# Design and Implementation of Water Quality Control and Monitoring Devices in a Small-Scale Aquaculture System

Jamali A Nagamora, Leo Rey S Sagun, Rosalie Vertudes, Jeffrey Ken B Balangao, Abdul Halil S Abdullah

<figure><img src="image_1.png" /></figure>

## ► To cite this version:

Jamali A Nagamora, Leo Rey S Sagun, Rosalie Vertudes, Jeffrey Ken B Balangao, Abdul Halil S Abdullah. Design and Implementation of Water Quality Control and Monitoring Devices in a Small-Scale Aquaculture System. International Journal of Biosciences, 2022, 21 (6), pp.91-105. 10.12692/ijb/21.6.91-105. [https://hal-03928988](https://hal-03928988)

HAL Id: hal-03928988

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Submitted on 22 Feb 2023

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<figure><img src="image_2.png" /></figure>
# Design and Implementation of Water Quality Control and Monitoring Devices in a Small-Scale Aquaculture System

Jamali A. Nagamora<sup>*1*</sup>, Leo Rey S. Sagun<sup>2</sup>, Rosalie Vertudes<sup>2</sup>,

Jeffrey Ken B. Balangao<sup>2</sup>, Abdul Halil S. Abdullah II<sup>2</sup>

<sup>*1*</sup>College of Engineering and Technology, Mindanao State University - Iligan Institute of Technology, Iligan City, Philippines

<sup>2</sup>College of Engineering and Architecture, University of Science and Technology of Southern Philippines, Cagayan de Oro City, Philippines

**Key words:** Aquaculture System, Sensors, Control and Monitoring, Water Quality Parameters

http://dx.doi.org/10.12692/ijb/21.6.91-105

Article published on December 04, 2022

## Abstract

To sustain an aquaculture system, one of the focus is on monitoring and controlling of water quality parameters. This study aimed at designing water quality control and monitoring system to be integrated and implemented in a developed small-scale aquaculture system. In this work, the necessary requirements needed for fish growth and development such as pH, salinity, ammonia and algae contents were being considered in developing different sensors for monitoring and control. The outcome of this work indicated the design and performance evaluation of the different sensors used to monitor and control the water parameters in the system. The implemented functionalities were able to monitor and maintain the pH, salinity, ammonia and algae contents in the aquaculture system. It can be recommended to also integrate in the system other water quality parameters such as oxidation-reduction potential, water hardness, nitrites and nitrates, among others.

<footnote>* Corresponding Author: Jamali A. Nagamora [✉] jamali.nagamora@g.msuiit.edu.ph</footnote>
## Introduction

Traditional farming such as in aquaculture basically has challenges when it comes to sustainability of operations. The manual and management operations must be there, as well as the maintenance of the water quality parameters (Hu et al., 2019; Eze and Ajmal, 2020; Vo et al., 2021; Rashid et al., 2021). These problems maybe lessen using the smart approach wherein smart devices (Sharma and Kumar, 2021) and technologies (Ullah and Kim, 2018; Imai et al., 2019; Kassem et al., 2021; Tsai et al., 2022; Nagamora et al., 2022) are integrated into the system to promote a sustainable, efficient and environment-friendly operation to attain good production. This is so because smart devices and technologies facilitate automated operations. They manage facilities and machinery therein and monitor water quality parameters (Kassem et al., 2021). Smart farming, thus, is offering more advantages than the conventional one and is revolutionizing farming in general.

In an aquaculture system, for an instance, sensors were being developed to monitor and maintain water quality parameters (Aziz et al., 2019; Balakrishnan et al., 2019; Tsai et al., 2022; Nagamora et al., 2022). Aziz et al. (2019) developed a monitoring system for continuously monitoring water quality. They came up with temperature sensor, light intensity sensor, pH sensor, GPS tracker and an inertial movement unit. Balakrishnan et al. (2019) designed an aquaculture system for controlling water parameters such as temperature, conductivity, turbidity and the nearness of oil layer over the water. Tsai et al. (2022) proposed an IoT-based smart aquaculture system for detecting the water quality of an aquafarm and providing automatic aeration such as temperature, pH value, dissolved oxygen and water hardness. Nagamora et al. (2022) integrated sensors in a small-scale aquaculture system for monitoring and control of light intensity, humidity, water temperature and dissolved oxygen. They recommended to include in the system sensors of other water quality parameters such as pH, oxidation-reduction potential and salinity, among others.

Water quality management is an essential part in aquaculture system. Water quality is a critical factor in the culture of any aquatic organisms for their growth and survival (Mustafa et al., 2016). Traditional practice of water quality management requires human intervention. It includes sampling of water from the pond and testing it using test papers or devices in a laboratory (Tsai et al., 2022). In order to minimize manual intervention, there is a likely a need to integrate smart technologies in the aquaculture system.

Hence, in this work, a continuation to the work of Nagamora et al. (2022) was conducted to integrate the control and monitoring of other water quality parameters in the developed small-scale aquaculture system. Control and monitoring system was designed and implemented to maintain the parameters such as pH, salinity, ammonia and algae contents. Specifically, the performance of the different sensors used for controlling and monitoring the water parameters was evaluated. Furthermore, an automatic fish feeder was also considered to facilitate control of the production of algae in the system.

## Methodology

### System block design overview

The system design includes determining what sensors to be used together with its corresponding circuitry, the proper micro-controller to be used in programming, and also the software to be used to program the sensors using the micro-controller.

### Control system design

The main purpose of the control system is to automate the control for the water quality of the system where it can adjust the water parameters like pH, salinity and ammonia content by using the liquid solution that doesn't need human intervention. Fig. 1 shows the control system block diagram.

The control system consist of three servos that interface with the Arduino uno micro-controller. The three servos are attached with liquid solution and the other one is attached with fish feeder to automate the
system servos that uses 5V control signals from the Arduino which has been included to automatically switch ON and OFF when the sensor reading is above or below the water parameter requirements.

## Monitoring system design

The main purpose of the monitoring system is to obtain the water quality parameter readings of the system displayed in the Liquid Crystal Display (LCD). By then, the data monitored is stored in the SD Card Module. The system can monitor the water parameters like pH, salinity, ammonia content and algae by using water parameter sensors. Fig. 2 shows the monitoring system block diagram.

The monitoring system consists of four sensors that also interface to arduino uno micro-controller. The sensors do have different water parameters to measure whose readings will be directly fed into the micro-controller so that they can be monitored and will be displayed to the liquid crystal display (LCD) and stored to the SD Card Module for review.

## Overall system block diagram

Fig. 3 presents the overall system block diagram of the water quality control and monitoring system in small scale aquaculture system. The sensors used to feed the water parameters data to the micro-controller.

The micro-controller displays the data in Liquid Crystal Display and store the data in SD card module. In the system, if the micro-controller detects undesirable parameters, the control system will run the servo for liquid solution. The monitoring and control communication system is the essence of the whole aquaculture system. And that the micro-controller unit serves as the brain of the whole system.

## Programming

Fig. 4 shows the general algorithm for the overall flow of programming the sensors. This is the main system algorithm serving as guide for programming the sensors. Without this, the sensors cannot function.

### Algorithm for pH sensor

Fig. 5 shows the algorithm of the pH sensor. The first step is to start the pH sensor. The next step is to measure the water in terms of pH. If the pH value is greater than 7, the servo will remain OFF and if the pH value is less than 7, the servo will turn ON.

### Algorithm for salinity sensor

Fig. 6 shows the algorithm of the salinity sensor. The first step is to start the salinity sensor. The next step is to measure the water in terms of salinity. If the salinity value is greater than 0, the servo will remain OFF and if the salinity value is less than 0, the servo will turn ON.

### Algorithm for ammonia sensor

Fig. 7 shows the algorithm of the ammonia sensor. The first step is to start the ammonia sensor. The next step is to measure the water in terms of ammonia value. If the ammonia value is greater than 1, the servo will turn ON and if the ammonia value is less than 1, the servo will remain OFF.

### Algorithm for algae sensor in the pond

Fig. 8 shows the algae sensor algorithm of the system. The algorithm seems only to measure the algae on the water and no involved controls. This is because the filtration system and food waste management system will help in controlling the algae production. These two systems would run 24 hrs. to help control the algae production.

### Algorithm for algae sensor in filtration tank

Fig. 9 shows the algae sensor algorithm of the filtration tank system. The sensor measures algae or turbidity in the filtration tank. This will help to monitor the filtration tank if it needs to clean the filter so it provides response to the user to maintain the clarity of the water in the filtration tank.

## Calibration

Calibration is a comparison between a known measurement (the standard) and the measurement using an instrument. Calibration is important to check the accuracy of the instrument. Fig. 10 below
shows the flowchart of calibrating the sensors. There is a comparison between the sensors and reference sensor by measuring the water parameters to measure accuracy. The percentage error is set as 1% because that is a percentage acceptable for the accuracy of the sensor. In this study, the calibration involved the sensors of pH, ammonia, salinity and algae.

### Performance evaluation

The system design must be tested and validated after it is developed. Testing is done by uploading the program on the actual micro-controller to see if the system runs. There is also assessment of the sensors (pH, salinity, ammonia and algae) to check if they read accurately and correctly. Troubleshooting is necessary for the consistency of the results being yielded. However, if the system works accordingly

and properly as desired during testing, there is no need to do troubleshooting. Troubleshooting only happens when there is something needed to be fixed for the system to work and function accordingly as desired.

## Results and discussion

### Overall schematic system design

Fig. 11 shows the overall monitoring and control system design of the small scale aquaculture system. In monitoring system, four sensors are attached to the Arduino micro-controller and for the control system, it attached 3 servos for liquid solution to control and maintain the water parameters of the pond. The data being fed into the micro-controller will immediately be displayed on the Liquid Crystal Display (LCD) and stored in the SD card module to collect data.

<table><caption>**Table 1.** Comparison of the Calibrated pH Sensor Reading and Reference Reading.</caption><thead><tr><td>TRIALS</td><td>SENSOR READING</td><td>REFERENCE READING</td><td>PERCENTAGE ERROR</td></tr></thead><tbody><tr><td>1</td><td>6.94</td><td>7.02</td><td>1.13960114</td></tr><tr><td>2</td><td>6.97</td><td>7.04</td><td>1.00430416</td></tr><tr><td>3</td><td>6.93</td><td>6.98</td><td>0.72150072</td></tr><tr><td>4</td><td>6.96</td><td>7.0</td><td>0.57471264</td></tr><tr><td>5</td><td>6.98</td><td>7.01</td><td>0.28612303</td></tr></tbody></table>

### Monitoring system design

As in Fig. 12, the monitoring system is composed of four sensors, one data logger module, LCD and Arduino uno R3. This will help determine and monitor the water parameters. The list of sensors are

the following: pH level sensor for the pH level of water, electrical conductivity sensor for salinity monitoring, turbidity sensor for algae monitoring, and MQ-137 ammonia sensor for ammonia monitoring.

<table><caption>**Table 2.** Comparison of the Calibrated Electric Conductivity Sensor Reading and Reference Reading.</caption><thead><tr><td>TRIALS</td><td>SENSOR READING (ppt)</td><td>REFERENCE READING (ppt)</td><td>PERCENTAGE ERROR</td></tr></thead><tbody><tr><td>1</td><td>33.42</td><td>33.76</td><td>1.017354877</td></tr><tr><td>2</td><td>34.79</td><td>35.11</td><td>0.919804541</td></tr><tr><td>3</td><td>35.02</td><td>35.24</td><td>0.628212451</td></tr><tr><td>4</td><td>34.85</td><td>35.02</td><td>0.487804878</td></tr><tr><td>5</td><td>35.04</td><td>35.12</td><td>0.228310502</td></tr></tbody></table>

The pH sensor is connected to analog input 0, EC sensor is to analog input 1, turbidity sensor is to analog input 2 and MQ-137 sensor is connected to analog input 3. Analog input 4 and 5 is for the LCD

and digital input/output 13,12,11 and 10 is for the data logger. All of the components are then connected to the 5V and GND pin of the micro-controller.
Control system design

As shown in Fig. 13, the control system is consist of 3 servos connected in the Arduino digital pin 9, 8, and 7 and their power source and ground is connected to the battery. On the one hand, Fig. 14 shows the servo mechanism of the system. When the sensor detects intolerable level of parameters, it will feed direct to micro-controller then it commands the servo to rotate and release the liquid solution.

Filtration tank system design

Fig. 15 shows the filtration system design. The upper part is the filter tank and the lower is the sump tank. The filter tank is composed of screen (to keep the stone stable), zeolite stones and foam filter. The sump tank after the water filtered above the liquid solution will facilitate a release if the water parameters are above or below in their tolerable ranges and then will go through the pond.

<table><caption>**Table 3.** Comparison of the Calibrated MQ-137 Ammonia Sensor Reading and Reference Reading.</caption><thead><tr><td>TRIALS</td><td>SENSOR READING (ppm)</td><td>REFERENCE READING (ppm)</td><td>PERCENTAGE ERROR</td></tr></thead><tbody><tr><td>1</td><td>4.01</td><td>4.08</td><td>1.74563591</td></tr><tr><td>2</td><td>4.25</td><td>4.31</td><td>1.39211136</td></tr><tr><td>3</td><td>4.28</td><td>4.32</td><td>0.93457943</td></tr><tr><td>4</td><td>4.35</td><td>4.37</td><td>0.45977011</td></tr><tr><td>5</td><td>4.40</td><td>4.41</td><td>0.22727272</td></tr></tbody></table>

<table><caption>**Table 4.** Comparison of the Calibrated Turbidity Sensor Reading and Reference Reading.</caption><thead><tr><td>WATER SAMPLES</td><td>SENSOR READING (NTU)</td><td>REFERENCE READING (NTU)</td><td>PERCENTAGE ERROR</td><td>CATEGORY</td><td>TOLERABILITY</td></tr></thead><tbody><tr><td>Sample 1</td><td>1090.85</td><td>1108.75</td><td>1.58878089</td><td>Clear</td><td>yes</td></tr><tr><td>Sample 2</td><td>1565.16</td><td>1584.68</td><td>1.24715684</td><td>Hazy</td><td>yes</td></tr><tr><td>Sample 3</td><td>2105.41</td><td>2123.74</td><td>0.86869790</td><td>Semi-Cloudy</td><td>no</td></tr><tr><td>Sample 4</td><td>2733.25</td><td>2749.42</td><td>0.59160340</td><td>Cloudy</td><td>no</td></tr><tr><td>Sample 5</td><td>2992.74</td><td>3004.15</td><td>0.38125597</td><td>Dark</td><td>no</td></tr></tbody></table>

Filtration tank system response

Fig. 16 shows the filtration tank system response where the turbidity sensor is connected to Arduino micro-controller and to the relay then to the buzzer. The system will be turned ON if the turbidity sensor detects undesired range of turbidity in the filtration system. This system will help facilitate awareness relative to the control of the turbidity level in the filtration tank. It is important to monitor and control

the filtration system because the parameters would be dependent on the circulated water.

Liquid solutions for pH, salinity and ammonia

The pond area must be determined to calculate the required solution to be poured in the pond. The pond has a rectangular shape with a height of 40cm, width of 100cm and a length of 200cm. The average depth of water is 15cm.

<table><caption>**Table 5.** Automatic Fish Feeder per feeding time calibration result.</caption><thead><tr><td>Trial</td><td>Iterations</td><td>Weight (g)</td><td>Tolerability</td></tr></thead><tbody><tr><td>1</td><td>2</td><td>7</td><td>NO</td></tr><tr><td>2</td><td>4</td><td>15</td><td>NO</td></tr><tr><td>3</td><td>6</td><td>20</td><td>NO</td></tr><tr><td>4</td><td>8</td><td>27</td><td>NO</td></tr><tr><td>5</td><td>10</td><td>33</td><td>NO</td></tr><tr><td>6</td><td>12</td><td>38</td><td>NO</td></tr><tr><td>7</td><td>14</td><td>44</td><td>NO</td></tr><tr><td>8</td><td>16</td><td>49</td><td>NO</td></tr><tr><td>9</td><td>18</td><td>54</td><td>YES</td></tr><tr><td>10</td><td>20</td><td>61</td><td>NO</td></tr></tbody></table>
The amount of water in the pond could reach up to 300 liters. The dimensions would form the basis for calculating the required drops of pH, salinity and ammonia contents to the pond.

Fig. 17 shows the installment of the pH, salinity and ammonia liquid solutions in bottles. Each bottle is

attached with the servo and filled with 50 ml of liquid solution. Fig. 18 shows liquid solution system installment in the sump tank. The servo is not synchronized. Only if the sensors would detect intolerable range that the servo will turn into 90° angle to release liquid buffer solution to adjust the water parameter ranges.

<figure><img src="image_3.png" /><figcaption>Fig. 1. Control System Block Diagram.</figcaption></figure>

<figure><img src="image_4.png" /><figcaption>Fig. 2. Monitoring System block diagram.</figcaption></figure>

### Algae control solution

Fig. 19 shows the automatic fish feeder mechanism which facilitates algae control solution. The bottle is filled with food and tapped to the servo at 0°. When the servo wants to feed the fish it twists the bottle 180°. Food would fall out but also clog the holes so a very limited amount actually is dispensed. The servo immediately rights itself back and fort then to at 0° and waits for the next feeding. The food and bottle will both determine the quantity being dispersed. It has 6 hrs.-interval in their feeding time. The amount dispensed is consistent because the food will come

out based on the living fish on the pond. If there is many fish, the program is adjusted to more iteration so servo will perform so that it can feed the exact amount pellets.

### Calibration

#### pH Sensor Calibration

In calibrating pH sensor, the pH sensor probe and pH digital meter were tested and compared. This is to know the accuracy of the pH sensor probe with Arduino uno micro-controller. The pH value ranges from 0 to 14.
<figure><img src="image_5.png" /><figcaption>Fig. 3. Overall System Block Diagram.</figcaption></figure>

The pH value of 7 and below has acidic value, 7 and up has alkalinity value and 7 is a fresh water value. The liquid sample used in the calibration test was a fresh water sample.

<figure><img src="image_6.png" /><figcaption>Fig. 4. General Algorithm.</figcaption></figure>

Table 1 presents the data gathered on a fresh water liquid sample using the two sensors. The percentage error values shows are lower which means the sensor is quite accurate and reliable. Therefore, it is good and suitable to be implemented in the system.

<figure><img src="image_7.png" /><figcaption>Fig. 5. Algorithm for pH sensor.</figcaption></figure>

### Electric conductivity sensor calibration

In calibrating the electric conductivity (EC) meter/sensor, the EC sensor probe and TDS digital meter were assessed and compared. This is to know the accuracy of the EC sensor probe with Arduino uno micro-controller. In salinity measurement, the value is being measured in ppt (parts per thousand). The liquid sample used in this calibration test was a salt water sample.
<figure><img src="image_8.png" /><figcaption>Fig. 6. Algorithm for Salinity Sensor.</figcaption></figure>

Table 2 presents the data gathered on a salt water liquid sample using the two sensors. The percentage error values show that they are lesser than 5% which means the sensor is quite accurate and reliable. Therefore, the sensor is good to be implemented in the system.

<figure><img src="image_9.png" /><figcaption>Fig. 7. Algorithm for Ammonia Sensor.</figcaption></figure>

### MQ-137 ammonia sensor calibration

In calibrating MQ-137 ammonia sensor, the MS-137 sensor probe and LaMotte smart 3 calorimeter were tested and compared. This is known to be the accuracy of the MQ-137 ammonia sensor probe with Arduino uno micro-controller.

In ammonia measurement, the value is measured in ppm (parts per million). The liquid sample used in this calibration test was an ammonium chloride sample.

<figure><img src="image_10.png" /><figcaption>Fig. 8. Algorithm for algae sensor in the pond.</figcaption></figure>

<figure><img src="image_11.png" /><figcaption>Fig. 9. Algorithm for algae sensor in filtration tank.</figcaption></figure>

Table 3 shows the data gathered on ammonium chloride liquid sample using the two sensors. The percentage error values show that they are less than 5% which means that the sensor is also quite accurate and reliable. Therefore, the sensor is good and suitable also to be implemented in the system.

<figure><img src="image_12.png" /><figcaption>Fig. 10. Flowchart for Calibration.</figcaption></figure>
Turbidity sensor calibration

In calibrating turbidity sensor, the turbidity sensor
probe and also the LaMotte smart 3 calorimeter were
assessed and compared. This is to know the accuracy
of the turbidity sensor probe with Arduino uno micro-
controller. In turbidity measurement, the value is
measured in NTU (Nephelometric Turbidity Units).
There were five samples of turbid water being used in
such case.

<figure><img src="image_13.png" /><figcaption>Fig. 11. Overall Schematic System Design.</figcaption></figure>

<figure><img src="image_14.png" /><figcaption>Fig. 12. Monitoring Device Schematic Design.</figcaption></figure>

Table 4 presents the data gathered on five different
liquid samples using the two sensors. It also includes
the category and tolerability of a water to the fish. The
percentage error values show very low results which
means that the sensor is also quite accurate and

reliable. And therefore, it is good also to be
implemented in the system.

<figure><img src="image_15.png" /><figcaption>Fig. 13. Control Device Schematic Design.</figcaption></figure>

<figure><img src="image_16.png" /><figcaption>Fig. 14. Servo Mechanism.</figcaption></figure>

The automatic fish feeder is one of the alternative
solutions for algae production control to help reduce
excess amount of food waste in the pond by feeding
the fish exact amount of food or pellets. The fish
feeder also needs to be calibrated to know the exact
amount that will be fed to the fish.

The amount of food for fish fingerlings to be fed is
usually 204 g in a day. If the feeding time of the fish is
four times a day, there should be 51 g per feeding
time.
<figure><img src="image_17.png" /><figcaption>Fig. 15. Filtration System Design.</figcaption></figure>

<figure><img src="image_18.png" /><figcaption>Fig. 16. Filtration Tank Turbidity Response.</figcaption></figure>

Table 5 presents the data gathered on calibrating the automatic fish feeder. A total of 10 trials were performed to estimate the exact iteration to obtain the desired weight of food to be released in the pond.

<figure><img src="image_19.png" /><figcaption>Fig. 17. Liquid Solution placements of pH, salinity and ammonia.</figcaption></figure>

The result obtained in the 9th trial was 54 g of fish food pellets which is the nearest to the desired amount of food being suggested. Therefore, in the automatic fish feeder, the device will perform 18 iterations to release 54 g of fish food four times a day to feed the fish.

<figure><img src="image_20.png" /><figcaption>Fig. 18. Liquid Solution system installed in the Sump Tank.</figcaption></figure>

Performance evaluation
pH Monitoring and Control

Fig. 20 shows the water pH readings during the start of the monitoring. In general, the pH level readings of the system fluctuated. This is because of the filtration tank that kept the water of the system refreshed. It had a moment that it went down significantly at 12NN.

<figure><img src="image_21.png" /><figcaption>Fig. 19. Automatic Fish Feeder Mechanism.</figcaption></figure>

In the 6th day of monitoring, the system monitored an undesirable range in which the pH level range dropped to 6.9. In such case, the control system will
turn ON to treat the water and the servo will turn 90°
angle and delay five seconds of time to release desired
pH liquid solution then pour it into the water. Fig. 21

shows that the pH level of the pond went up after the
desired pH liquid solution was applied into the
warter.

<figure><img src="image_22.png" /><figcaption>**Fig. 20.** Water pH readings of the System.</figcaption></figure>

<figure><img src="image_23.png" /><figcaption>**Fig. 21.** Controlled water pH readings of the System.</figcaption></figure>

Salinity Monitoring and Control

In the day of monitoring, Fig. 22 shows the data of salinity level of the system that went down for a time because of the filtration system that used to dissolve salts. During the 4th day of monitoring, the system monitored an undesirable range in which the salinity

level range dropped to -0.01 ppt. As such, the control
system will turn on to treat the water, the servo will
turn 90° angle and delay 12 seconds of time to release
desired liquid solution and pour it into the water. Fig.
23 shows that the salinity level of the pond went up
after the desired salinity liquid solution was applied.
<figure><img src="image_24.png" /><figcaption>Fig. 22. Salinity level of the System.</figcaption></figure>

<figure><img src="image_25.png" /><figcaption>Fig. 23. Controlled Salinity Level of the System.</figcaption></figure>

### *Ammonia monitoring and control*

During the day of monitoring, ammonia content level of the system rose up at some point because of some wastes of the fishes that were not being filtered and stocked up in the corner as shown in Fig. 24.

However, during the 5th day of monitoring, the system experienced an undesirable range in which the ammonia content range rose up to 1.02ppm. In such a case, the control system will turn ON to treat the

water, the servo will also turn 90° and delay 22 seconds of time to release the desired liquid solution and pour it into the water. Fig. 25 shows that the ammonia content of the pond dropped down after the desired ammonia liquid solution applied.

### *Turbidity Monitoring and response*

During the 1st day of turbidity monitoring in the pond, the turbidity level went up as shown in Fig. 26. This is because of the wastes of the fish and some food
wastes in the pond that caused cloudiness and haziness in the pond. Sooner or later the turbidity which implies presence of algae in the water will become an outbreak if not monitored. It is important

to really put a filtration tank in the system in closed recirculating water pond like aquaculture to minimize unwanted specimens that flow in the pond.

<figure><img src="image_26.png" /><figcaption>Fig. 24. Ammonia Content Level of the System.</figcaption></figure>

<figure><img src="image_27.png" /><figcaption>Fig. 25. Controlled Ammonia Level of the System.</figcaption></figure>

On the 8th day of monitoring, there's this observation in the filtration tank on the turbidity level that reached up to 2500 NTU.

This is an undesirable range for the turbidity level as programmed so the buzzer response will turn ON as

such. The tank in this case needs to be cleaned. On the 9th day, the turbidity level of the filtration tank is coming back to 1256.38 NTU as in Fig. 27.

This implies that the filtration tank had underwent cleaning already.
<figure><img src="image_28.png" /><figcaption>Fig. 26. Turbidity Level of the System.</figcaption></figure>

<figure><img src="image_29.png" /><figcaption>Fig. 27. Controlled Turbidity Level of the System.</figcaption></figure>

## Conclusion

In this study, the water quality control and monitoring system was developed to monitor the pH, salinity, ammonia and presence of algae in the small-scale aquaculture system. The functionality system was able to maintain proper pH, salinity, ammonia and presence of algae in the developed aquaculture system. On the one hand, the automatic fish feeder was also able to minimize the algae production by controlling the food that will be fed to the fish to avoid large amount of excess food wastes in the

system. It can be recommended to also integrate in the system other water quality parameters such as oxidation-reduction potential, water hardness, nitrites and nitrates, among others.

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