Resource-Efficient Nutrient Dosing for Sustainable Aquaponics: Analysis System for Nutrient Requirements in Hydroponics (ASNRH) Using Aquaculture Byproducts and Neural Networks

Aquaponics is a water-reusing, circular form of controlled-environment agriculture, but 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 By-product 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.

Authors

Institutions

Publication Details

Journal
Sustainability
Published
2025-12-25
DOI
https://doi.org/10.3390/su18010247
Primary Topic
Innovations in Aquaponics and Hydroponics Systems
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Resource-Efficient Nutrient Dosing for Sustainable Aquaponics: Analysis System for Nutrient Requirements in Hydroponics (ASNRH) Using Aquaculture Byproducts and Neural Networks

YiNa Jeong, Surak Son
Sustainability
Innovations in Aquaponics and Hydroponics Systems
article

Resource-Efficient Nutrient Dosing for Sustainable Aquaponics: Analysis System for Nutrient Requirements in Hydroponics (ASNRH) Using Aquaculture Byproducts and Neural Networks

YiNa Jeong, Surak Son
article en

Abstract

Aquaponics is a water-reusing, circular form of controlled-environment agriculture, but 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 By-product 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.

SustainabilityVol. 18(1)
Catholic Kwandong University (KR)
Ministry of Education, India
Zero hunger
Openalex Percentile: Top 24%
Innovations in Aquaponics and Hydroponics Systems
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.