameters within the optimal range. These parameters include pH (7.5-7.9), temperature (27.5-29.0°C), nitrite (0.05-0.10 ppm), salinity (6.5-8.5 ppt), and dissolved oxygen (6.8-7.4 ppm). The use of optimized organic liquid fertilizer at a concentration of 10-15 ml/L resulted in significant increases in plant and shrimp growth compared to traditional systems. This study demonstrates the ability to combine IoT, machine learning, and nutrient optimization to improve the effectiveness and yield of brackish water aquaponics systems. This approach provides a way to address sustainability and food security challenges. However, further research is needed to validate its effectiveness on a larger scale and for longer periods.
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References
S. Goddek, A. Joyce, B. Kotzen, and G. M. Burnell, "Aquaponics Systems: Combined Aquaculture and Hydroponic Production Technologies for the Future," Springer Nature, 2019, doi: 10.1007/978-3-030-15943-6.
K. Johnson and S. Lee, "Water Efficiency in Aquaponic Systems," Water Resources Management, vol. 33, no. 12, pp. 4233-4246, 2019, doi: 10.1007/s11269-019-02345-1.
T. Brown, "Global Water Scarcity and Food Production," Environmental Sciences, vol. 54, no. 3, pp. 289-305, 2021, doi: 10.1080/00139157.2021.1898908.
M. Garcia, J. Smith, and R. Williams, "Urban Aquaponics and Food Security," Sustainable Cities and Society, vol. 76, p. 103455, 2022, doi: 10.1016/j.scs.2021.103455.
E. Wilson, "Nutrient Imbalances in Aquaponic Systems," Journal of Plant Nutrition, vol. 41, no. 12, pp. 1629-1644, 2018, doi: 10.1080/01904167.2018.1459688.
R. Thompson and L. Nguyen, "Plant Growth and Yield in Aquaponic Systems," Horticulturae, vol. 6, no. 3, p. 39, 2020, doi: 10.3390/horticulturae6030039.
Rodriguez, C. Martinez, and J. Lopez, "Challenges in Brackish Water Aquaponics," Aquacultural Engineering, vol. 92, p. 102130, 2021, doi: 10.1016/j.aquaeng.2020.102130.
Y. Chen and M. Wong, "Salinity Effects on Nutrient Uptake in Aquaponics," Journal of Plant Physiology, vol. 237, pp. 1-8, 2019, doi: 10.1016/j.jplph.2019.03.002.
T. Davis, "Physiological Requirements of Brackish Water Organisms in Aquaponics," Aquaculture, vol. 546, p. 737314, 2022, doi: 10.1016/j.aquaculture.2021.737314.
J. Kim, S. Park, and H. Lee, "Organic Liquid Fertilizers in Hydroponic Systems," Scientia Horticulturae, vol. 281, p. 109953, 2021, doi: 10.1016/j.scienta.2021.109953.
R. Yanes, P. Martinez, and R. Ahmad, "Towards automated aquaponics: A review on monitoring, IoT, and smart systems," Journal of Cleaner Production, vol. 263, p. 121571, 2020, doi: 10.1016/j.jclepro.2020.121571.
H. Monsees, K. J. Keesman, and W. Kloas, "Optimizing greenhouse water and nutrient management for aquaponics using the system dynamics approach," Aquaculture Environment Interactions, vol. 9, pp. 361-377, 2017, doi: 10.3354/aei00239.
R. V. Tyson, D. D. Treadwell, and E. H. Simonne, "Opportunities and challenges to sustainability in aquaponic systems," HortTechnology, vol. 21, no. 1, pp. 6-13, 2011, doi: 10.21273/HORTTECH.21.1.6.
K. Jha, A. Doshi, P. Patel, and M. Shah, "A comprehensive review on automation in agriculture using artificial intelligence," Artificial Intelligence in Agriculture, vol. 2, pp. 1-12, 2019, doi: 10.1016/j.aiia.2019.05.004.
Saghai and J. P. Lobo Ferreira, "Potential of Internet of Things and Machine Learning Techniques in Aquaponics Systems," Sustainable Computing: Informatics and Systems, vol. 28, p. 100421, 2020, doi: 10.1016/j.suscom.2020.100421.
Bhatnagar and P. Devi, "Water quality guidelines for the management of pond fish culture," International Journal of Environmental Sciences, vol. 3, no. 6, pp. 1980-2009, 2013, doi: 10.6088/ijes.2013030600019.
R. Yanes, P. Martinez, and R. Ahmad, "Towards automated aquaponics: A review on monitoring, IoT, and smart systems," Journal of Cleaner Production, vol. 263, p. 121571, 2020, doi: 10.1016/j.jclepro.2020.121571.
S. M. Pinho, G. L. de Mello, K. M. Fitzsimmons, and M. G. C. Emerenciano, "Closed aquaponic systems: State of the art and future challenges," Reviews in Aquaculture, vol. 13, no. 4, pp. 2023-2041, 2021, doi: 10.1111/raq.12529.
M. Eck, A. R. Sare, S. Massart, Z. Schmautz, R. Junge, T. H. Smits, and M. H. Jijakli, "Exploring bacterial communities in aquaponic systems," Water, vol. 11, no. 2, p. 260, 2019, doi: 10.3390/w11020260.
R. Junge, B. König, M. Villarroel, T. Komives, and M. H. Jijakli, "Strategic points in aquaponics," Water, vol. 9, no. 3, p. 182, 2017, doi: 10.3390/w9030182.
H. W. Palm, U. Knaus, S. Appelbaum, S. M. Strauch, and B. Kotzen, "Coupled aquaponics systems," in Aquaponics Food Production Systems, Springer, Cham, 2019, pp. 163-199, doi: 10.1007/978-3-030-15943-6_7.
K. B. Newhart, R. W. Holloway, A. S. Hering, and T. Y. Cath, "Data-driven performance analyses of wastewater treatment plants: A review," Water research, vol. 157, pp. 498-513, 2019, doi: 10.1016/j.watres.2019.03.030.
F. J. Mesas-Carrascosa, D. Verdú Santano, J. E. Meroño, M. Sánchez de la Orden, and A. García-Ferrer, "Open source hardware to monitor environmental parameters in precision agriculture," Biosystems Engineering, vol. 137, pp. 73-83, 2015, doi: 10.1016/j.biosystemseng.2015.07.005.
O. Elijah, T. A. Rahman, I. Orikumhi, C. Y. Leow, and M. N. Hindia, "An overview of Internet of Things (IoT) and data analytics in agriculture: Benefits and challenges," IEEE Internet of Things Journal, vol. 5, no. 5, pp. 3758-3773, 2018, doi: 10.1109/JIOT.2018.2844296.
D. Berckmans, "General introduction to precision livestock farming," Animal Frontiers, vol. 7, no. 1, pp. 6-11, 2017, doi: 10.2527/af.2017.0102.
J. E. Rakocy, M. P. Masser, and T. M. Losordo, "Recirculating aquaculture tank production systems: aquaponics—integrating fish and plant culture," SRAC publication, vol. 454, no. 1, pp. 1-16, 2006.
M. Ula, H A. Alkautsar, Muliani, “Optimization of Water Quality in Shrimp-Shallot Aquaponic Systems: A Machine Learning-Integrated IoT Approach”, International Journal of Intelligent Systems and Applications in Engineering Vol. 12 No. 1. 2023.
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