A Comprehensive Review on : Aquaponic Farming Water Quality Prediction
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https://doi.org/10.32628/CSEIT2410420Keywords:
Aquaponic Farming, Water Quality Prediction, IoT Monitoring, Automated Systems, Hydroponics-Aquaculture Integration, Imbalanced Data, Multi-Model Categorization, Machine Learning, Real-Time Analysis, Sensor Networks, pH and DO Monitoring, Ammonia Control, Data Cleaning, Predictive ModellingAbstract
Aquaponic farming, which combines aquaculture and hydroponics, depends strongly on maintaining optimal water quality to guarantee the health and productivity of both fish and plants. This review paper explores the latest developments in IoT-based automated water monitoring systems, focusing on their role in predicting and managing water quality in aquaponic systems. Regardless of significant progress there are several research gaps. Recent studies highlight challenges such as inconsistent sensor selection, calibration issues, insufficient publicly available data, and inadequate data cleaning and preprocessing. Also, the issues of imbalanced datasets, limited long-term data, and underdeveloped IoT and AI integration prevent the development of accurate predictive models. The scalability and maintenance of systems, understanding microbial dynamics, and nutrient management are also critical areas needing further exploration. This review also identifies the need for deeper case studies and advanced feature extraction methods to enhance prediction accuracy. By addressing these gaps, including system scalability and nutrient management, future research can improve data availability and quality, enabling more robust predictions and contributing to more efficient and sustainable aquaponic systems.
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