Towards an Improved Internet of Things Sensors Data Quality for a Smart Aquaponics System Yield Prediction

Abstract

The mobile aquaponics system is a sustainable integrated aquaculture-crop production system in which wastewater from fish ponds are utilized in crop production, filtered, and returned for aquaculture uses. This process ensures the optimization of water and nutrients as well as the simultaneous production of fish and crops in portable homestead models. The Lack of datasets and documentations on monitoring growth parameters in Sub-Saharan Africa hamper the effective management and prediction of yields. Water quality impacts the fish growth rate, feed consumption, and general well-being irrespective of the system. This research presents an improvement on the IoT water quality sensor system earlier developed in a previous study in carried out in conjunction with two local catfish farmers. The improved system produced datasets that when trained using several machine learning algorithms achieved a test RMSE score of 0.6140 against 1.0128 from the old system for fish length prediction using Decision Tree Regressor. Further testing with the XGBoost Regressor achieved a test RMSE score of 7.0192 for fish weight prediction from the initial IoT dataset and 0.7793 from the improved IoT dataset. Both systems achieved a prediction accuracy of 99%. These evaluations clearly show that the improved system outperformed the initial one.•The discovery and use of improved IoT pond water quality sensors.
•Development of machine learning models to evaluate the methods.
•Testing of the datasets from the two methods using the machine learning models.

Full text

MethodsX 11 (2023) 102436
Contents lists available at ScienceDirect
MethodsX
journal homepage: www.elsevier.com/locate/methodsx
Towards an improved internet of things sensors data quality for a
smart aquaponics system yield prediction
A.H. Eneh
a
, C.N. Udanor
a , ∗
, N.I. Ossai
b
, S.O. Aneke
a
, P.O. Ugwoke
c
, A.A. Obayi
a
,
C.H. Ugwuishiwu
a
, G.E. Okereke
a
a
Department of Computer Science, University of Nigeria, Nigeria
b
Department of Zoology & Environmental Biology, University of Nigeria, Nigeria
c
Digital Bridge Institute, Nigeria Communications Commission, Abuja, Nigeria
          
Method name:
Improved Smart Aquaponics Dataset Collection
Techniques
Keywords:
Internet of things
Cloud computing
Sensors
Aquaculture
Catsh
Machine learning
       
The mobile aquaponics system is a sustainable integrated aquaculture-crop production system
in which wastewater from sh ponds are utilized in crop production, ltered, and returned for
aquaculture uses. This process ensures the optimization of water and nutrients as well as the si-
multaneous production of sh and crops in portable homestead models. The Lack of datasets and
documentations on monitoring growth parameters in Sub-Saharan Africa hamper the eective
management and prediction of yields. Water quality impacts the sh growth rate, feed consump-
tion, and general well-being irrespective of the system. This research presents an improvement
on the IoT water quality sensor system earlier developed in a previous study in carried out in con-
junction with two local catsh farmers. The improved system produced datasets that when trained
using several machine learning algorithms achieved a test RMSE score of 0.6140 against 1.0128
from the old system for sh length prediction using Decision Tree Regressor. Further testing with
the XGBoost Regressor achieved a test RMSE score of 7.0192 for sh weight prediction from the
initial IoT dataset and 0.7793 from the improved IoT dataset. Both systems achieved a prediction
accuracy of 99%. These evaluations clearly show that the improved system outperformed the
initial one.
• The discovery and use of improved IoT pond water quality sensors.
• Development of machine learning models to evaluate the methods.
• Testing of the datasets from the two methods using the machine learning models.
∗ Corresponding author.
E-mail address: collins.udanor@unn.edu.ng (C.N. Udanor) .
Social media: @cudanor (C.N. Udanor)
https://doi.org/10.1016/j.mex.2023.102436
Received 14 August 2023; Accepted 10 October 2023
Available online 11 October 2023
2215-0161/© 2023 Published by Elsevier B.V. This is an open access article under the CC BY license
( http://creativecommons.org/licenses/by/4.0/ )

A.H. Eneh, C.N. Udanor, N.I. Ossai et al. MethodsX 11 (2023) 102436
Specications table
Subject area: Computer Science
More specic subject area: Application of Computer Science (IoT Sensors and Machine learning) to Aquaculture
Name of your method: Improved Smart Aquaponics Dataset Collection Techniques
Name and reference of original method: A Pilot Implementation of a Remote IoT Sensors for Aquaponics System Datasets Acquisition
C.N. Udanor , N.I. Ossai, B.O. Ogbuokiri, O.E. Nweke, P.O. Ugwoke, U.K. Ome (2021). A Pilot Implementation of
a Remote IoT Sensors for Aquaponics System Datasets Acquisition, The Journal of Computer Science and it’s
Applications (JCSA), Vol. 28, Iss. 2. https://dx.doi.org/10.4314/jcsia.v28i2.1
Resource availability: Dataset: dataset at:
https://www.kaggle.com/datasets/ogbuokiriblessing/sensor-based-aquaponics-sh-pond-datasets
Initial method:
https://www.sciencedirect.com/science/article/pii/S2352340922005972
Method details
Several previous IoT sensor-based systems for monitoring water quality in aquaculture employed a combination of sensors like
temperature, pH value, dissolved oxygen, and water level, among others [4] . Chen, et. al. [5] in Taiwan used the Arduino Mega
2560 microcontroller and transmitted the water quality dataset over a LoRaWAN network. Danh et al. [6] in Vietnam added salinity
and oxidation-reduction sensors and used the Thingspeak cloud storage, providing a mobile user interface for farmers. Nocheski and
Naumoski [7] added a light intensity sensor in addition to the other water quality sensors listed above. Saha et al. [8] used Raspberry Pi
as the microcontroller to implement pond water quality monitoring with a smartphone camera and an Android application. Kim et al.
[9] in addition implemented a closed-loop water ow control using the Message Queue Telemetry Transport (MQTT) communication
protocol. Taher, et al. [10] helps farmers in Bangladesh to monitor the health of their sh using a combination of dissolved oxygen,
pH, and ammonia sensors. In helping farmers monitor their sh farms [11] introduce a QR code tag of an aquatic product to track
and view historical data. Ramya et al. [12] employed IoT to remotely monitor the quantity of food items in the pond water, as well
as implementing an automatic feeding system.
Other digital technologies employed to sh farm data collection and analysis include AI, big data analytics, and blockchain [13] ,
web-based applications with real-time sensor data visualization, alert and remote-control water pump systems [14] . Machine learning
algorithms like logistic regression are also used to predict sh disease by analyzing the IoT water quality data collected in Bangladesh
[15] .
We observed that while a handful of work has been done on IoT-based aquaculture systems mostly in Asia, not much work has
been done in the Sub-Saharan African region, and neither is much work done on aquaponics IoT combination. Yet also there is little
or no public water quality dataset, especially as regards catsh farming.
Working in conjunction with two local catsh farmers in Nsukka, Enugu State, South-East Nigeria we set up three experimental
project sites under the Lacuna Agriculture Fund Award 2020. We built IoT sensor units that were mounted on mobile tarpaulin sh
ponds with spinach plant beds on top of them. Three ponds were set up in each of the farmer’s sites, one set had the IoT units but no
plant bed, while the other had neither IoT sensors nor plant beds, but was used as control. Meanwhile, in the University of Nigeria,
Nsukka campus we set up 12 ponds, 9 of which were tted with the IoT sensor units and plant beds, each comprising six sensors
(temperature, pH, dissolved oxygen, turbidity, ammonia, and nitrate). The design and implementation of the IoT units and how they
were used for data collection are described in the following sub-sections.
Data collection
Data collection was done using an automated method with the use of IoT sensors to collect data on the water physicochemical
properties which was done in real-time and transmitted to a cloud computing storage. Fish, plant, and algae growth, morphometry,
and population dynamics were continuously assayed fortnightly. The detail of this approach is discussed in the next subsection.
Smart aquaponics dataset collection method
The experiment was conducted using six water quality sensors, out of which four were submerged in the pond water (temperature,
turbidity, dissolved oxygen, and pH), while the other two were gas sensors, which were suspended over the water surface. These
are ammonia and nitrate sensors. These sensors were calibrated according to industry specications and programmed to collect
water quality parameters in real-time and automatically upload the same to the cloud through the (IoT) [2] gateway. The process
of calibration involved using some calibration solutions, in some cases (sodium hydroxyl), as in the cases of the pH and dissolved
oxygen sensors. The sensors were connected to a 32-bit microcontroller known as ESP 32. The microcontroller has an in-built wireless
(WiFi) module which enables the sensors’ data to be automatically uploaded to a cloud computing platform, Thingspeak IoT cloud,
through an IoT (edge) network. The C programming language was used to write the software program that controlled the sensors
with the Arduino 1.8.4 integrated development environment (IDE) known as Sketch. The code was uploaded to the microcontroller.
The sensor units were locally designed, constructed, and programmed. The IoT system was programmed to automatically read the
six water quality parameters for each of the 12 aquaponics sh ponds and transmit them to the cloud storage hosted by Thingspeak
( https://thingspeak.com/channels/1414062/ ) every 18 seconds, which is the minimum interval allowed by Thingspeak.
2

A.H. Eneh, C.N. Udanor, N.I. Ossai et al. MethodsX 11 (2023) 102436
Fig. 1. IoT circuitry schematic diagram of the improved system.
Fig. 2. IoT Unit showing sensor circuitry.
After testing the system for 10 months and comparing the values with ground truth values, we discovered that some sensors did
not perform very well, notably the pH, and the two gas sensors. The pH sensor was not meant for such a volume of water as the
sh pond. We discovered this later after more studies on the characteristics of the sensors. Meanwhile, the gas sensors (MQ-135 and
MQ-137) for nitrate and ammonia, respectively, were reading high and unusual values.
We had to redesign the system using the Arduino Mega 2560 with built-in Wi-Fi as the microcontroller. We used a professional
pH meter which can function large volume of water, and a new gas sensor for ammonia and nitrite, described in the next section.
Fig 1 shows the circuit schematic diagram of the improved system while Figs. 2 and 3 show the constructed new IoT circuitry and
the sensors, respectively.
The core of the new system is the 32-bit Arduino 2560 + WiFi R3, a new microcontroller (MCU) dierent from the original 2560
(see Fig. 1 ). This version has the Expressif’s ESP8266 micro-WiFi module embedded in it. It has at its core the ATMega 2560, which
is in the traditional Arduino mega. The MCU supports a voltage range from 5V to 12V, ash memory of up to 32MB, and CPU speed
of up to 80MHz. It also comes with several [ 16 , 18 ] dual inline pins (DIP) switches and a table used for selecting the pins to connect
dierent things depending on the objectives. The 2560 MCU also has 54 digital pins, whereas 15 pins support pulse width modulation
(PWM), and 16 analog input pins and communicate via the 4x serial ports (UART).
3

A.H. Eneh, C.N. Udanor, N.I. Ossai et al. MethodsX 11 (2023) 102436
Fig. 3. Aquaponics IoT submersed sensors.
Whereas the ESP8266 is primarily responsible for the connection to the Internet through a GSM router gateway to upload the
datasets from the sensors to the cloud, the sensors are connected to the various I/O pins of the mega 2560 MCU. For instance, the
DS18B20 submersible temperature sensor is connected to the digital pin 2, and the rest to analog pins as follows: TDS is connected
to pin AD0, pH to AD1, dissolved oxygen to AD2, gas sensors; nitrate and ammonia to AD4 and AD5, respectively, while turbidity
and water level sensors are connected to AD6 and AD7, respectively.
The MCU-MICS-6814, an air quality sensor that measures three gases; carbon monoxide, ammonia, and nitrite (CO, NH
3
, NO
2
)
were installed to detect the ammonia and nitrite coming from the pond water. The MICS 6814 outdoor leak gas detector can detect
the following gases: Carbon monoxide CO from 1 – 1000ppm; Nitrogen dioxide NO2: 0.05 – 10ppm; Ethanol C2H5OH: 10 – 500ppm;
Hydrogen H2: 1 – 1000ppm; Ammonia NH3: 1 – 500ppm; Methane CH4 > 1000ppm; Propane C3H8 > 1000ppm; Iso-butane C4H10
> 1000ppm.
Ammonia concentration in the sh pond can lead to mortality. Ammonia removal begins with converting it to nitrite by a good
bacteria called Nitrosomonas. Nitrite is then converted to nitrate which is the nal process of removing ammonia [17] . Nitrates are
generally removed by plants, hence the need for the aquaponics system. Though nitrite and nitrate are not as harmful as ammonia
but little concentration of them can lead to sh mortality.
Sensors specifications
In this section, we list the makes and models of the sensors used as well as the acceptable ranges for each of the water parameters.
(i) DF Robot Gravity Analog pH Sensor Meter Pro Professional kit for Arduino Water Quality Surveillance Aquaculture :
This sensor is used to read the pH level of the pond water instead of the DF Robot pH sensor probe for Arduino version 2.0 used
in the initial IoT system. It monitors the water’s pH and provides an early warning to avoid the water being acidic. Acceptable
pH ranges for the catsh range from 6.5-9.0. pH also aects ammonia (NH
3
) concentrations. Each unit of change in pH is a
factor of 10X of ammonia. Total ammonia and nitrogen, in addition to water temperature and pH, are needed to determine
un-ionized ammonia (NH3) concentration . Total alkalinity = 50 -150 mg/L, the ability of the water to buer changes in pH.
pH of the aquaculture environment for the growth of sh and shrimp is about 6.5 ∼8.5.
(ii) DF Robot Dissolved Oxygen (DO) sensor probe for Arduino: DO has relatively lower solubility and availability in aquatic life
than in terrestrial environments. Acceptable ranges for dissolved oxygen should be greater than 3mg/L, preferably 5mg/L, or
more. The saturated dissolved oxygen in water with a water temperature of 25°C and a chlorinity of 0.0 is 8.26mg/L.
DF Robot DC 5V TS-300B Turbidity Sensor Module : Mixed Water Detection Module Water Quality Test Turbidity Transducer
for Arduino.
(iii) Dallas DS18B20 temperature sensor. This sensor reads digital values. DSB18B20 has a Unique 1-Wire interface that requires
only one port pin for communication. The ideal water temperature for catsh should not exceed 85°F (30 C).
(iv) Ammonia detection sensor NH
3
gas sensor module MICS 6814: Un-ionized Ammonia (NH
3
) = Chronic or long-term problems
0.06 mg/L. Acute or short-term mortality 0.6 mg/L.
(v) Nitrite detection sensor NO
2
gas sensor module MICS 6814: This sensor replaced the MQ 135 sensor . Ideally, nitrate levels
in a freshwater aquarium should be kept below 20 mg/L. However, any changes should occur slowly, only removing less than
50 mg/L of the Nitrate per day.
(vi) Total Dissolved Solid (TDS): The TDS sensor was added to the new system which measures the amount of solluble solid in
milligram that is dissolved in one liter of water (mg/l) or in parts per million (ppm). The more the solid dissolved in the water
the less clean the water will be. The best values for fresh water sh pond should be less than 400ppm.
4

A.H. Eneh, C.N. Udanor, N.I. Ossai et al. MethodsX 11 (2023) 102436
Fig. 4. Machine learning result of rst IoT the system.
Fig. 5. Machine learning result of the improved IoT system.
Table 1
Fish length evaluation results for the two IoT systems.
Evaluation metrics The result for the Old System Result for New System
MSE 1.02725608411738 0.3770336613092
RMSE 1.01353642466237 0.61403066805271
MAE 0.54105020046009 0.27729398177683
R-Squared 0.99735129480191 0.99871034058614
Machine learning model development
The datasets collected from both the old and new IoT units were taken from one of the 12 ponds, respectively and cleaned,
preprocessed, and trained on the Google Colab platform using Python 3.7. Both datasets were downloaded from Kaggle where they
were both stored separately. IoTPond_old, consists of 279,612 rows and 11 columns, while the dataset from the new sensor unit,
IoTpond_new contains 128,206 rows and 11 columns. The purpose was to understand the correlation between the dierent dataset
features and use machine learning models to predict sh growth in terms of length and weight based on various attributes of the
water quality sensors.
Method validation
Figs. 4 and 5 show the result of the correlation analysis of the water quality features in order of their importance from both the
old and the new system, respectively. The new system shows a better result.
From Figs. 4 and 5 , we notice that in the rst IoT dataset, the most inuential attributes for both systems were turbidity, nitrate,
PH, and temperature. While for the new IoT dataset ammonia, nitrate, total dissolved solids (TDS), and pH are the most important
attributes for determining the sh weight.
To predict sh length and weight, we utilized models like Linear Regression, Ridge Regression, Lasso Regression, K-Neighbors
Regressor, and Decision Tree Regressor. We got their root mean squared error values (RMSE) for both sh weight and sh length
and concluded from the models’ RMSE that we can select Decision Tree Regressor for making our nal predictions. The extreme
gradient boost (XGBoost) Regressor model was used to enhance the performance of the models. It performed a randomized search
with cross-validation to nd the optimal hyperparameters for the models. The hyperparameters tuned were max_depth, learning_rate,
n_estimators, gamma, and min_child_weight. The evaluation metric used for scoring was negative root mean squared error (RMSE).
After tting the randomized search object to the training data, we obtained the best hyperparameters and evaluated the model
on the test data. The test scores for sh length prediction for both versions of IoT sensor units are shown in Table 1 .
5

A.H. Eneh, C.N. Udanor, N.I. Ossai et al. MethodsX 11 (2023) 102436
Table 2
Fish weight evaluation results for the two IoT systems.
Evaluation metrics The result for the Old System Result for New System
MSE 49.26981352230902 0.51018846367
RMSE 7.019245936873064 0.71427478163
MAE 2.23104303871279 0.25627263382
R-Squared 0.999530930656313 0.99825094144
Fig. 6. Fish weight prediction in the old IoT unit.
Fig. 7. Fish weight prediction in the New IoT system.
The rst IoT dataset and the new IoT dataset achieved test RMSE scores of 1.0128 and 0.6140, respectively for sh length
prediction using Decision Tree Regressor. Further testing with the XGBoost Regressor achieved a test RMSE score of 7.0192 for sh
weight prediction for the initial IoT dataset, and 0.7793 for the new IoT dataset. The new system consistently outperformed the old
system in producing lower error values, and both tying on R-squared score, which is a measure of prediction accuracy.
The test scores for sh weight prediction for both versions of IoT sensor units are shown in Table 2 .
Just as discovered in Table 1 , we also see that in Table 2 the improved system also outperformed the old system in all error
values. Using the XGBoost Regressor on weight prediction led to the improved performance of the old IoT system, almost equaling
the performance of the improved system.
Figs. 6 and 7 show the regression plots for the predicted sh growth by weight against the actual weights in both the old and new
IoT systems. Again, we see that the new system shows a better and more consistent smoother plot than the old system.
These evaluations clearly show that the new IoT system performed better than the initial one on all fronts. We also found out
that XGBoost Regressor performed well for both sh weight and sh length models in both datasets and models. However, based
on the models’ RMSE, we recommend using Decision Tree Regressor for making nal predictions for sh length and XGB Regressor
for making nal predictions for sh weight. We also discovered that the poor performance of the initial IoT system was due to
poor-quality sensors and conguration of the sensors.
6

A.H. Eneh, C.N. Udanor, N.I. Ossai et al. MethodsX 11 (2023) 102436
Conclusion and future work
We were motivated by the need to assist farmers to nd solutions to the stunted growth and mortality issues of their shes. We also
discovered that there was a water quality data and knowledge gap which made it dicult for farmers and researchers to eectively
monitor and predict the performance of shes in aquaculture ponds. Having received a grant from the Lacuna Agriculture Fund, we
designed an IoT sensor system to monitor the physicochemical parameters of sh pond water. Working in conjunction with local
farmers we collected the water parameters like temperature, dissolved oxygen, pH, turbidity, ammonia, and nitrate in real-time with
the IoT sensors we designed and built. After several months of data collection, it was observed that some of the sensors did not deliver
optimal values when compared with ground truths. This necessitated further research and the discovery of better-quality sensors for
high quality datasets. A new system was designed and built with the new Arduino Mega 2560 + Wi-Fi microcontroller. Datasets from
the old and new systems were both trained using dierent machine learning algorithms. The new IoT system performed much better
than the initial one in all fronts showing that sensor quality, design experience, and research are major considerations in designing
IoT systems.
The design and implementation of an improved IoT-Cloud orchestrated sensor system for aquaponics will provide a robust and
scalable communication infrastructure, facilitating real-time monitoring, and data analytics in the aquaculture industry. This will also
enhance the eciency, productivity, and sustainability of aquaponics systems, contributing to the advancement of agriculture and
food production. The cloud-based analytics platform will provide advanced insights, anomaly detection, and predictive maintenance,
facilitating informed decision-making for system optimization.
Future work will focus on turning the best predictive machine learning model to a mobile application. Farmers will receive the
app installed in their smart phones for monitoring water quality and predicting sh yield. Results gotten from the use of the app will
be compared with baseline data for sh growth.
Ethics statements
Our work was carried out in line with the ARRIVE guidelines and in accordance with the U.K. Animals (Scientific Procedures)
Act, 1986 and associated guidelines; EU Directive 2010/63/EU for animal experiments in terms of experimental design, sample size
determination, and measurements. The shes were randomly selected based on the sample size already determined per sh pond
based on the stocking density. Measurements of weight and length were done and the shes returned to the ponds.
For a published article
C.N. Udanor, N.I. Ossai, B.O. Ogbuokiri, O.E. Nweke, P.O. Ugwoke, U.K. Ome (2021). A Pilot Implementation of a Remote IoT
Sensors for Aquaponics System Datasets Acquisition, The Journal of Computer Science and it’s Applications (JCSA), Vol. 28, Iss. 2.
https://dx.doi.org/10.4314/jcsia.v28i2.1 .
Related research article
During the Lacuna Agriculture Fund 2020 project grant award [1] aimed at building agricultural datasets, our team designed
and developed an IoT-sensors microcontroller-based system [ 2,19 ] aimed at remotely monitoring and collecting physicochemical
parameters from catsh aquaponics pond water. The primary deliverable from the project was a labeled dataset containing water
quality parameters like water temperature, pH, dissolved oxygen, turbidity, dissolved ammonia, and nitrate from 12 aquaponics ponds.
At the end of the project, the dataset was contributed to the Kaggle machine learning dataset at [3] under the Creative Commons
Attribution 4.0 to the research community. This dataset comes in handy in developing machine learning models for predicting catsh
health and yield.
In furtherance of the aforementioned research, the authors have been motivated to further improve the quality of the dataset to
completely address the following problems: (i) the absence of accurate water quality datasets in any known machine learning data
repository for the African Catsh ( Clarias gariepinus ), (ii) the high cost of generating a labeled African catsh dataset for machine
learning without an expert, (iii) the challenge of time consumption and inaccurate generation of labeled data using manual methods,
(iv) the need to recycle and reuse water given its scarcity in many African communities, (v) the need for an innovative ways of
increasing productivity in a limited space oered by aquaponics, and (vi) the challenge of making aquaculture production widespread
through the mobile and portable aquaponics model.
Given the above, the authors considered developing a scalable and robust GSM-Edge-Cloud aquaponics system with an ecient
data collection mechanism from IoT sensors. The architecture of the proposed system includes an intelligent cloud-based analytics
platform for real-time monitoring.
Declaration of Competing Interest
The authors declare that they have no known competing nancial interests or personal relationships that could have appeared to
inuence the work reported in this paper.
7

A.H. Eneh, C.N. Udanor, N.I. Ossai et al. MethodsX 11 (2023) 102436
CRediT authorship contribution statement
A.H. Eneh: Supervision. C.N. Udanor: Conceptualization, Methodology, Software, Visualization, Investigation. N.I. Ossai: Inves-
tigation. S.O. Aneke: Writing – original draft. P.O. Ugwoke: Writing – original draft, Validation, Data curation. A.A. Obayi: Writing
– original draft. C.H. Ugwuishiwu: Writing –review & editing. G.E. Okereke: Software, Validation.
Data availability
Data will be made available on request.
Acknowledgments
This work was supported by the Meridian Institute under the Lacuna Fund for Agricultural Dataset 2020 [Grant No.: 0326-S-001,
2020], at 105 Village Place, Dillon, Colorado 80435, United States.
Supplementary material and/or additional information
Link to dataset:
Udanor collins, Blessing Ogbuokiri, Nweke Onyinye, Sensor Based Aquaponics Fish Pond Datasets, Kaggle, available at:
https://www.kaggle.com/dsv/3748790 , Kaggle, 2022.
References
[1] Sensor based aquaponics sh pond datasets: IoT sh pond monitoring datasets, lacuna fund, Available at: https://lacunafund.org/datasets/agriculture/ (Accessed:
8th August, 2023).
[2] C.N. Udanor, N.I. Ossai, E.O. Nweke, B.O. Ogbuokiri, A.H. Eneh, C.H. Ugwuishiwu, A. Christiana, An internet of things labelled dataset for aquaponics sh pond
water quality monitoring system, Data in Brief 43 (2022) 108400
.
[3] U. collins, B. Ogbuokiri, N. Onyinye, Sensor Based Aquaponics Fish Pond Datasets, Kaggle, 2022, doi: 10.34740/KAGGLE/DSV/3748790 .
[4] S.U Kiruthika, Dr.S. Kanaga Suba Raja, R. Jaichandran, J. Adv. Res. Dyn. Control Syst. 9 (1) (2017) .
[5] C.H. Chen, Y.C. Wu, J.X. Zhang, Y.H. Chen, IoT-based sh farm water quality monitoring system, Sensors 22 (17) (2022) 6700 .
[6] L.V.Q. Danh, D.V.M. Dung, T.H. Danh, N.C. Ngon, Design and deployment of an IoT-based water quality monitoring system for aquaculture in Mekong Delta,
Int. J. Mech. Eng. Robot. Res. 9 (8) (2022) 1170–1175
.
[7] Nocheski S. and Naumoski A. Scientic technical union of mechanical engineering “Industry 4.0 ” Issue, 2018.
[8] S. Saha, R.H. Rajib, S. Kabir, IoT based automated sh farm aquaculture monitoring system, International Conference on Innovations in Science, Engineering
and Technology (ICISET), IEEE, 2018 Date of Conference: 27-28, doi:
10.1109/ICISET.2018.8745543 .
[9] Y. Kim, N. Lee, B. Kim, K. Shin, Realization of IoT based sh farm control using mobile app, International Symposium on Computer, Consumer and Control
(IS3C). 06-08 December, 2018, doi:
10.1109/IS3C.2018.00055 .
[10] A.T. Tamim, H. Begum, S.A. Shachcho, M.M. Khan, B. Yeboah-Akowuah, M. Masud, J.F. Al-Amri, Development of IoT based sh monitoring system for aqua-
culture, Intell. Autom. Soft Comput. 32 (1) (2022) -71 p5517p
.
[11] G. Gao, K. Xiao, M. Chen, An intelligent IoT-based control and traceability system to forecast and maintain water quality in freshwater sh farms, Comput.
Electron. Agricult. (2019) Elsevier
.
[12] A. Ramya, R. Rohini, S. Ravi, IoT based smart monitoring system for sh farming, Int. J. Eng. Adv. Technol. 8 (6) (2019) VolumeIssue numberSpecial Issue .
[13] X. Yang, D. Cao, J. Chen, Z. Xiao, A. Daowd, AI and IoT-based collaborative business ecosystem: a case in Chinese sh farming industry, Int. J. Technol. Manag.
82 (2) (2020) 151–171 June 18, doi:
10.1504/IJTM.2020.107856 .
[14] B.E Agossou, T. Toshiro, IoT & AI based system for sh farming: case study of Benin, in: GoodIT ’21: Proceedings of the Conference on Information Technology
for Social Good September, ACM, 2021, pp. 259–264, doi:
10.1145/3462203.3475873 .
[15] M. Ahmed, Md.O Rahaman, M. Rahman, M.A. Kashem, Analyzing the quality of water and predicting the suitability for sh farming based on
IoT in the context of Bangladesh, 2019 IEEE International Conference on Sustainable Technologies for Industry 4.0 (STI) , 24-25 December, 2019,
doi:
10.1109/STI47673.2019.9068050 .
[16] Louis, L.&. Kumar, A. Implementation of closed loop based scan mechanism, 2021, 309-313. doi: 10.1109/CCIntelS.2015.7437930 .
[17] Eco-ltration. How to cure and prevent dangerously high nitrate and nitrite levels in a sh pond. Available at: https://www.eco-ltration.
co.uk/prevention-and-treatment-for-high-nitrate-nitrite-levels/#: ∼:text = Nitrates%20are%20a%20natural%20by-product%20of%20the%20bacterial,to%20
sh%20mortality%20if%20not%20kept%20in%20check
, (Nov 30, 2021).
[18] Koyanagi F., AutoDesk Instructables, Arduino MEGA 2560 With WiFi Built-in - ESP8266. Available at: https://www.instructables.com/Arduino-MEGA-2560-
With-WiFi-Built-in-ESP8266/
(Retrieved: 25th June, 2023).
[19] C.N. Udanor, N.I. Ossai, B.O. Ogbuokiri, O.E. Nweke, P.O. Ugwoke, U.K. Ome, A pilot implementation of a remote IoT sensors for aquaponics system datasets
acquisition, J. Comput. Sci. Appl. (JCSA) 28 (2) (2021) Iss, doi:
10.4314/jcsia.v28i2.1 .
8