Optimization of Water Quality in Shrimp-Shallot Aquaponic Systems: A Machine Learning-Integrated IoT Approach - Ula, Munirul; Muliani, Muliani; Aidilof, Hafizh Al Kautsar (International Journal of Intelligent Systems and Applications in Engineering, 2023)
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.