Design of a Solar Powered IoT (Internet of Things) Remote Water Quality Management System for a Biofloc Aquaculture Technology

Blancaflor, Eric; Baccay, Melito A.

doi:10.1145/3475992.3475996

Cited by 7 publications

(3 citation statements)

References 5 publications

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“…The author obtained 77.3 accuracy of the proposed model regarding the IoT Biofloc solution. Eric B. Blancaflor et al [20] worked on remote water quality management. They divided the solution into three layers.…”

Section: Literature Reviewmentioning

confidence: 99%

IoT-Based Smart Biofloc Monitoring System for Fish Farming Using Machine Learning

Abid,

Amjad,

Munir

et al. 2024

IEEE Access

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Biofloc technology assists in increasing the sustainability of fish farming by reusing and recycling waste water. However, its sophisticated operation makes it very sensitive to environmental conditions. A slight disturbance in one or more parameters can lead to high fish mortality and loss. IoT systems provide an efficient way of closely monitoring the biofloc to avoid catastrophe. The best aqua conditions vary depending on the fish. Therefore, there is a strong need to explore ideal conditions for different fishes. In this work, we have focused on Tilapi fish in the southern Punjab region to find the most suitable parameters. We have developed an IoT solution for monitoring Biofloc and gathering data. We have used low-cost sensors in our product to make it feasible for poor fish farmers. Multiple machine learning algorithms such as decision trees, random forest, support vector machine, logistic regression, Gaussian naive Bayes, XGBoost and ensemble learning are applied to the collected dataset to effectively predict mortality. Our analysis exhibits that the random forest and XGBoost achieved 98% accuracy in estimating mortality. The union of IoT, machine learning, and affordability positions our study at the forefront of advancing sustainable aquaculture practices in southern Punjab, Pakistan.

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Section: Literature Reviewmentioning

confidence: 99%

IoT-Based Smart Biofloc Monitoring System for Fish Farming Using Machine Learning

Abid,

Amjad,

Munir

et al. 2024

IEEE Access

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“…Among the parameters shown in Figure 3, pH stands out clearly. This can be partly explained by the fact that several papers on aquaculture/hydroponics [48,49,57,[59][60][61][62][63][64][65][66][67][68][69][70][71][72][73][74][75] and the pH is closely related to the metabolism of various aquatic species, so its monitoring and control is essential in this activity. Moreover, together with temperature, this is the sensor with more options of brands/models and technological maturity to do measurements in the aqueous medium.…”

Section: What Are the Water Quality Parameters Monitored?mentioning

confidence: 99%

“…In some cases, sensors are evaluated in strictly controlled laboratory settings (e.g., [51,95,96]), where environmental conditions are tightly regulated to provide consistent and reliable test conditions. In other papers, sensors are tested in more complex and dynamic environments, such as aquaculture tanks (e.g., [49,57,59,[63][64][65]85,[97][98][99]), rivers and lakes (e.g., [14,15,47,50,52,78,81,[100][101][102][103][104][105]). These natural environments can present challenges that are not present in controlled laboratory environments, such as temperature variations, water currents, and the presence of contaminants.…”

Section: The Sensors Analyzed What Environments?mentioning

confidence: 99%

Low-Cost Water Quality Sensors for IoT: A Systematic Review

Camargo

Spanhol

Slongo

et al. 2023

Sensors

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In many countries, water quality monitoring is limited due to the high cost of logistics and professional equipment such as multiparametric probes. However, low-cost sensors integrated with the Internet of Things can enable real-time environmental monitoring networks, providing valuable water quality information to the public. To facilitate the widespread adoption of these sensors, it is crucial to identify which sensors can accurately measure key water quality parameters, their manufacturers, and their reliability in different environments. Although there is an increasing body of work utilizing low-cost water quality sensors, many questions remain unanswered. To address this issue, a systematic literature review was conducted to determine which low-cost sensors are being used for remote water quality monitoring. The results show that there are three primary vendors for the sensors used in the selected papers. Most sensors range in price from US$6.9 to US$169.00 but can cost up to US$500.00. While many papers suggest that low-cost sensors are suitable for water quality monitoring, few compare low-cost sensors to reference devices. Therefore, further research is necessary to determine the reliability and accuracy of low-cost sensors compared to professional devices.

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Design, development, and deployment of a sensor-based aquaculture automation system

Sasikumar,

Lourdu Lincy,

Sathyan

et al. 2024

Aquacult Int

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The aquaculture automation system (AcAS) is a user-friendly single-window unit. This allows end users to easily monitor and control the entire system through a built-in, customizable graphical user interface. AcAS was designed for simplicity, making it easy to con gure and use. This system was integrated with highly e cient industrial-grade environmental sensors (pH, conductivity, oxidation-reduction potential and dissolved oxygen) to ensure precise and error-free results in harsh environments. It can also store the user and system data in an attached memory device. It is equipped with built-in Wi-Fi, LoRa/ZigBee, and 4G/5G modules for data transfer, making it compatible with modern communication technology. It was programmed to be farmer-friendly and helped farmers maintain optimal shrimp growth conditions by monitoring various parameters. AcAS takes corrective measures as required, and provides updates to farmers through a graphical display unit. Additionally, farmers can con gure devices to receive alerts regarding important eld parameters or alarm conditions. Therefore, AcAS enhances the e ciency and sustainability of aquaculture farming by enabling precise control of farming conditions and proactive management of aquaculture. HighlightsAcAS is a user-friendly unit that allows easy monitoring and control. It is integrated with industrial-grade environmental sensorsAcAS is equipped with communication modules for data transfer.It helps farmers maintain optimal shrimp growth conditions. Farmers can con gure devices to receive alerts.It enhances the e ciency and sustainability of aquaculture farming.

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Design of a Solar Powered IoT (Internet of Things) Remote Water Quality Management System for a Biofloc Aquaculture Technology

Cited by 7 publications

References 5 publications

IoT-Based Smart Biofloc Monitoring System for Fish Farming Using Machine Learning

IoT-Based Smart Biofloc Monitoring System for Fish Farming Using Machine Learning

Low-Cost Water Quality Sensors for IoT: A Systematic Review

Design, development, and deployment of a sensor-based aquaculture automation system

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