IoT-VR Integrated Framework for Precision Prebiotic Dosing in Intensive Aquaculture: A Technology-Based Approach to Sustainable Fish Production
Abstract
Purpose of the study: This study aimed to develop and validate an integrated precision aquaculture framework that combines IoT-controlled multi-prebiotic dosing, centralized environmental monitoring, and virtual reality-based operator training to improve water quality, fish growth performance, and operational competency in intensive multi-pond fish farming systems.
Methodology: A closed-loop precision aquaculture system was implemented for 30 days in 40 homogeneous circular ponds. The system used centralized sensors (dissolved oxygen, pH, temperature), IoT-actuated solenoid valves with inline flow sensors for four prebiotic formulations, Water Quality Index computation, VR-based operator training, and statistical analysis using one-way ANOVA, multiple linear regression, and paired t-tests.
Main Findings: IoT-based management significantly improved Water Quality Index, survival rate, and specific growth rate compared with manual management. Automated prebiotic dosing was volumetrically accurate and consistently on time. Higher water quality strongly correlated with better growth and survival. VR training substantially reduced operator task completion time and operational errors, enhancing overall system efficiency and reliability.
Novelty/Originality of this study: This study presents a fully integrated multidisciplinary precision aquaculture framework that uniquely combines IoT-driven multi-prebiotic automated dosing, centralized environmental monitoring for homogeneous pond networks, and VR-based immersive training as an active human–system interaction layer. It advances current knowledge by demonstrating a scalable, technology-mediated model that unites automation, water quality management, and skill development in intensive aquaculture.
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