Enhanced IoT Water Quality Monitoring System for Data-Based Aquaculture

Measurable water quality parameters (e.g., potential of hydrogen, electrical conductivity) are crucial factors in determining the health and viability of coastal fisheries. In particular, shellfish farms (e.g., oysters) are susceptible to changes in water quality. Farmers are often faced with the choice of relying on publicly-available data or investing in expensive commercial monitoring buoys. By gaining accurate real-time localized knowledge of process conditions, effective control methods can then be implemented to maintain optimal process conditions for improved performance and support data-driven decision making. This project focuses on the integration and testing of modern Internet-of-Things (IoT) technologies, open-source software and instrumentation, and automated data collection. The produced water quality measurement (WQM) device consists of a cost-effective PVC frame to support an Arduino-based data collection system. The data collection system monitors and collects seven water quality metrics (i.e., dissolved oxygen, electrical conductivity, oxidation reduction potential, potential of hydrogen, total dissolved solids, temperature, and turbidity). An LTE connection is used to relay the collected metrics and buoy location. The MQTT protocol is used to transmit the data, and a PC receives and translates the data into human-readable graphs and measures. An open-source user interface, developed in Python, allows the user to view time series plots of the data, see a go/no-go status regarding pre-defined process control limits, and view the location of the buoy on a map. This work focuses on the physical construction and testing of the device components and systems to support preparation for future field deployment.