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This paper presents a study conducted to provide an innovative, resource-effective and urban-suitable solution to present agricultural challenges in the Philippines through the development of an Arduino-based smart aquaponics system made self-sustainable by wireless Android Internet of Things (IoT) monitoring and automatic correction, automatic feeding system and solar power technology. Three aquaponics setups namely the Ion-Sensitive Field Effect Transistor (ISFET) pH sensor-monitored setup, glass-electrode pH sensor-monitored setup, and uncontrolled setup were constructed to compare the growth of the plants and fish in terms of length and weight. The system involves the detection of two essential parameters, namely the pH level and temperature using respective sensors integrated to the Arduino YUN microcontroller. Abnormalities in the said parameters were automatically corrected through the peristaltic buffer device and aerator, sending a notification message via electronic mail. The aquaponics system was capable of effective wireless transmission of measured data and monitoring of water parameters in an Android unit installed with an application through the connectivity of Wireless Fidelity. Data gathering, specifically the measuring and recording of length and weight of vegetables (Wild Chili, Cherry Tomato, Eggplant, and Bok Choy) and fish (Black Nile Tilapia) were done every two weeks. Graphical results showed that the ISFET-monitored setup successfully monitored and maintained the optimal water quality of the aquaponics setup better than the other two setups.

Index Terms—Aquaponics; Arduino programming; Internet of Things; Ion Sensitive Field Effect Transistor

I. INTRODUCTION

In answer to the present, worsening effects of soil degradation, water insufficiency and climate-concerning challenges afflicting agricultural yield in the Philippines, aquaponics is regarded as the integration of two relatively well-established food production technologies: aquaculture, a condition-controlled cultivation of water populations; and hydroponics, a technique of plant production using less or no soil at all. In a recirculating aquaponics system, the liquid effluent rich in nutrients derived from manure and nitrogenous waste excreted from fish fertilizes hydroponic beds, providing essential elements for plant growth [1]. In turn, the fish water is filtered, providing a clean and safe environment for the growth of the population. Aside from food security, aquaponics is also a great means for business which guarantees high yields of fresh, purely organic, nutritious crops.

The two most critical water parameters to be monitored and corrected in an aquaponics system are the pH level and temperature [2, 3]. The common glass-electrode pH sensors are not advisable to be used because of its fragility to break when immersed in water for a long period of time and its continuously-changing temperature levels. On the other hand, the Ion-Sensitive Field Effect Transistor (ISFET) proved promising motives as to why it is better than the glass pH electrode including faster response and low output impedance, aside from its tenacity and temperature-stability [4]. In addition, with the use of solar panels as an alternative source of electricity for aquaponics system, the electrical consumption of the system will be greatly reduced and will make the system self-sustainable along with the automatic feeding system [5]. These ideas all contribute to developing an aquaponics system that is fortified to solve for previous aquaponics studies problems and limitations such as the lack of continuous monitoring of the important parameters along with appropriate correction, the accessibility of measured data and corrections made regardless of time and location, the immense electrical consumption of the aquaponics setup, and the lack of self-sustainability of its needs of all sorts.

The study is limited to culturing Black Nile Tilapia (Oreochromis niloticus), which is the most commonly cultured aquatic species [6] and, according to government statistics in 2013 [7], the 3rd top consumed fish in the Philippines at 12.5 grams/day.  Oriental Eggplant (Solanum melongena), Cherry Tomato (Lycopersicon esculentum Mill), and Bok Choy or Chinese White Cabbage (Brassica rapa), three of the most consumed vegetables in the Philippines [8], including Wild Chili (Capsicum frutescens), a popular ingredient in Philippine cuisine, were the cultivated vegetables in the aquaponics study.

II. METHODOLOGY

 A.   Research Design

Three aquaponics setups namely, the ISFET-monitored setup, glass-electrode-sensor-monitored setup, and the uncontrolled setup (typical aquaponics setup used by urban farmers) were constructed. The monitored or controlled setups are composed of five sections namely the water parameter detection, Arduino data analysis, water parameter correction, automated feeding and Internet of Things remote access as seen from Figure 1.

Figure 1: Main Block Diagram

1.   Water Parameter Detection

This section includes the acquisition of the pH and temperature parameter values of water using the sensors installed within the sump tank of the aquaponics setup. The temperature sensor output will be directly fed into the Arduino YUN for data processing. The ISFET pH sensor output (for the first monitored setup) or the glass-electrode pH sensor output (for the second monitored setup) will first be sent to the analog readout circuit (ARC) for voltage amplification. Output from the analog readout circuit will then be sent to the Arduino YUN for data processing.

2.   Arduino Data Analysis

The Arduino YUN will convert the analog voltage output from the temperature sensor as well as output from the ARC into digital form for the microcontroller to interpret acquired data. It has an improved level of granularity with 32-bit quantization which gives good quality of the analog signal. From the output voltage of the temperature sensor and ARC (pH sensor), it digitized this signal precisely, sampling different values closely to the next interval. The digital counts from 0-1023 bits are associated to the input voltage using a 5V reference voltage.

Equation (1) shows the formula on the conversion of a digital count to the actual voltage.

(1)

Figure. 2: Arduino YUN Pin Configuration

3.   Water Parameter Correction

For this section, the resulting data acquired from the previous section would then serve as the input for the decision support system programmed in the microcontroller. The operation of the peristaltic buffer device whether it would correct pH or not, the amount of buffer needed as well as adjusting the temperature with the aerator depends on what decision the system makes based on the interpreted pH and temperature reading results.

4.   Automated Feeding

The automated feeding comprises of Arduino data analysis and the food dispenser. The Arduino is programmed with initialized default time settings of fish feeding wherein it would send a digital signal to the relay driver when the feeder would dispense food.

5.   Internet of Things Access

The section consists of the data transmission and reception of the generated report between the systems, liquid crystal display (LCD) and AquaDroid application on the Android unit via Arduino UNO and YUN. For the LCD, the quantitative results of pH and temperature level for ISFET and glass-electrode pH sensor monitored setups are displayed. This is also shown in the AquaDroid graphical user interface (GUI) along with the moisture sensors’ condition to assure the functionality of the water pumps and sufficient natural aeration to the fish tanks, and fish feeder status. Moreover, parameter abnormalities detected and corrected are sent to the user’s electronic mail for logging.

Figure. 3: AquaDroid Application Retrieved Data UI

B.   Hardware Development

Figure. 4: Relay Driver Circuit

The relay driver circuit shown above is the circuit responsible for controlling the water parameter correction system and automatic feeding system. There are a total of six relay drivers connected in parallel. One relay driver utilizes 24V and controls the two automatic fish feeding devices since the scheduled time for feeding of both monitored systems are just the same. The other two relay drivers utilize 12V and control the pH level corrector which are the two separate automatic buffer devices for the ISFET pH sensor- monitored setup and glass-electrode pH sensor– monitored setup. Lastly, the other two relay drivers also utilize 12V and are responsible for turning on the two aerators, which are the correcting device for the temperature level parameter.

C.   Software Development

 The set of programs needed in this project study are done using Arduino 1.6.7 IDE and MIT App Inventor IDE. The Arduino 1.6.7 IDE is used for the processing of the acquired current signal and for the correction system using the decision support system algorithm. The MIT App inventor IDE is used for developing the AquaDroid Android application which will serve as the GUI of the system that will allow the user to monitor the pH and temperature level, moisture sensor condition and automatic feeder status.

D.   Evaluation Procedure

Buffer solutions of pH 4, 7 and 10 were used to test for accuracy of the ISFET and glass-electrode pH sensors. Ten trials were performed for each of the pH solutions where each sensor is submerged at the same testing time. A 1L water sample from the ISFET monitored fish tank is used where both devices were submerged.

The results of the evaluation procedures were checked and evaluated by eco modular specialists and the cooperative development officer of the city government of Pasay.

III. SMART AQUAPONICS SETUPS

The two monitored subsystems (ISFET and glass-electrode pH sensors monitored subsystems) are primarily concerned with the two important parameters of the water measured in this study namely the pH level and temperature. Along with the detection is the decision support system to check and compare acquired results to standard values suitable for the aquaponics system. Whenever a parameter is out of the standard measurement range, the system provides an automated correction system. The monitored systems are composed of the monitoring and correction system of the water parameter and the automated feeding of fish culture. Data from the monitoring system would serve as the input for the Arduino to the correction system. The microcontroller then converts the signal from analog to digital, and then interfaced with the Android via an IoT gateway.

Monitoring of the said parameters is done continuously with data of the readings shown on the LCD display. Data gathered will be kept in a database. Current status can be checked whenever and wherever through the Android application developed for it displays the data for every parameter. The correction system automatically functions and simultaneously sends an auto-notification to the Android application whenever there are abnormalities in the water parameter.  

The constructed smart aquaponics system is illustrated in Figure 5. It consists of three different setups (measured 6ft x 4 ft) which include the ISFET-monitored, the glass-electrode-sensor-monitored and the uncontrolled set-up. The parts of each set-up were constructed with the same type of materials. The uncontrolled set-up consists of two 15L storage boxes as two-level filter, 75L storage box as media bed, and a 200L blue plastic drum as fish tank. The ISFET-monitored and glass-electrode-sensor-monitored setups were built the same as the uncontrolled setup except that they have a 100L sump tank each where water parameters were controlled.

The whole system was powered by one 40W and two 30W solar panels connected to two solar charge controllers and three 12V solar batteries. The three 14.4W water pumps were directly supplied by the 12V solar battery. The continuous flood system design of the set-ups shown in Figure 6 uses only one water pump for the circulation of water. The water in the fish tank is pumped to the filter and grow bed, flowing through the sump tank and is being drained back to the fish tank through gravity. The amount of water flowing through the pipes was controlled by the attached valve.

The Arduino Yun microcontroller processes all the data acquired from the pH, temperature and moisture sensors. There are two pH meter sensors, ISFET pH sensor and a glass electrode pH sensor, with their analog readout circuits attached to the analog inputs of Arduino Yun. DS18B20 temperature sensors, one on each monitored set-ups, were submerged in each sump tank and were both connected to a digital input 2 of the microcontroller. The DFRobot soil moisture sensors held in the soil of grow beds of monitored set-ups were connected to two analog inputs of the Arduino. The microcontroller processes the acquired data from the sensors and displays the real-time readings. The correction system of the project includes two 6W peristaltic pumps that discharge 26.6 mL Calcium carbonate (CaCO3) solution in two seconds every time the water drops below pH 6. This was controlled by the microcontroller through its two digital outputs. The system is also incorporated with two 9.6 W, 12V DC aerators that correct the temperature every time it exceeds 29°C.  The aerators were connected to the two digital outputs 4 and 5 of the microcontroller.

(a)

(b)

Figure. 5: Smart Aquaponics System. (a) Front View. (b) Top View

Figure. 6: Circulation of Water in a Continuous Flood System of the Aquaponics Setups

Figure. 6: ISFET-monitored setup

Figure. 7: Actual ISFET-monitored setup and its Parts

Figure. 8: Glass-electrode-monitored setup

Figure. 9: Actual Glass-electrode-monitored setup and its Parts

Figure. 8: Uncontrolled setup

Figure. 8: Actual Uncontrolled setup and its Parts

Figure. 8: Location of the Control Circuit Box and the Solar Charge Controller and Battery Box

IV. RESULTS AND DISCUSSION

A.   pH Level Evaluation

Table 1

Experimental Data for pH Level Readings

Trial PH 4 pH 7 pH 10

ISFET Glass elec-trode ISFET Glass elec-trode ISFET Glass elec-trode

1 3.9 3.8 6.9 7.1 10 9.8

2 4 3.9 7 6.9 10 9.9

3 3.9 3.9 7 7 9.8 10. 3

4 4 3.9 6.9 6.8 10 10

5 4.1 3.9 6.9 6.8 9.9 10

6 3.9 3.8 7 6.8 10 9.7

7 3.9 3.7 6.9 6.9 9.9 9.7

8 4 3.7 7.1 7 10 9.9

9 4 4 7 7 10 9.9

10 4 4.2 7 7.1 9.9 9.6

Mean 3.97 3.88 6.97 6.94 9.95 9.88

Table 1 shows the readings of the ISFET and glass-electrode pH sensors in pH 4, 7 and 10 solutions. The two sensors’ readings were compared by undergoing statistical data analysis using the two sample t-test. This test is used since the evaluation procedure is parametric and the variables under study is continuous with less than 30 samples or trials for each pH level. In each test, the null hypothesis Ho is set that there is no significant difference between the standard pH and the sensor tested against. The level of significance in all tests were set to 0.05.

As summarized, the standard buffer solutions of pH 4, pH 7 and pH 10 were compared to the Ion Sensitive Field Effect Transistor (ISFET) and glass electrode (GE) readings for ten trials each. Statistically analyzing the results, the t stat value is less than the t critical two tail value for all ISFET pH levels which signifies that the null hypothesis should not be rejected. This means that there are no significant differences between the ISFET’s readings and the standard pH levels, indicating the ISFET’s accuracy. On the other hand, t stat value is greater than the t critical two tail value for glass-electrode pH levels which signifies that the null hypothesis should be rejected. This means that there are significant differences between the GE’s readings and the standard pH levels, implying the inaccuracy of the glass electrode. Moreover, the mean values of ISFET readings for all pH levels are closer to the standard pH levels than those of the glass-electrodes’.  

Table 2

t-Test Results for: (a) pH 4; (b) pH 7; and (c) pH 10

(a)

Symbol Standard pH ISFET GE

Mean 4 3.97 3.88

Variance 0 0.00455555556 0.02177777778

t Stat 1.40556385699 2.57142857142

t Critical two-tail 2.26215716279 2.26215716279

(b)

Symbol Standard pH ISFET GE

Mean 7 6.97 6.94

Variance 0 0.00455555555 0.013777777777

t Stat 1.40556385699 1.616447718240

t Critical two-tail 2.26215716279 2.262157162798

(c)

Symbol Standard pH ISFET GE

Mean 10 9.95 3.88

Variance 0 0.005 0.03955555555

t Stat 1.90799618401 2.29606797749

t Critical two-tail 2.26215716279 2.26215716279

As summarized, the standard buffer solutions of pH 4, pH 7 and pH 10 were compared to the Ion Sensitive Field Effect Transistor (ISFET) and glass electrode (GE) readings for ten trials each. Statistically analyzing the results, the t stat value is less than the t critical two tail value for all ISFET pH levels which signifies that the null hypothesis should not be rejected. This means that there are no significant differences between the ISFET’s readings and the standard pH levels, indicating the ISFET’s accuracy. On the other hand, t stat value is greater than the t critical two tail value for glass-electrode pH levels which signifies that the null hypothesis should be rejected. This means that there are significant differences between the GE’s readings and the standard pH levels, implying the inaccuracy of the glass electrode. Moreover, the mean values of ISFET readings for all pH levels are closer to the standard pH levels than those of the glass-electrodes’.  

B.   Recorded Growth of Black Nile Tilapia Fish Evaluation

The lengths of all the fish per setup were measured in inches using a foot rule while the weights were measured using a digital weighing scale. There was a total of ten fish per setup and all of which started from approximately 3-inch and 7.5g size. As shown in Table 3 and illustrated in Figures 5 and 6, measurements were done every after two weeks to see the significant growth of the fish in terms of length and weight. As shown for both graphs in Figures 5 and 6, the fish in ISFET pH-monitored setup significantly grow the fastest among the three setups.

C.   Recorded Growth of Plants Evaluation

Further proofs on the superiority of the ISFET pH sensor can be seen on Tables 4, 5, 6, and 7 in the growth of wild chili, tomato, eggplant, and bok choy, respectively, being cultivated.

All of the plants started as seeds broadcasted in a (2ft x 2ft) Styrofoam box. After four weeks, wild chili, tomatoes, eggplants, and bok choy with a height of 3.5-4.5, 5.2-5.6, 6-7, 2-2.5 inches, respectively, were transferred to grow beds. The height of each plant was measured using a meter. As shown in Tables 4, 5, 6, and 7, measurements were done every two weeks for three months and the average height per setup was calculated to see their significant differences. It can be seen that all the plants cultivated in the ISFET pH-monitored setup has the highest average length.

This study is aimed to develop a solar-powered Arduino-based smart aquaponics system through Android IoT application. In association to this, the research focused on proving the superiority of the Ion Sensitive Field Effect Transistor (ISFET)-based pH sensor over the commonly used glass electrode pH sensor by testing its performance for evaluation. Both pH sensor readings on different pH levels were submitted to two sample t-tests. Interpreting the results of the testing and evaluation procedure opted to both pH sensors, the ISFET readings showed no significant differences when compared to standard pH solutions unlike the glass electrode. Moreover, statistically assessing the results, the mean values of the ISFET readings were closer to the standard pH values, indicating the ISFET pH sensor’s accuracy. A reliable proof to this is the more promising length and weight of the plants and fish which were cultivated in the ISFET-monitored setup compared to the other two setups.

The display of water parameter readings on the AquaDroid Android application as well as abnormality detection and correction notification via e-mail were successfully implemented, providing convenient access for the user regardless of time and location. Also, the construction of automatic feeder and integration of solar power technology made the whole system self-sustainable because of reduced power consumption yet increased viability and maintainability.

ACKNOWLEDGMENT

The authors would like to acknowledge Mr. Rolando A. Londonio, City Cooperative Development Officer of the City Government of Pasay, Philippines together with Mr. Rene Magdaraog and Mr. Ronnie Tangpuz, Jr. for providing free seminars on aquaponics setup and construction and the Electronics Engineering Department, College of Engineering, Technological University of the Philippines - Manila for the encouragement and support in the whole process of conducting this study.

References

[1] J. E. Rakocy, M. P. Masser, and T. M. Losordo. \"Recirculating aquaculture tank production systems: aquaponics—integrating fish and plant culture.\" SRAC publication vol. 454, pp. 1-16, Nov. 2006.

[2] K. Connolly and T. Trebic. Optimization of a Backyard Aquaponic Food Production System, McGuill University, Montreal, Canada, 2010.

[3] C. F. Sace and K. M. Fitzsimmons. “Recirculating aquaponic systems using nile tilapia (Oreochromis niloticus) and freshwater prawn (Macrobrachium rosenbergii) polyculture and the productivity of selected leafy vegetables”. Merit Research Journal of Business and Management, vol. 1(1), pp.11-29, 2013.

[4] Xueji Zhang, Huangxian Ju, and Joseph Wang, eds. Electrochemical sensors, biosensors and their biomedical applications. Academic Press, 2011.

[5] D. C. Love, M. S. Uhl, and L. Genello. \"Energy and water use of a small-scale raft aquaponics system in Baltimore, Maryland, United States.\" Aquacultural Engineering vol. 68, pp. 19-27, Sep. 2015.

[6] D. C. Love, J. P. Fry, Ximin Li, E. S. Hill, L. Genello, K. Semmens, and R. E. Thompson. “Commercial aquaponics production and profitability: Findings from an international survey.” Aquaculture vol. 435, pp. 67-74, 2015.

[7] Philippine Fisheries Profile. Bureau of Fisheries and Aquatic Resources. Quezon City, Philippines, p. 65, 2015

[8] M. E. M. Mutuc, S. Pan, and R. M. Rejesus. \"Household vegetable demand in the Philippines: Is there an urban-rural divide?” Agribusiness 23, vol. 4, pp. 511-527, 2007.

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