Abstract :

As The Global Demand For Protein Intensifies, The Aquaculture Industry Is Increasingly Adopting Automated Smart Farming Paradigms To Replace Traditional, Labour-intensive Methodologies. This Research Investigates The Mitigation Of High Aquatic Mortality Rates And Operational Inefficiencies By Implementing A Localized Monitoring Network For Critical Water Parameters, Including PH, Temperature, And Dissolved Oxygen (DO). We Present The Development Of OmniAqua, An ESP32-based Autonomous Unmanned Surface Vehicle (A-USV) Capable Of Independent Navigation Within Pond Ecosystems Via A Wi-Fi-enabled Control Interface. The Platform Features An Integrated Sensor Suite For High-frequency Environmental Sampling Alongside An IP-based Camera Module For Continuous Remote Surveillance. By Combining A Dual-motor Propulsion System With A Precision Mechanical Unit For Scheduled Feeding, The Vehicle Facilitates Comprehensive Pond Management Through A Mobile-integrated Dashboard. The Implementation Of Cloud-based Data Logging And Pre-emptive Alert Protocols Significantly Diminishes The Probability Of Human Error And Water Quality Degradation. Empirical Evaluations Indicate That This IoT-integrated Framework Enhances Resource Utilization And Reduces Overhead, Offering A Cost-efficient And Scalable Model For Advancing Sustainable, High-tech Aquatic Production. Keywords: OmniAqua, Smart Aquaculture, ESP32, Autonomous Surface Vehicle, IoT Monitoring, Automated Feeding.

Published:

01-4-2026

Issue:

Vol. 26 No. 4 (2026)

Page Nos:

37-45

Section:

Articles

License:

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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