Automatic monitoring of aquaculture water quality parameters

To propose a solution to apply Internet of Things (IoT) technology to automatically monitor aquaculture water quality parameters, help farmers actively monitor and detect many sources of adverse impacts on the pond environment, and at the same time assist management agencies to easily assess the environmental impact of aquaculture activities on the surrounding environment, MSc. Vuong Huy Hoang and a research team from the Institute of Information Technology conducted a project: "Research, application and implementation of a water quality monitoring system for shrimp farming for sustainable high-tech agricultural development in Ninh Thuan province based on the Internet of Things (IoT) and Cloud Computing", (subject code: UDNGDP.01/20-21). The topic was graded fairly by the Acceptance Council at the Vietnam Academy of Science and Technology.

Regular monitoring in aquaculture areas is very important to help farmers in the Mekong Delta provinces actively monitor and detect many sources of adverse impacts on the pond environment. From these monitoring results, the management agency can easily assess the environmental impact of aquaculture activities on the surrounding environment.

Figure 1. Super-intensive shrimp farming model in Ninh Thuan

Currently, most of the environmental monitoring centers of the provinces use the traditional method of quick measurements at the scene such as temperature, pH, DO, salinity and taking samples to the laboratory for analytical test. Therefore, the authors propose a solution to apply IoT technology to automatically monitor some aquaculture water quality parameters to monitor the farming area with a large area with a wireless sensor network, low server layer, software and internet to store, analyze and warn early about environmental fluctuations to reduce risk and improve efficiency.

Within the scope of the study, the authors selected 5 main parameters affecting the aquatic environment: temperature, salinity, pH, dissolved oxygen DO, and redox degree ORP.

The research team built a solution to apply IoT technology to automatically monitor aquaculture water quality parameters in concentrated farming areas such as hatcheries and industrial farms. The system has a flexible structure, many options to suit conditions and application capabilities.

Figure 2. IoT system for automatic monitoring of aquaculture water quality parameters

Subsystem of monitoring station for pond water environmental parameters is designed with floats floating on the water surface. In particular, the measuring system integrates sensors and analyzers to collect 5 main environmental indicators: temperature, pH, EC salinity, DO dissolved oxygen and ORP.


Figure 3. Block diagram of the subsystem for measuring aquatic environmental parameters in aquaculture

In which, each measuring station has the ability to self-configure and operate independently, or can operate according to the configuration set up remotely via TCP/IP protocol via wireless network. The automatic monitoring station is integrated with a module that supplies and stores electrical energy from solar cells to ensure continuous independent operation for a long time. At the same time, the monitoring station is installed on a specialized buoy system and has an appropriate mechanical structure, ensuring that the equipment standards work well in the outdoor environment in the coastal area.


Figure 4. Sensor system connection block diagram

The mechanical frame and float are designed to be balanced, withstand a large load (total weight on the float 55~60kg), use 304 stainless steel material to withstand the outdoor environment with salt vapor. With this design, the station will directly monitor the water environment indicators automatically, flexibly and continuously. The measuring station is capable of operating independently on water thanks to a solar battery power system.


Figure 5. Station for measuring environmental parameters of aquaculture water

The monitoring and data collection station is located on the ground to store and process data sent back from independent measuring stations on shrimp ponds via the LoRa network. This station uses two communication modules: Lora module and 3G/4G-LTE module. The Lora module of the central station receives the signal from the Lora module of the monitoring station. This signal is transmitted to the central control device for processing, then transmitting it to the 3G/4G-LTE module. The data is stored and displayed on the web-server interface. In addition, it is also possible to send SMS messages to the manager's phone number when there is a threshold alarm.

Figure 6. Data collection center subsystem

The processing and service provisioning subsystem is a software that collects and processes data on a monitoring database server, providing database querying services for real-time monitoring data representation services, data for alarm models and statistical reports. In addition, the server is responsible for providing services, system administration functions, user administration, data security, data processing analysis, and running of early warning models.

Figure 7. Software model for data collection and processing on the server

Of which, the storage and calculation of data is done quickly. Each session data including: temperature, pH, DO, ORP, EC sent to Web Server updated every 60 seconds. Access at:

Figure 8. Webbase interface of the data collection and processing software on the server

The IoT system for automatic monitoring of aquaculture water quality parameters is currently being tested in Ninh Thuan province with good results. At the same time, the system will serve the sustainable development of high-tech agriculture, improve the efficiency of aquaculture care by applying science and technology, bring socio-economic efficiency, and manage environmental management at shrimp farming facilities, and help management units capture the water environment at farming facilities to assess the facility's ability to operate effectively and the risk of environmental pollution.



Translated by Phuong Huyen
Link to Vietnamese version




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