Machine Learning-Based Dissolved Oxygen Classification Using Low-Cost IoT Sensors for Smart Aquaponic

Dissolved oxygen (DO) plays a vital role in maintaining balanced aquaponic ecosystems, yet conventional optical and galvanic DO sensors remain costly and impractical for low-budget deployments. However, most existing dissolved oxygen monitoring studies rely on costly sensing infrastructures, regression-oriented prediction approaches, or centralized processing schemes, which limit their applicability in small-scale and resource-constrained aquaculture settings. Furthermore, many previous works focus primarily on numerical prediction accuracy without explicitly addressing data imbalance issues or providing actionable classification outputs that can directly support real-time operational decisions at the pond level. This study proposes a machine learning–based approach for estimating DO levels using low-cost pH, temperature, and nitrogen sensors integrated with an IoT data acquisition system. A dataset comprising approximately 1,048,536 records was processed using feature engineering and class balancing techniques, followed by training an XGBoost classifier optimized through grid search. The model classified DO into three categories—Low (<5 mg/L), Medium (5–7 mg/L), and Good (>7 mg/L)—achieving 96.6% accuracy, outperforming baseline regression models including Linear Regression, Random Forest, and XGBoost Regressor. Feature importance analysis revealed temperature and the pH–temperature interaction as dominant predictors. The model was successfully deployed on a Raspberry Pi for real-time monitoring, offering a scalable and cost-effective alternative to high-end probes. The proposed framework demonstrates practical potential for smart aquaponic systems, enabling affordable, automated, and data-driven oxygen management.

@article{Supria2025,
  title     = {Machine Learning-Based Dissolved Oxygen Classification Using Low-Cost IoT Sensors for Smart Aquaponic},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {16},
  number    = {12},
  year      = {2025},
  publisher = {The Science and Information Organization},
  author    = {Supria and Afis Julianto and Wahyat and Marzuarman and M Nur Faizi and Hardiyanto},
  doi       = {10.14569/IJACSA.2025.0161254},
  url       = {https://doi.org/10.14569/IJACSA.2025.0161254}
}