Aquamind-AI: An Intelligent IoT and Machine Learning Framework For Closed-Loop Smart Aquaponics

Global freshwater depletion, accelerating climate volatility, and arable soil salinization pose existential constraints on conventional agro-aquacultural production. Aquaponic – a circular polyculture biotechnology coupling Recirculating Aquaculture Systems (RAS) with soil-less hydroponics—presents an environmentally sustainable paradigm by recovering toxic metabolic ichthyological excretions as primary vegetative biofertilizer. However, the operational equilibrium of aquaponic systems is governed by tightly coupled, non-linear biological dynamics across finfish, autotrophic nitrifiers (Nitrosomonas and Nitrobacter), and plant root interfaces. Transient deviations in physicochemical parameters (such as water temperature, dissolved oxygen, and hydrostatic levels) induce rapid ecosystem collapse, hypoxic asphyxiation, or systemic nitrite poisoning. To resolve these multidimensional vulnerabilities, this paper delivers a comprehensive, scientifically grounded review of smart aquacultural methodologies and introduces AquaMind-AI, an open-architecture, edge-to-cloud cyber-physical framework engineered for autonomous aquaponic governance. AquaMind-AI integrates a lowcost, fault-tolerant sensory edge layer (incorporating digital single-wire DS18B20 thermal probes, non-contact HC-SR04 ultrasonic echo-ranging hydro-level sensors, and DHT11 ambient microclimate transmitters managed by ATmega328P edge microcontrollers) with galvanically isolated, optocoupled commutation stages driving 6V DC hydraulic recirculation pumps and 220V AC high-output aeration compressors. The computational middleware leverages an asynchronous FastAPI microservice backend coupled with an ensemble Random Forest regressor with harmonic diurnal feature engineering for multi-horizon thermal forecasting (R2 = 0.942 for 1h, R2 = 0.854 for 6h), an unsupervised multivariate Isolation Forest algorithm for zero-day sensor outlier isolation, and a dynamic piecewise Health (Shealth) and Risk (Rsystem) scoring engine. We demonstrate that AquaMind-AI achieves proactive anomaly mitigation, eliminates catastrophic failure modes, and establishes a validated, scalable blueprint for climate-adaptive food security in arid and semi-arid geographies such as the Caspian Basin and Kura-Aras lowlands.