
==== Front
MethodsX
MethodsX
MethodsX
2215-0161
Elsevier

S2215-0161(24)00358-3
10.1016/j.mex.2024.102906
102906
Computer Science
IoT-enabled effective real-time water quality monitoring method for aquaculture
Shete Rupali P. a
Bongale Anupkumar M. ambongale@gmail.com
b⁎
Dharrao Deepak c
a Symbiosis Institute of Technology, Symbiosis International (Deemed University), Pune Campus, Lavale, Pune, Maharashtra, India
b Department of Artificial Intelligence and Machine Learning, Symbiosis Institute of Technology, Symbiosis International (Deemed University), Pune Campus, Lavale, Pune, Maharashtra, India
c Department of Computer Science and Engineering, Symbiosis Institute of Technology, Symbiosis International (Deemed University), Pune Campus, Lavale, Pune, Maharashtra, India
⁎ Corresponding author. ambongale@gmail.com
13 8 2024
12 2024
13 8 2024
13 1029068 7 2024
7 8 2024
12 8 2024
© 2024 The Authors
2024
https://creativecommons.org/licenses/by-nc-nd/4.0/ This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
Aquaculture is growing industry from the perspective of sustainable food fulfillment and county's economic development. Technology oriented aquafarming is the solution for effective water quality monitoring and high yield production. Internet of Things (IoT) integrated aquaculture can cater to such requirements. This research article introduces a comprehensive method aimed at seamlessly incorporate IoT sensors into aquafarming environments, utilizing Arduino boards and communication modules. The proposed method measures accurate water quality parameters, such as temperature, pH levels, and Dissolved Oxygen (DO), which are essential for maintaining optimal conditions for suitable aquaculture environment. This method enables the real-time collection of critical data points that are essential prevent fish diseases and mortality with low human intervention and maintenance cost. The key contributions of the methodology are mentioned below.• Design and development of a compact and efficient Printed Circuit Board (PCB) to achieve accurate sensor data readings and reliable communication in an aqua environment.

• Prevent fish disease and mortality rate through data-driven decision incorporating correlation of DO, pH, and temperature sensor data.

• Conducted instrument calibration checks and cross-validated automated system data with manual observations through repeatability tests to ensure precise measurements of sensor parameters.

Graphical abstract

Image, graphical abstract

Keywords

Aquaculture
IoT
Water quality monitoring
Fish health
Dissolved oxygen sensor
Method name

IoT-Based Water Quality Monitoring and Data Analysis in Aquaculture
==== Body
pmcSpecifications tableSubject area	Computer Sciences	
More specific subject area	IoT Analytics	
Name of your method	IoT-Based Water Quality Monitoring and Data Analysis in Aquaculture	
Name and reference of original method	NA	
Resource availability	The authors can make the data available upon a reasonable request.	

Background

Aquaculture, also known as fish farming, is the cultivation of aquatic organisms in a controlled habitat. The aquaculture industry in India, a country with a long history of fisheries and aquaculture, is essential to the economy and food security. However, it faces significant challenges, including frequent natural disasters like monsoons and cyclones, which can cause significant damage. Furthermore, there is shortage of skilled labour for regular water quality monitoring and maintenance. Optimal water quality in Aquaculture Systems (AS) is essential for aquatic species' growth and survival. Traditionally water quality parameters such as temperature, pH value, and Dissolved Oxygen (DO) have been monitored using labor-intensive manual measurements and periodic laboratory analysis. Furthermore, it raises concerns about regular maintenance of water quality within acceptable limits [1].

Substantial benefits to fish farmers can be imparted by Internet of Things (IoT) enabled system to overcome limitations of sensor calibration and data integration [1]. Growth of fish and its metabolism depend significantly on water temperature. The variation in temperature has an impact on oxygen solubility and metabolic rates in aquatic organisms [2]. Deterioration of water quality due to untreated sewage and other pollutants is a major environmental issue in India. This has been addressed by Keshipeddi proposing an IoT-based solution which uses the Arduino-uno to continuously monitor water parameters like pH and turbidity [3]. Citing high demand for freshwater, Srivastava discussed the need for smart water quality monitoring in India [4]. Maintaining a stable pH is essential for preventing stress and disease in fish [5]. Ajith and Manimegalai demonstrated an IoT-based water quality monitoring system that uses cloud computing and deep learning to provide real-time data [6]. Billah et al. described the creation of real-time water quality monitoring system for fish farms that was specifically designed for catfish habitats [7]. Loh et al. present IoT-based monitoring system for fish farms that measures temperature, pH, dissolved oxygen, and total dissolved solids [8]. Few researchers have processed, acquired, and collected data using sensors, smart phone camera, modules, Arduino, Raspberry Pi, and other components [[9], [10], [11], [12], [13]]. Another study found that IoT can be used to track and store data via cloud storage interfaces [14]. The real-time fishpond monitoring system interfaces cloud storage with an Intel® Edison microcontroller and introduces AWS technology [15,16]. Researchers looked into an IoT based smart fishery management system that boosts fish production [17,18]. Smart aquaculture system that monitors and controls aquaculture environments in real time using AI, cloud and IoT are designed [19,20].

Although a significant amount of research has been, particularly in India, there is a noticeable gap in similar research based on the on-site environmental conditions and requirements. Traditional aquafarming methods lack systematic and automated data collection and decision-making technology for improved fish yield with low mortality rate. To address these problems, this research proposes an effective method with design of IoT floating buoy unit for collecting and analysis of real-time water parameter monitoring method for Tilapia fish harvesting as per the onsite farmer requirements at Pune, Maharashtra, India.

Method details

A floating buoy with an IoT unit that can be submerged in water and continuously monitor DO, pH, and temperature has been developed. This system collects sensor data at predetermined intervals, allowing for detailed analysis and prediction of water quality parameters via machine learning models. The proposed method is currently being deployed for testing at a remote aquafarm pond of dimension 120 × 70 feet at Pune, Maharashtra, India. The pond comprises several fish farming cages of measuring 8 feet wide and 4.5 feet depth water level, where Tilapia fishes are farmed. Complete pond is covered by bird net. As the designed hardware unit needs to be submerged in the water, a floating buoy is attached with the custom unit. Before immersing the floating unit into pond, a power switch knob is activated, and data communication is checked on cloud interface. The water parameters data are collected from the sensors at fifteen minutes regular intervals. The onsite real time collected data is transmitted through communication module. The data is processed for correlation analysis DO, pH and temperature to identify anomalies and ensuring the healthy water environment.

IoT system design and architecture

The incorporation of an IoT-based platform into aquaculture infrastructure optimizes data communication pathways among microcontrollers, diverse sensors, and servers. The designed methodology architecture is depicted in Fig. 1.Fig. 1 Integrated system architecture.

Fig 1

Within our system, the acquisition of precise measurements for multiple water parameters is achieved through the deployment of high-precision, lab-grade sensors. The data generated by the DO, pH and temperature sensor is subsequently uploaded to the server platform via the SIM 800 L module. The Arduino controller oversees the monitoring of the implemented sensors and executes the storage operations on the cloud platform. User View gives aqua culturists a simple dashboard with real-time and historical data, alerts, and control options, helping them maintain optimal water conditions for aquatic life.

All these operations are systematically executed step by step and are depicted herein as a flow diagram presented below in Fig. 2.Fig. 2 Flow diagram of the IoT based water quality monitoring system.

Fig 2

Hardware set-up

The design and architecture of the proposed IoT-based water quality monitoring system is described in this section. The primary objective is to provide real-time, accurate monitoring of vital water quality parameters in aquaculture environments. The proposed system utilizes DO, pH, and temperature sensors, as shown in Fig. 3. Sensors are coordinated by a microcontroller to provide comprehensive and real-time data on aqua-farm water quality parameters, enabling proactive management and maintenance of aquaculture environments.Fig. 3 Different sensors used for IoT based monitoring system.

Fig 3

The method uses an Arduino Uno microcontroller board with expansion shields and digital and analog I/O pins. A pH sensor circuit with integrated probe measures precise pH values and sensors are calibrated for accurate readings. The calibration process follows standards, with low, mid, and high points at pH 4.01, pH 7.00, and pH 10.01 respectively. To take readings, the pH probe with Extended Range (EXR) sensing glass is submerged in the aqua cage. Fig. 3 shows pH probe design. DO sensor maintaining aquatic organisms' health requires oxygen. DO sensors measure water dissolved oxygen. A galvanic DO probe with Polytetrafluoroethylene (PTFE) membrane is used here. Regularly, oxygen molecules diffuse through the probe membrane and are reduced at the cathode, generating a mild voltage. The probe's mV output increases with increasing oxygen levels. Test samples are used for internal calibration prior to on-site deployment. The details of the specifications of used DO sensor are mentioned in Table 1.Table 1 Details of the specifications of do sensor.

Table 1Particulars	Information	
Range	0.01 - 100+ mg/L	
Accuracy	+/- 0.05 mg/L	
Response Time	1 reading per second	
Data format	ASCII	

A lab-grade PT-1000 sensor is used to keep records on the aqua pond's temperature. With the use of a resistance-type thermometer, this sensor probe can measure temperatures between −200 °C and 850 °C. By incorporating the GSM module into aquaculture monitoring systems, aqua culturists can remotely monitor water parameters and get real-time updates on the state of the water quality. Cellular GSM/GPRS services are provided by the SIM800L GSM module, which connects users to data services over a cellular network. It can also be used to enable test-bed remote data transfer to cloud computing systems. Additionally, this module is compatible with a 5 V power supply, supports QUAD-BAND GSM/GPRS operation, and is designed to integrate seamlessly with Arduino platforms.

Printed circuit board (PCB) design layout

PCB diagram of IoT based device as shown in Fig. 4 includes sensor integration traces and connectors, as well as power regulation circuits and communication module interfaces. The pinned connections incorporate ATMEGA328 controller, which is interfaced with other components such as MOSFET, SIM unit etc. The components incorporated into the PCB design, ensures seamless connectivity and effective power management.Fig. 4 PCB circuit diagram of IoT based device.

Fig 4

As per the design, the IoT hardware is developed with DO, pH, and temperature sensors, GSM module embedded on PCB with waterproof enclosure is shown in Fig. 5. Additionally, it is equipped with a battery backup capable of sustaining operations for a minimum of 48 h. The entire unit is housed within an IP66 waterproof enclosure, providing protection against dust ingress, and enabling resistance to low-pressure water jets from any direction, as per pond environment specifications. Sensor probes responsible for capturing DO, pH, and water temperature readings are positioned underside for optimal data collection. Finally, the unit is enclosed within an additional layer of plastic casing protection is deployed for data collection and validation.Fig. 5 IoT Hardware Setup Showing DO, pH, and Temperature Sensors, GSM Module embedded on PCB with Waterproof Enclosure.

Fig 5

Method validation

The study successfully compiled, displayed, and analyzed the data on water quality that was gathered, offering insightful information for proactive aquaculture management.

Data collection

The data collection process from the IoT-based water quality monitoring system involves continuous real-time acquisition of key water parameters, including DO, pH levels, and temperature. An Arduino microcontroller interfaces with each sensor, processes the data, and then sends it to a cloud server through a wireless communication module. Through the GSM communication module, data is sent to a cloud server in real time. The server handles the data and stores it so that it can be accessed from afar. Cross-validation is also done by comparing the system's recorded values to readings made by farmers manually. This information could be accessed through a mobile app, which let fish farmers check the quality of the water. Data collected from the system includes time stamp, sensor readings and related metadata. Sample data collected from the sensors as shown in Table 2, represents the temperature sensor, the pH sensor, and the DO sensor data values.Table 2 Sample subset of collected dataset.

Table 2Dissolved Oxygen (mg/L)	pH	Temperature ( °C)	
8.40	8.50	27.67	
8.50	8.40	27.56	
8.50	8.40	27.45	
8.40	8.40	27.41	
8.40	8.80	27.29	
8.50	8.70	27.18	
8.60	8.70	27.12	
8.40	8.70	27.08	
8.50	8.90	27.40	
8.40	9.00	27.20	
8.40	9.10	27.40	
8.30	9.00	27.80	
8.30	9.20	27.80	
8.25	8.90	29.00	
8.25	8.90	28.94	
8.25	8.80	28.58	
8.25	8.80	28.55	
8.25	8.70	28.70	
8.25	8.70	28.79	
8.25	8.90	28.80	
8.25	8.80	28.27	
8.25	9.00	28.28	
8.25	8.90	28.13	
8.25	8.90	28.19	
8.20	8.79	28.39	
8.00	9.02	29.08	
8.00	8.99	29.64	
7.80	9.25	30.21	
7.80	9.17	30.87	

Statistical analysis of sensor data

The parametric distribution of DO, pH, and temperature, as represented by their probability densities is depicted in Fig. 6. The extreme DO values of collected data ranges from 6.5 mg/L to 9.1 mg/L, but most of the observed DO data points typically are in the range between 7 and 8.7 mg/L. This indicates that the majority of DO readings are clustered around this central value, with fewer instances of extremely high or low oxygen range. The relatively small standard deviation indicates that oxygen levels remained stable during the monitoring period, which is critical for aquatic organisms' respiration and overall health.Fig. 6 Parametric distribution of DO, pH, and temperature.

Fig 6

The pH values are predominantly concentrated between 8 and 9. The close clustering of pH values around the mean indicates that the water's acidity or alkalinity levels were well-regulated, reducing the possibility of harmful fluctuations that could stress aquatic organisms. In terms of temperature, the most common range is 25 to 30 °Celsius. This indicates that the water temperature remained relatively stable, which is important for the fish's metabolic rates and overall health. Sudden temperature changes can cause stress and harm fish health, but the observed stability indicates that the environmental control measures in place were effective.

Correlation analysis

The correlation matrix heatmap visualizes the relationships between various water quality parameters, as shown in Fig. 7. This visualization depicts the interrelationships between the variables being studied. Colour intensity represents the strength of correlation, with darker shades indicating stronger associations. The colour spectrum, which runs from blue to red, indicates the direction of correlation, with red indicating positive and blue indicating negative correlations.Fig. 7 Heatmap showing correlation DO, pH and temperature.

Fig 7

The correlation coefficient of −0.35 between DO and pH conveys a moderately negative correlation. This information proposes that as water temperature increases, so does the DO concentration. Further it can be related to the increased metabolic activity of aquatic organisms at higher temperatures. This can result in increased oxygen consumption and subsequent aeration processes to restore DO levels. A strong negative correlation between DO and temperature can be observed with value −0.69 and a moderate to strong positive correlation can be seen among pH and temperature with 0.77 value. The perfect positive relationship between each variable and itself is visible by observing the diagonal of the correlation matrix, with a value of '1′ for each variable. To summarize, the correlation matrix heatmap reveals relationship between DO, pH, and temperature and helps to analyse how one parameter may affect the other parameter enriching our understanding of these parameters within the context of the research project.

The correlations and distributions of water quality parameters has been visualized using the pair plot. The graphical visualization assists to analyse the aqua environment and make well informed judgment for the sustainability of fish farming operations. Fig. 8 shows pairwise relationships among different variables. pH and DO scatter plot display a broad distribution of data points with the presence of noticeable clusters. At high pH value, DO seems to stabilize in the middle range. Further on the scatterplot a negative correlation can be seen among temperature and DO. As the temperature increases DO levels tend to drop in proportion. Usually, warmer water contains less DO in aquatic ecosystems, which can have significant consequences. One more pair plot relationship of pH and temperature shows a positive linear trend on the scatterplot. This reveals that rising temperatures usually coexist with an increase in pH.Fig. 8 Pair plot for dataframe.

Fig 8

To guarantee the precision and dependability of the data that was gathered with a maximum error of 4.87 %, validation procedures were carried out through repeatability tests and calibration checks. To confirm the sensor readings consistency, repeatability tests conducted on the same water under the similar circumstances. To ensure the sensor value correctness, calibration checks were carried out by comparing the measured values with industry standard reference values. The outcome of the test manifests the accuracy and dependability of the system thus by ensuring that the sensor readings were within allowable bounds.

Limitations

In aquafarming IoT applications, ensuring data security and privacy is the most essential aspect to protect sensitive information like water quality metrics. In most of the cases aquacultures refuse to share the data for public, or at least proper assurance is required from the researchers stating the data shall use preserving necessary privacy measures. One of the limitations and future scope of the presented work can be to extend the data collection module with appropriate privacy preservation techniques. This ensures restrictions of unauthorized access, data breaches, and tampering. Though the experiments include lab grade sensors, there is a need for protection of the sensors and enclosure from accidental damage. The designed prototype fulfils all the necessary requirements of the aqua farmers, but on large scale production of the system should ensure regulatory compliance standards to meet legal requirements and industry guidelines.

Conclusion

IoT based aquaculture water monitoring system is designed and developed on a compact and efficient PCB to achieve accurate sensor data readings and reliable communication in an aqua environment. The module is developed by considering the on-field requirements of fish farmers. The proposed methodology with sensor-based hardware setup assist in efficient aqua farming by preventing fish mortality, maintaining healthy water parameters, reducing fish diseases, and thereby supporting improved yield production and food sustainability. The performance of the system is tested by deploying it on a site location in Pune, Maharashtra. The setup addresses shortfalls of the existing IoT setup in terms of reliance on self-reporting and tailor-made monitoring method for Tilapia fish harvesting as per the onsite farmer requirement. The system proved to be reliable in terms of battery usage, with a minimum backup of up to 48 h and uninterrupted data transmission service at every fifteen minutes periodic interval. The module is mounted on a floating buoy, and it is safeguarded from water and dust within an enclosure. A thorough analysis of the data, which included pair plots, distribution plots, and correlation matrix heatmaps, showed how well the system kept fish health conditions steady and ideal. DO values in this distribution typically range between 7 and 8.5 mg/L. This indicates that the majority of DO readings are clustered around this central value, with fewer instances of extremely high or low oxygen levels. pH and temperature have a correlation coefficient of approximately 0.77, indicating a moderate to strong positive correlation between these variables. DO levels tend to drop in proportion to temperature increases. Warmer water in aquatic ecosystems usually contains less DO, which can have important consequences. The monitoring system's accuracy and dependability are confirmed by the positive correlations and regular data distributions.

Ethics statements

Ethical clearance is not applicable for the proposed work.

Credit author statement

Rupali P. Shete: Writing – original draft, Writing - review & editing, Data collection and Validation; Anupkumar M Bongale: Writing – original draft, Writing - review & editing, Investigation, Supervision; Deepak Dharrao: Conception and design, Data collection and Analysis, Drafting and revising of the paper.

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Data availability

Data will be made available on request.

Funding

There is no funding for this research work.
==== Refs
References

1 Islam S.I. Ahammad F. Mohammed H. Cutting-edge technologies for detecting and controlling fish diseases: current status, outlook, and challenges J. World Aquac. Soc. 2024
2 S. Kumar, R. Samkaria, R. Singh, N. Karnatak, A. Gehlot, S. Choudhary, IoT and WPAN based approach for Water Quality Monitoring of Fish Farm, 7, (2018) 16–23. 10.37591/JOITI.V7I3.273.
3 Keshipeddi S. IoT based smart water quality monitoring system SSRN Electronic J. 2021 10.2139/ssrn.3904842
4 Srivastava S. Study of IoT based smart water quality monitoring system Int. J. Res. Appl. Sci. Eng. Technol. 2021 10.22214/ijraset.2021.37483
5 Medina J. Arias A. Triana J. Giraldo L. Segura-Quijano F. Gonzalez-Mancera A. Zambrano A. Quimbayo J. Castillo E. Open-source low-cost design of a buoy for remote water quality monitoring in fish farming PLoS. One 17 2022 10.1371/journal.pone.0270202
6 B. Ajith Jerom, R. Manimegalai, An IoT based smart water quality monitoring system using cloud. 2020 International Conference on Emerging Trends in Information Technology and Engineering (ic-ETITE), (2020) 1–7. 10.1109/ic-ETITE47903.2020.450.
7 M. Billah, Z. Yusof, K. Kadir, A. Ali, I. Ahmad, Quality maintenance of fish farm: development of real-time water quality monitoring system. 2019 IEEE International Conference on Smart Instrumentation, Measurement and Application (ICSIMA), (2019) 1–4. 10.1109/ICSIMA47653.2019.9057294.
8 S. Loh, P. Teh, G. Goay, J. Sim, Development of an IoT-based Fish Farm Monitoring System. 2023 IEEE 13th International Conference on Control System, Computing and Engineering (ICCSCE), (2023) 281–286. 10.1109/ICCSCE58721.2023.10237168.
9 Raju K.R.S.R. Varma G.H.K. Knowledge based real time monitoring system for aquaculture using IoT 2017 IEEE 7th international advance computing conference (IACC) 2017 IEEE 318 321
10 Manoj M. Dhilip Kumar V. Arif M. Bulai E.R. Bulai P. Geman O. State of the art techniques for water quality monitoring systems for fishponds using iot and underwater sensors: a review Sensors 22 6 2022 2088 35336256
11 Haque E. Al Noman A. Ahmed F. Image processing based water quality monitoring system for biofloc fish farming 2021 Emerging Technology in Computing, Communication and Electronics (ETCCE) 2021 IEEE 1 6
12 B.E. Agossou, T. Toshiro, IoT & AI based system for fish farming: case study of Benin. In Proceedings of the Conference on Information Technology for Social Good (2021, September) (pp. 259–264).
13 Uddin M.A. Dey U.K. Tonima S.A. Tusher T.I. An iot-based cloud solution for intelligent integrated rice-fish farming using wireless sensor networks and sensing meteorological parameters 2022 IEEE 12th Annual Computing and Communication Workshop and Conference (CCWC) 2022 IEEE 0568 0573
14 Acar U. Kane F. Vlacheas P. Foteinos V. Demestichas P. Yücetürk G. Drigkopoulou I. 387 Vargün, A. Designing an IoT cloud solution for aquaculture Proceedings of the 2019 global 388 IoT summit (GIoTS) 2019 IEEE 1 6
15 H. Hashim, O. Yusoff, P. Saad, Z. Harun, Development of real-time monitoring system for aquaculture farming using IoT. 2023 IEEE International Conference on Applied Electronics and Engineering (ICAEE), (2023) 1–4. 10.1109/ICAEE58583.2023.10331205.
16 R. Kodali, A. Sabu, Aqua Monitoring System using AWS. 2022 International Conference on Computer Communication and Informatics (ICCCI), (2022) 1–5. 10.1109/ICCCI54379.2022.9740798.
17 Janet J. Balakrishnan S. Rani S.S. IOT based fishery management system Int. J. Oceans Oceanogr. 13 1 2019 147 152
18 Kiruthika S.U. Raja S.K.S. Jaichandran R. IOT based automation of fish farming J. Adv. Res. Dynam. Control Syst. 9 1 2017
19 B. Wu, C. Wang, K. Du, A Smart Aquaculture System Exploiting IoT, AI and Cloud Computing. 2023 IEEE International Conference on Smart Internet of Things (SmartIoT), (2023) 251–256. 10.1109/SmartIoT58732.2023.00045.
20 M. Palconit, R. Concepcion, R. Tobias, J. Alejandrino, V. Almero, A. Bandala, R. Vicerra, E. Sybingco, E. Dadios, Development of IoT-based Fish Tank Monitoring System. 2021 IEEE 13th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM), (2021) 1–6. 10.1109/HNICEM54116.2021.9731950.
