An internet of things labelled dataset for aquaponics fish pond water quality monitoring system

Udanor, Collins N.; Ossai, N.I.; Nweke, E.O.; Ogbuokiri, B.O.; Eneh, A.H.; Ugwuishiwu, Chikodili; Aneke, S.O.; Ezuwgu, A.O.; Ugwoke, P.O.; Christiana, Arua

doi:10.1016/j.dib.2022.108400

Cited by 29 publications

(15 citation statements)

References 3 publications

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“…These are ammonia and nitrate sensors. These sensors were calibrated according to industry specifications 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.…”

Section: Methods Detailsmentioning

confidence: 99%

“…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 catfish 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.…”

Section: Related Research Articlementioning

confidence: 99%

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Towards an improved internet of things sensors data quality for a smart aquaponics system yield prediction

Eneh,

Udanor,

Ossai

et al. 2023

MethodsX

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Section: Methods Detailsmentioning

confidence: 99%

Section: Related Research Articlementioning

confidence: 99%

Towards an improved internet of things sensors data quality for a smart aquaponics system yield prediction

Eneh,

Udanor,

Ossai

et al. 2023

MethodsX

Self Cite

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“…It only indicated that the temperature levels used in this experiment for temperature in the fish tank of 27°C and 30°C and the temperature in grow bed of 24°C and 27°C were within the acceptable temperature for aquaculture species ranges between 25.5°C and ranges between 30.5°C. (Udanor et al, 2022) Furthermore, the ANOVA results showed significance in the linear factors and in the two-way interaction factor, there is a need to determine which among the levels of these factors made great impact to the significance of the factor. The researcher used the Post Hoc analysis through the Tukey's Test to undertake a pairwise comparison between levels of the factors that were statistically significant.…”

Section: Numerical Resultsmentioning

confidence: 99%

“…The common fish -plant combination used in the aquaponic systems was the Nile tilapia (Love et al, 2014) and lettuce (Villaroel et al, 2016) which was suggested by Rakocy (2012) as a good plant for the aquaponic systems due to its short production period. Furthermore, the automated aquaponic system was commonly used to monitor the design parameters used and the growth of the Nile tilapia and Romaine lettuce using the Internet of Things (Udanor et al, 2022;Ezzahoui et al, 2021;Lee and Wang, 2020;Valiente et al, 2018).…”

Section: Literature Reviewmentioning

confidence: 99%

“…Previous studies, to mention a few, developed automated aquaponic systems using the Internet of Things to monitor the pH level of water, temperature of tank, temperature of grow beds and other relevant parameter and measure the growth of fish and plants (Udanor et al, 2022;Ezzahoui et al, 2021;Lee and Wang, 2020;Valiente et al, 2018). The Internet of Things system used consists of microcontroller such as ESP-32 (Udanor et al, 2022), and a "ThinkSpeak", a cloud platform using WIFI (Lee and Wang, 2020;Valiente et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

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Effects of pH Level, Temperature in Fish Tank, and Temperature in Grow Bed on the Growth of Nile Tilapia and Romaine Lettuce in an Automated Aquaponics System

Proceedings of the International Conference on Industrial Engineering and Operations Management

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This paper presents an experimental study to determine the effects of pH level, temperature of the tank, and temperature of the grow bed on the length of the Nile Tilapia and plant height of the Romaine Lettuce using the automated aquaponics system and to recommend optimal combination of identified factors to maximize their growth. A 2 3 Factorial Design with two replicates for sixteen runs was used to perform the experiment and the results were processed using the Minitab software. Measurement on the length of both the Nile tilapia and Romaine lettuce was done once a week. The experiment lasted for 112 days. The results indicated that the pH level, temperature in the fish tank, and temperature in the grow bed have great impact on both the growth in length of the Nile tilapia and Romaine lettuce. It was also found out that the two-way interaction between the temperature in fish tank and temperature in grow bed significantly impact the growth of Nile tilapia and Romaine lettuce, respectively. Using Tukey's test, it was also found out that the higher-level values of the pH level, temperature in fish tank, and temperature in grow bed showed significance when compared to other levels for a one factor, two-way interaction, and three-way interaction. Moreover, the optimal values of the pH level, temperature in fish tank, and temperature in grow bed for both the Nile tilapia and Romaine lettuce were (7.0 pH, 30°C, 27°C), (7.0 pH, 27°C, 27°C), and (7.0 pH, 30°C, 24°C).

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Applications, technologies, and evaluation methods in smart aquaponics: a systematic literature review

Anila,

Daramola

2024

Artif Intell Rev

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Smart aquaponics systems are gaining popularity as they contribute immensely to sustainable food production. These systems enhance traditional farming with advanced technologies like the Internet of Things (IoT), solar energy, and Artificial Intelligence (AI) for increased proficiency and productivity. However, assessing the performance and effectiveness of these systems is challenging. A systematic literature review (SLR) was conducted to examine the applications, technologies, and evaluation methods used in smart aquaponics. The study sourced peer-reviewed publications from IEEE Xplore, Scopus, SpringerLink and Science Direct. After applying inclusion and exclusion criteria, a total of 105 primary studies were selected for the SLR. The findings show that aquaponics predictions (27%) have been under-explored compared to applications that involved monitoring or monitoring and controlling aquaponics (73%). IoT technologies have been used to create prototype aquaponic systems and collect data, while machine learning/deep learning (predictive analytics) are used for prediction, abnormality detection, and intelligent decision-making. So far, predictive analytics solutions for aquaponics yield prediction, return-on-investment (ROI) estimates, resource optimisation, product marketing, security of aquaponics systems, and sustainability assessment have received very little attention. Also, few studies (37.7%) incorporated any form of evaluation of the proposed solutions, while expert feedback and usability evaluation, which involved stakeholders and end-users of aquaponics solutions, have been rarely used for their assessment. In addition, existing smart aquaponics studies have limitations in terms of their short-term focus (monitoring and controlling of aquaponics not undertaken over a long time to assess performance and sustainability), being conducted mostly in controlled settings (which limits applicability to diverse conditions), and being focused on specific geographical contexts(which limits their generalizability). These limitations provide opportunities for future research. Generally, this study provides new insights and expands discussion on the topic of smart aquaponics.

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An internet of things labelled dataset for aquaponics fish pond water quality monitoring system

Cited by 29 publications

References 3 publications

Towards an improved internet of things sensors data quality for a smart aquaponics system yield prediction

Towards an improved internet of things sensors data quality for a smart aquaponics system yield prediction

Effects of pH Level, Temperature in Fish Tank, and Temperature in Grow Bed on the Growth of Nile Tilapia and Romaine Lettuce in an Automated Aquaponics System

Applications, technologies, and evaluation methods in smart aquaponics: a systematic literature review

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