Proposed Design of an IoT-Based Automated Water Quality Monitoring, Mitigation, and Dashboard System for Discus Fish (Symphysodon spp.) Using a Rule-Based Expert System

The discus fish (Symphysodon sp.) is among the world's most commercially valuable freshwater amamental species, requiring a narrow envelope of a temperature of 27-32°C, a pH of 6.0-7.0, and a free-ammonia concentration of approximately 0 ppm. These fish are sensitive to their environment, brief parameter excursions trigger osmotic imbalance, gill-epithellal necrosis, and mass mortality within hours. Conventional reactive lot systems introduce a 5-20 min human-loop latency, and machine-leaming controllers often fail at deployment because problems, leaving small-scale breeders without a rellable means of autonomous correction. This paper proposes an autonomous lat-based water quality monitoring, mitigation, and dashboard system developed under the Design Science Research (DSR). A four Dardmeter serising array temperature, pH, total dissolved solids, and ammonia feed a Rule-Based Expert Systein (RBES) running on ESP32 dual-core edge node. Now extended to include bidirectional pe dosing pumps and a Peltier Designscience Research (DSR). A four-d dependency, A responsive web-and-mobile Progressive Web App dashboard provides real-time gauges, a composite Water Quality Health Score, trend analytics. Performance targets are a response latency of $2,000 ms, a temperature precision of 0.1°C, a PH with no hou a Mitigation Reliability Rote (MRR) of 95%, and water-quality stabilization within 30 min.