IoT-Enabled Real-Time Monitoring and Disease Prediction System for Sustainable Aquaculture
Year: 2026
Type: article
Abstract: The global shrimp aquaculture industry is highly vulnerable to water quality fluctuations, disease outbreaks and the inefficiencies of manual monitoring. To address these challenges, this paper presents a unified Artificial Intelligence (AI) and Internet of Things (IoT) enabled framework that integrates real-time sensing, predictive analytics and automated pH sensor calibration for precision aquaculture management. A custom-designed aqua buoy system continuously measures the critical parameters including pH, Dissolved Oxygen (DO) and Oxidation Reduction Potential (ORP) using a distributed IoT sensor network with cloud connectivity. A responsive web dashboard and mobile application deliver real-time visualization, farmer advisories and automated alert notifications, facilitating timely interventions. Machine learning models are employed in the proposed model for disease classification and intelligent decision-making: Random Forest achieves 98.35% accuracy in disease prediction, while Random Forest regression provides highly reliable pH voltage prediction by minimizing manual calibration requirements. Furthermore, Long Short-Term Memory (LSTM) networks are utilized for time-series forecasting, achieving up to 94% accuracy in predicting pH, DO and ORP trends, thereby enabling early corrective actions. The proposed method enhances monitoring efficiency, reduces shrimp mortality risk and supports environmentally sustainable farming practices. This integrated approach demonstrates the potential of combining IoT, disease classification & prediction and automated calibration techniques to transform aquaculture into a data-driven, precision-managed and resource-efficient industry.
Source: IEEE Access
Language: English
Subfield: Water Science and Technology
Field: Environmental Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,075
OpenAlex ID: W7131625145