Assessment of an automated IoT-biofloc water quality management system in the <i>Litopenaeus vannamei</i>’s mortality and growth rate

Blancaflor, Eric; Baccay, Melito A.

doi:10.1080/00051144.2022.2031540

Cited by 6 publications

(1 citation statement)

References 9 publications

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“…Eric B. Blancaflor et al [21] assessed an automated IoT-based Biofloc water quality system for the growth and mortality of Litopenaeus vannamei. The author concluded that water quality had a 10% higher survival rate.…”

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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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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Internet of Things in aquaculture: A review of the challenges and potential solutions based on current and future trends

Rastegari¹,

Nadi²,

Lam³

et al. 2023

Smart Agricultural Technology

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Assessment of an automated IoT-biofloc water quality management system in the Litopenaeus vannamei’s mortality and growth rate

Cited by 6 publications

References 9 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

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

Internet of Things in aquaculture: A review of the challenges and potential solutions based on current and future trends

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