Development of a Wireless Sensor-Based Shrimp Pond Water Quality Monitoring System
Keywords:
Shrimp Aquaculture , Wireless Sensor Network , Water Quality Monitoring , Dissolved OxygenAbstract
Purpose: The study aims to design, develop, and validate a wireless sensor-based monitoring system for shrimp ponds, addressing the limitations of conventional manual monitoring in capturing dynamic fluctuations of critical water quality parameters.
Subjects and Methods: The system was equipped with low-power sensors to measure dissolved oxygen (DO), pH, temperature, and salinity, integrated with solar-assisted power, wireless transmission, and a cloud-based dashboard. Calibration and validation were first conducted under laboratory conditions, followed by an eight-week field deployment across three shrimp ponds. Accuracy was evaluated against standard reference instruments, while network performance and energy autonomy were continuously monitored.
Results: Laboratory calibration achieved high accuracy with RMSE values of 0.08 for pH, 0.18 mg/L for DO, 0.15 °C for temperature, and 0.42 ppt for salinity. Field trials generated more than 190,000 valid measurements, revealing consistent diurnal water quality dynamics. Sensor data showed strong concordance with manual reference measurements (concordance correlation coefficient > 0.95). The wireless communication network achieved a packet delivery ratio of 96.7% with median latency of 2.3 seconds. Solar-assisted nodes maintained uninterrupted operation for more than 30 days, ensuring system robustness in outdoor aquaculture conditions.
Conclusions: The developed wireless sensor-based monitoring system proved reliable for real-time aquaculture applications, offering both technical accuracy and practical benefits for farm management. The system enabled proactive interventions, such as timely aeration during pre-dawn oxygen depletion, thereby reducing shrimp stress and mortality risk. Future improvements will focus on enhancing sensor resistance to biofouling and integrating predictive analytics for decision support. Overall, the study demonstrates the feasibility of Internet of Things (IoT)-enabled solutions to advance sustainable and precision shrimp aquaculture.
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