Economic &amp; Operational Impact Analysis of a Solar Powered Remote Water Quality Management System Designed for an Indoor Biofloc Aquaculture Setup

Blancaflor, Eric; Baccay, Melito A.

doi:10.1145/3485768.3485794

Cited by 1 publication

(1 citation statement)

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“…They used a pH sensor, Arduino, Database, LCD, and Thermo hydrometer to monitor the Biofloc acidity. Eric B. Blancaflor et al [25] focused on cost and profit comparison. The researcher explored the economic impact of solar power water quality management systems for Biofloc water tanks.…”

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%