Resource-Efficient Nutrient Dosing for Sustainable Aquaponics: Analysis System for Nutrient Requirements in Hydroponics (ASNRH) Using Aquaculture Byproducts and Neural Networks
Abstract
its sustainability benefits depend on reliable, constraint-aware nutrient dosing under
delayed inflow effects. Aquaponics involves coupling hydroponics with aquaculture but
is difficult to control because the greenhouse/crop state at the current time step (𝑡) must
anticipate water-quality changes that arrive at the next time step (𝑡 + 1), under hard EC–
pH and dose constraints. We propose the Analysis System for Nutrient Requirements in
Hydroponics (ASNRH), a two-module, constraint-aware framework that directly
regresses next-step elemental supplementation (N, P, K; mg·L−1). First, the Fish-farm Byproduct
Prediction Module (FBPM) uses a lightweight GRU forecaster to predict inflow
chemistry at 𝑡 + 1 (e.g., NH4+/NO2−/NO3−, alkalinity) from standard aquaculture sensors.
Second, the Nutrient Requirement Prediction Module (NRPM) encodes the current
hydroponic and crop state at t in parallel with the FBPM inflow at 𝑡 + 1 via a dual-branch
architecture and fuses both representations to produce non-negative dose
recommendations while penalizing forecasted EC/pH violations and excessive actuation
volatility. The data pipeline assumes low-cost greenhouse and aquaculture sensors with
chronological, leakage-free splits. A protocol-first simulation evaluates ASNRH against
time-series and rule-based baselines using accuracy metrics (MAE/RMSE/R2), EC/pH
violation rates, and robustness under missingness/noise; ablations isolate the
contributions of the inflow branch, constraint-aware losses, and lightweight physics
priors. The framework targets deployability in decoupled or coupled aquaponics by
structurally resolving 𝑡 vs. 𝑡 + 1 asynchrony and internalizing domain constraints
during learning; procedures are specified to support reproducibility and subsequent field
trials. By operationalizing anticipatory dosing from reused aquaculture byproducts under
EC/pH feasibility constraints, ASNRH is designed to support sustainability goals such as
reduced nutrient wastage and fewer corrective water exchanges in coupled or decoupled
aquaponics.
Share and Cite
Son, S.; Jeong, Y. Resource-Efficient Nutrient Dosing for Sustainable Aquaponics: Analysis System for Nutrient Requirements in Hydroponics (ASNRH) Using Aquaculture Byproducts and Neural Networks. Sustainability 2026, 18, 247. https://doi.org/10.3390/su18010247
Son S, Jeong Y. Resource-Efficient Nutrient Dosing for Sustainable Aquaponics: Analysis System for Nutrient Requirements in Hydroponics (ASNRH) Using Aquaculture Byproducts and Neural Networks. Sustainability. 2026; 18(1):247. https://doi.org/10.3390/su18010247
Chicago/Turabian StyleSon, Surak, and Yina Jeong. 2026. "Resource-Efficient Nutrient Dosing for Sustainable Aquaponics: Analysis System for Nutrient Requirements in Hydroponics (ASNRH) Using Aquaculture Byproducts and Neural Networks" Sustainability 18, no. 1: 247. https://doi.org/10.3390/su18010247
APA StyleSon, S., & Jeong, Y. (2026). Resource-Efficient Nutrient Dosing for Sustainable Aquaponics: Analysis System for Nutrient Requirements in Hydroponics (ASNRH) Using Aquaculture Byproducts and Neural Networks. Sustainability, 18(1), 247. https://doi.org/10.3390/su18010247

